1
|
Jaywant A, Gunning FM, Oberlin LE, Santillana M, Ognyanova K, Druckman JN, Baum MA, Lazer D, Perlis RH. Cognitive Symptoms of Post-COVID-19 Condition and Daily Functioning. JAMA Netw Open 2024; 7:e2356098. [PMID: 38353947 PMCID: PMC10867690 DOI: 10.1001/jamanetworkopen.2023.56098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/20/2023] [Indexed: 02/16/2024] Open
Abstract
Importance The frequent occurrence of cognitive symptoms in post-COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations. Objective To investigate the prevalence of self-reported cognitive symptoms in post-COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post-COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status. Design, Setting, and Participants Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older. Exposure Post-COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness. Main Outcomes and Measures Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9. Results The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post-COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post-COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]). Conclusions and Relevance The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post-COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post-COVID-19 condition.
Collapse
Affiliation(s)
- Abhishek Jaywant
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M. Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Lauren E. Oberlin
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Mauricio Santillana
- Machine Intelligence Group for the Betterment of Health and the Environment, Northeastern University, Boston, Massachusetts
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Katherine Ognyanova
- Department of Communication, School of Communication and Information, Rutgers University, New Brunswick, New Jersey
| | - James N. Druckman
- Department of Political Science, University of Rochester, Rochester, New York
| | - Matthew A. Baum
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts
- Department of Political Science, Northeastern University, Boston, Massachusetts
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
- Institute for Quantitative Social Science, Harvard University, Cambridge, Massachusetts
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
2
|
Klein B, LaRock T, McCabe S, Torres L, Friedland L, Kos M, Privitera F, Lake B, Kraemer MUG, Brownstein JS, Gonzalez R, Lazer D, Eliassi-Rad T, Scarpino SV, Vespignani A, Chinazzi M. Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic. PLOS Digit Health 2024; 3:e0000430. [PMID: 38319890 PMCID: PMC10846712 DOI: 10.1371/journal.pdig.0000430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024]
Abstract
The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.
Collapse
Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Timothy LaRock
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Stefan McCabe
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Leo Torres
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Lisa Friedland
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Maciej Kos
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Brennan Lake
- Cuebiq Inc., New York, New York, United States of America
| | | | - John S. Brownstein
- Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Richard Gonzalez
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - Matteo Chinazzi
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- The Roux Institute, Northeastern University, Portland, Maine, United States of America
| |
Collapse
|
3
|
Abstract
It is critical to understand how algorithms structure the information people see and how those algorithms support or undermine society's core values. We offer a normative framework for the assessment of the information curation algorithms that determine much of what people see on the internet. The framework presents two levels of assessment: one for individual-level effects and another for systemic effects. With regard to individual-level effects we discuss whether (a) the information is aligned with the user's interests, (b) the information is accurate, and (c) the information is so appealing that it is difficult for a person's self-regulatory resources to ignore ("agency hacking"). At the systemic level we discuss whether (a) there are adverse civic-level effects on a system-level variable, such as political polarization; (b) there are negative distributional or discriminatory effects; and (c) there are anticompetitive effects, with the information providing an advantage to the platform. The objective of this framework is both to inform the direction of future scholarship as well as to offer tools for intervention for policymakers.
Collapse
Affiliation(s)
- David Lazer
- Khoury College of Computer Sciences, Northeastern University
- Department of Political Science, Northeastern University
- Network Science Institute, Northeastern University
| | - Briony Swire-Thompson
- Department of Political Science, Northeastern University
- Network Science Institute, Northeastern University
- Department of Psychology, Northeastern University
| | - Christo Wilson
- Khoury College of Computer Sciences, Northeastern University
- Network Science Institute, Northeastern University
| |
Collapse
|
4
|
Nyhan B, Settle J, Thorson E, Wojcieszak M, Barberá P, Chen AY, Allcott H, Brown T, Crespo-Tenorio A, Dimmery D, Freelon D, Gentzkow M, González-Bailón S, Guess AM, Kennedy E, Kim YM, Lazer D, Malhotra N, Moehler D, Pan J, Thomas DR, Tromble R, Rivera CV, Wilkins A, Xiong B, de Jonge CK, Franco A, Mason W, Stroud NJ, Tucker JA. Author Correction: Like-minded sources on Facebook are prevalent but not polarizing. Nature 2023; 623:E9. [PMID: 37914941 PMCID: PMC10651477 DOI: 10.1038/s41586-023-06795-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Affiliation(s)
- Brendan Nyhan
- Department of Government, Dartmouth College, Hanover, NH, USA.
| | - Jaime Settle
- Department of Government and Data Science, William and Mary, Williamsburg, VA, USA
| | - Emily Thorson
- Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA
| | - Magdalena Wojcieszak
- Department of Communication, University of California, Davis, CA, USA
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Annie Y Chen
- CUNY Institute for State and Local Governance, New York, NY, USA
| | - Hunt Allcott
- Environmental and Energy Policy Analysis Center, Stanford University, Stanford, CA, USA
| | | | | | - Drew Dimmery
- Meta, Menlo Park, CA, USA
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Deen Freelon
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Andrew M Guess
- Department of Politics, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Edward Kennedy
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Young Mie Kim
- School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Neil Malhotra
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | | | - Jennifer Pan
- Department of Communication, Stanford University, Stanford, CA, USA
| | | | - Rebekah Tromble
- School of Media and Public Affairs, The George Washington University, Washington, DC, USA
- Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA
| | | | | | | | | | | | | | - Natalie Jomini Stroud
- Moody College of Communication, University of Texas at Austin, Austin, TX, USA
- Center for Media Engagement, University of Texas at Austin, Austin, TX, USA
| | - Joshua A Tucker
- Wilf Family Department of Politics, New York University, New York, NY, USA
- Center for Social Media and Politics, New York University, New York, NY, USA
| |
Collapse
|
5
|
González-Bailón S, Lazer D. Are algorithmic bias claims supported?-Response. Science 2023; 381:1420. [PMID: 37769090 DOI: 10.1126/science.adk4899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Affiliation(s)
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| |
Collapse
|
6
|
Perlis RH, Lunz Trujillo K, Safarpour A, Quintana A, Simonson MD, Perlis J, Santillana M, Ognyanova K, Baum MA, Druckman JN, Lazer D. Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States. JAMA Netw Open 2023; 6:e2334945. [PMID: 37755830 PMCID: PMC10534266 DOI: 10.1001/jamanetworkopen.2023.34945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 08/09/2023] [Indexed: 09/28/2023] Open
Abstract
Importance Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters. Objective To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors. Design, Setting, and Participants This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC. Main Outcome and Measure Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index. Results The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity. Conclusions and Relevance In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Kristin Lunz Trujillo
- Northeastern University, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | - Alauna Safarpour
- Northeastern University, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | - Alexi Quintana
- Northeastern University, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | | | | | | | | | | | | | - David Lazer
- Northeastern University, Boston, Massachusetts
| |
Collapse
|
7
|
Perlis RH, Lunz Trujillo K, Green J, Safarpour A, Druckman JN, Santillana M, Ognyanova K, Lazer D. Misinformation, Trust, and Use of Ivermectin and Hydroxychloroquine for COVID-19. JAMA Health Forum 2023; 4:e233257. [PMID: 37773507 PMCID: PMC10542734 DOI: 10.1001/jamahealthforum.2023.3257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/24/2023] [Indexed: 10/01/2023] Open
Abstract
Importance The COVID-19 pandemic has been notable for the widespread dissemination of misinformation regarding the virus and appropriate treatment. Objective To quantify the prevalence of non-evidence-based treatment for COVID-19 in the US and the association between such treatment and endorsement of misinformation as well as lack of trust in physicians and scientists. Design, Setting, and Participants This single-wave, population-based, nonprobability internet survey study was conducted between December 22, 2022, and January 16, 2023, in US residents 18 years or older who reported prior COVID-19 infection. Main Outcome and Measure Self-reported use of ivermectin or hydroxychloroquine, endorsing false statements related to COVID-19 vaccination, self-reported trust in various institutions, conspiratorial thinking measured by the American Conspiracy Thinking Scale, and news sources. Results A total of 13 438 individuals (mean [SD] age, 42.7 [16.1] years; 9150 [68.1%] female and 4288 [31.9%] male) who reported prior COVID-19 infection were included in this study. In this cohort, 799 (5.9%) reported prior use of hydroxychloroquine (527 [3.9%]) or ivermectin (440 [3.3%]). In regression models including sociodemographic features as well as political affiliation, those who endorsed at least 1 item of COVID-19 vaccine misinformation were more likely to receive non-evidence-based medication (adjusted odds ratio [OR], 2.86; 95% CI, 2.28-3.58). Those reporting trust in physicians and hospitals (adjusted OR, 0.74; 95% CI, 0.56-0.98) and in scientists (adjusted OR, 0.63; 95% CI, 0.51-0.79) were less likely to receive non-evidence-based medication. Respondents reporting trust in social media (adjusted OR, 2.39; 95% CI, 2.00-2.87) and in Donald Trump (adjusted OR, 2.97; 95% CI, 2.34-3.78) were more likely to have taken non-evidence-based medication. Individuals with greater scores on the American Conspiracy Thinking Scale were more likely to have received non-evidence-based medications (unadjusted OR, 1.09; 95% CI, 1.06-1.11; adjusted OR, 1.10; 95% CI, 1.07-1.13). Conclusions and Relevance In this survey study of US adults, endorsement of misinformation about the COVID-19 pandemic, lack of trust in physicians or scientists, conspiracy-mindedness, and the nature of news sources were associated with receiving non-evidence-based treatment for COVID-19. These results suggest that the potential harms of misinformation may extend to the use of ineffective and potentially toxic treatments in addition to avoidance of health-promoting behaviors.
Collapse
Affiliation(s)
- Roy H. Perlis
- Department of Psychiatry, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Associate Editor, JAMA Network Open
| | - Kristin Lunz Trujillo
- Department of Political Science, Northeastern University, Boston, Massachusetts
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | - Jon Green
- Department of Political Science, Northeastern University, Boston, Massachusetts
| | - Alauna Safarpour
- Department of Political Science, Northeastern University, Boston, Massachusetts
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | - James N. Druckman
- Department of Political Science, Northwestern University, Evanston, Illinois
| | - Mauricio Santillana
- Department of Political Science, Northeastern University, Boston, Massachusetts
| | - Katherine Ognyanova
- Department of Communication, School of Communication and Information, Rutgers University, New Brunswick, New Jersey
| | - David Lazer
- Department of Political Science, Northeastern University, Boston, Massachusetts
| |
Collapse
|
8
|
Solomonov N, Green J, Quintana A, Lin J, Ognyanova K, Santillana M, Druckman JN, Baum MA, Lazer D, Gunning FM, Perlis RH. A 50-state survey study of thoughts of suicide and social isolation among older adults in the United States. J Affect Disord 2023; 334:43-49. [PMID: 37086804 PMCID: PMC10751855 DOI: 10.1016/j.jad.2023.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/26/2023] [Accepted: 04/14/2023] [Indexed: 04/24/2023]
Abstract
BACKGROUND We aimed to characterize the prevalence of social disconnection and thoughts of suicide among older adults in the United States, and examine the association between them in a large naturalistic study. METHODS We analyzed data from 6 waves of a fifty-state non-probability survey among US adults conducted between February and December 2021. The internet-based survey collected the PHQ-9, as well as multiple measures of social connectedness. We applied multiple logistic regression to analyze the association between presence of thoughts of suicide and social disconnection. Exploratory analysis, using generalized random forests, examined heterogeneity of effects across sociodemographic groups. RESULTS Of 16,164 survey respondents age 65 and older, mean age was 70.9 (SD 5.0); the cohort was 61.4 % female and 29.6 % male; 2.0 % Asian, 6.7 % Black, 2.2 % Hispanic, and 86.8 % White. A total of 1144 (7.1 %) reported thoughts of suicide at least several days in the prior 2 week period. In models adjusted for sociodemographic features, households with 3 or more additional members (adjusted OR 1.73, 95 % CI 1.28-2.33) and lack of social supports, particularly emotional supports (adjusted OR 2.60, 95 % CI 2.09-3.23), were independently associated with greater likelihood of reporting such thoughts, as was greater reported loneliness (adjusted OR 1.75, 95 % CI 1.64-1.87). The effects of emotional support varied significantly across sociodemographic groups. CONCLUSIONS Thoughts of suicide are common among older adults in the US, and associated with lack of social support, but not with living alone. TRIAL REGISTRATION NA.
Collapse
Affiliation(s)
- Nili Solomonov
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, NY, United States of America
| | - Jon Green
- Northeastern University, Boston, MA, United States of America
| | - Alexi Quintana
- Northeastern University, Boston, MA, United States of America
| | - Jennifer Lin
- Northwestern University, Evanston, IL, United States of America
| | | | - Mauricio Santillana
- Harvard Medical School, Boston, MA, United States of America; Boston Children's Hospital, Boston, MA, United States of America
| | | | - Matthew A Baum
- Massachusetts General Hospital, Boston, MA, United States of America
| | - David Lazer
- Northwestern University, Evanston, IL, United States of America
| | - Faith M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, NY, United States of America
| | - Roy H Perlis
- Harvard University, Cambridge, MA, United States of America; Harvard Medical School, Boston, MA, United States of America; Massachusetts General Hospital, Boston, MA, United States of America.
| |
Collapse
|
9
|
Nyhan B, Settle J, Thorson E, Wojcieszak M, Barberá P, Chen AY, Allcott H, Brown T, Crespo-Tenorio A, Dimmery D, Freelon D, Gentzkow M, González-Bailón S, Guess AM, Kennedy E, Kim YM, Lazer D, Malhotra N, Moehler D, Pan J, Thomas DR, Tromble R, Rivera CV, Wilkins A, Xiong B, de Jonge CK, Franco A, Mason W, Stroud NJ, Tucker JA. Like-minded sources on Facebook are prevalent but not polarizing. Nature 2023; 620:137-144. [PMID: 37500978 PMCID: PMC10396953 DOI: 10.1038/s41586-023-06297-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/07/2023] [Indexed: 07/29/2023]
Abstract
Many critics raise concerns about the prevalence of 'echo chambers' on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from 'like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.
Collapse
Affiliation(s)
- Brendan Nyhan
- Department of Government, Dartmouth College, Hanover, NH, USA.
| | - Jaime Settle
- Department of Government and Data Science, William and Mary, Williamsburg, VA, USA
| | - Emily Thorson
- Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA
| | - Magdalena Wojcieszak
- Department of Communication, University of California, Davis, CA, USA
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Annie Y Chen
- CUNY Institute for State and Local Governance, New York, NY, USA
| | - Hunt Allcott
- Environmental and Energy Policy Analysis Center, Stanford University, Stanford, CA, USA
| | | | | | - Drew Dimmery
- Meta, Menlo Park, CA, USA
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Deen Freelon
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Andrew M Guess
- Department of Politics, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Edward Kennedy
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Young Mie Kim
- School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Neil Malhotra
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | | | - Jennifer Pan
- Department of Communication, Stanford University, Stanford, CA, USA
| | | | - Rebekah Tromble
- School of Media and Public Affairs, The George Washington University, Washington, DC, USA
- Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA
| | | | | | | | | | | | | | - Natalie Jomini Stroud
- Moody College of Communication, University of Texas at Austin, Austin, TX, USA
- Center for Media Engagement, University of Texas at Austin, Austin, TX, USA
| | - Joshua A Tucker
- Wilf Family Department of Politics, New York University, New York, NY, USA
- Center for Social Media and Politics, New York University, New York, NY, USA
| |
Collapse
|
10
|
Guess AM, Malhotra N, Pan J, Barberá P, Allcott H, Brown T, Crespo-Tenorio A, Dimmery D, Freelon D, Gentzkow M, González-Bailón S, Kennedy E, Kim YM, Lazer D, Moehler D, Nyhan B, Rivera CV, Settle J, Thomas DR, Thorson E, Tromble R, Wilkins A, Wojcieszak M, Xiong B, de Jonge CK, Franco A, Mason W, Stroud NJ, Tucker JA. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 2023; 381:398-404. [PMID: 37498999 DOI: 10.1126/science.abp9364] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/16/2023] [Indexed: 07/29/2023]
Abstract
We investigated the effects of Facebook's and Instagram's feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users' on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.
Collapse
Affiliation(s)
- Andrew M Guess
- Department of Politics and School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Neil Malhotra
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | - Jennifer Pan
- Department of Communication, Stanford University, Stanford, CA, USA
| | | | - Hunt Allcott
- Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, USA
| | | | | | - Drew Dimmery
- Meta, Menlo Park, CA, USA
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Deen Freelon
- UNC Hussman School of Journalism and Media, University of North Carolina at Chapel Hill, Chapel, NC, USA
| | | | | | - Edward Kennedy
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Young Mie Kim
- School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| | | | - Brendan Nyhan
- Department of Government, Dartmouth College, Hanover, NH, USA
| | | | - Jaime Settle
- Department of Government, William & Mary, Williamsburg, VA, USA
| | | | - Emily Thorson
- Department of Political Science, Syracuse University, Syracuse, NY, USA
| | - Rebekah Tromble
- School of Media and Public Affairs and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA
| | | | - Magdalena Wojcieszak
- Department of Communication, University of California, Davis, Davis, CA, USA
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands
| | | | | | | | | | - Natalie Jomini Stroud
- Moody College of Communication and Center for Media Engagement, University of Texas at Austin, Austin, TX, USA
| | - Joshua A Tucker
- Wilf Family Department of Politics and Center for Social Media and Politics, New York University, New York, NY, USA
| |
Collapse
|
11
|
Guess AM, Malhotra N, Pan J, Barberá P, Allcott H, Brown T, Crespo-Tenorio A, Dimmery D, Freelon D, Gentzkow M, González-Bailón S, Kennedy E, Kim YM, Lazer D, Moehler D, Nyhan B, Rivera CV, Settle J, Thomas DR, Thorson E, Tromble R, Wilkins A, Wojcieszak M, Xiong B, de Jonge CK, Franco A, Mason W, Stroud NJ, Tucker JA. Reshares on social media amplify political news but do not detectably affect beliefs or opinions. Science 2023; 381:404-408. [PMID: 37499012 DOI: 10.1126/science.add8424] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 03/16/2023] [Indexed: 07/29/2023]
Abstract
We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.
Collapse
Affiliation(s)
- Andrew M Guess
- Department of Politics and School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Neil Malhotra
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | - Jennifer Pan
- Department of Communication, Stanford University, Stanford, CA, USA
| | | | - Hunt Allcott
- Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, USA
| | | | | | | | - Deen Freelon
- UNC Hussman School of Journalism and Media, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Edward Kennedy
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Young Mie Kim
- School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| | | | - Brendan Nyhan
- Department of Government, Dartmouth College, Hanover, NH, USA
| | | | - Jaime Settle
- Department of Government, William & Mary, Williamsburg, VA, USA
| | | | - Emily Thorson
- Department of Political Science, Syracuse University, Syracuse, NY, USA
| | - Rebekah Tromble
- School of Media and Public Affairs and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA
| | | | | | | | | | | | | | - Natalie Jomini Stroud
- Moody College of Communication and Center for Media Engagement, University of Texas at Austin, Austin, TX, USA
| | - Joshua A Tucker
- Wilf Family Department of Politics and Center for Social Media and Politics, New York University, New York, NY, USA
| |
Collapse
|
12
|
González-Bailón S, Lazer D, Barberá P, Zhang M, Allcott H, Brown T, Crespo-Tenorio A, Freelon D, Gentzkow M, Guess AM, Iyengar S, Kim YM, Malhotra N, Moehler D, Nyhan B, Pan J, Rivera CV, Settle J, Thorson E, Tromble R, Wilkins A, Wojcieszak M, de Jonge CK, Franco A, Mason W, Stroud NJ, Tucker JA. Asymmetric ideological segregation in exposure to political news on Facebook. Science 2023; 381:392-398. [PMID: 37499003 DOI: 10.1126/science.ade7138] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 05/31/2023] [Indexed: 07/29/2023]
Abstract
Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta's Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook's news ecosystem than those favored by liberals.
Collapse
Affiliation(s)
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA
| | | | | | - Hunt Allcott
- Stanford Doerr School of Sustainability, Stanford University, Stanford, CA, USA
| | | | | | - Deen Freelon
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Andrew M Guess
- Department of Politics and School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Shanto Iyengar
- Department of Political Science, Stanford University, Stanford, CA, USA
| | - Young Mie Kim
- School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, USA
| | - Neil Malhotra
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | | | - Brendan Nyhan
- Department of Government, Dartmouth College, Hanover, NH, USA
| | - Jennifer Pan
- Department of Communication, Stanford University, Stanford, CA, USA
| | | | - Jaime Settle
- Department of Government, William & Mary, Williamsburg, VA, USA
| | - Emily Thorson
- Department of Political Science, Syracuse University, Syracuse, NY, USA
| | - Rebekah Tromble
- School of Media and Public Affairs and Institute for Data, Democracy, and Politics, The George Washington University, Washington, DC, USA
| | | | - Magdalena Wojcieszak
- Department of Communication, University of California, Davis, Davis, CA, USA
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands
| | | | | | | | - Natalie Jomini Stroud
- Moody College of Communication, University of Texas at Austin, Austin, TX, USA
- Center for Media Engagement, University of Texas at Austin, Austin, TX, USA
| | - Joshua A Tucker
- Wilf Family Department of Politics, New York University, New York, NY, USA
- Center for Social Media and Politics, New York University, New York, NY, USA
| |
Collapse
|
13
|
Robertson RE, Green J, Ruck DJ, Ognyanova K, Wilson C, Lazer D. Users choose to engage with more partisan news than they are exposed to on Google Search. Nature 2023:10.1038/s41586-023-06078-5. [PMID: 37225979 DOI: 10.1038/s41586-023-06078-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/12/2023] [Indexed: 05/26/2023]
Abstract
If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization1,2. This concern is central to the 'echo chamber'3-5 and 'filter bubble'6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources8-10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data-what real users were shown during their typical platform use-research in this vein typically relies on engagement data4,8,11-16 or estimates of hypothetical exposure17-23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants' engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users' own choices.
Collapse
Affiliation(s)
- Ronald E Robertson
- Stanford University, Stanford Internet Observatory, Stanford, CA, USA.
- Northeastern University, Network Science Institute, Boston, MA, USA.
| | - Jon Green
- Northeastern University, Network Science Institute, Boston, MA, USA
| | - Damian J Ruck
- Northeastern University, Network Science Institute, Boston, MA, USA
| | - Katherine Ognyanova
- Rutgers University, School of Communication & Information, New Brunswick, NJ, USA
| | - Christo Wilson
- Northeastern University, Network Science Institute, Boston, MA, USA
- Northeastern University, Khoury College of Computer Sciences, Boston, USA
| | - David Lazer
- Northeastern University, Network Science Institute, Boston, MA, USA
| |
Collapse
|
14
|
Green J, Druckman JN, Baum MA, Ognyanova K, Simonson MD, Perlis RH, Lazer D. Media use and vaccine resistance. PNAS Nexus 2023; 2:pgad146. [PMID: 37188276 PMCID: PMC10178922 DOI: 10.1093/pnasnexus/pgad146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
Public health requires collective action-the public best addresses health crises when individuals engage in prosocial behaviors. Failure to do so can have dire societal and economic consequences. This was made clear by the disjointed, politicized response to COVID-19 in the United States. Perhaps no aspect of the pandemic exemplified this challenge more than the sizeable percentage of individuals who delayed or refused vaccination. While scholars, practitioners, and the government devised a range of communication strategies to persuade people to get vaccinated, much less attention has been paid to where the unvaccinated could be reached. We address this question using multiple waves of a large national survey as well as various secondary data sets. We find that the vaccine resistant seems to predictably obtain information from conservative media outlets (e.g. Fox News) while the vaccinated congregate around more liberal outlets (e.g. MSNBC). We also find consistent evidence that vaccine-resistant individuals often obtain COVID-19 information from various social media, most notably Facebook, rather than traditional media sources. Importantly, such individuals tend to exhibit low institutional trust. While our results do not suggest a failure of sites such as Facebook's institutional COVID-19 efforts, as the counterfactual of no efforts is unknown, they do highlight an opportunity to reach those who are less likely to take vital actions in the service of public health.
Collapse
Affiliation(s)
- Jon Green
- Network Science Institute, Northeastern University, Boston, MA 02148, United States
- Shorenstein Center on Media, Politics and Public Policy, Harvard Kennedy School, Cambridge, MA 02138, United States
| | - James N Druckman
- Department of Political Science, Northwestern University, Evanston, IL 60208, United States
| | - Matthew A Baum
- Shorenstein Center on Media, Politics and Public Policy, Harvard Kennedy School, Cambridge, MA 02138, United States
| | - Katherine Ognyanova
- School of Communication and Information, Rutgers University, Piscataway, NJ 08854, United States
| | - Matthew D Simonson
- Department of Political Science, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Roy H Perlis
- Department of Psychiatry, Harvard Medical School, Boston, MA 02114, United States
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA 02148, United States
| |
Collapse
|
15
|
Perlis RH, Santillana M, Ognyanova K, Lazer D. Correlates of symptomatic remission among individuals with post-COVID-19 condition. medRxiv 2023:2023.01.31.23285246. [PMID: 36778263 PMCID: PMC9915816 DOI: 10.1101/2023.01.31.23285246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Importance Post-COVID-19 condition (PCC), or long COVID, has become prevalent. The course of this syndrome, and likelihood of remission, has not been characterized. Objective To quantify the rates of remission of PCC, and the sociodemographic features associated with remission. Design 16 waves of a 50-state U.S. non-probability internet survey conducted between August 2020 and November 2022. Setting Population-based. Participants Survey respondents age 18 and older. Main Outcome and Measure PCC remission, defined as reporting full recovery from COVID-19 symptoms among individuals who on a prior survey wave reported experiencing continued COVID-19 symptoms beyond 2 months after the initial month of symptoms. Results Among 423 survey respondents reporting continued symptoms more than 2 months after acute test-confirmed COVID-19 illness, who then completed at least 1 subsequent survey, mean age was 53.7 (SD 13.6) years; 293 (69%) identified as women, and 130 (31%) as men; 9 (2%) identified as Asian, 29 (7%) as Black, 13 (3%) as Hispanic, 15 (4%) as another category including Native American or Pacific Islander, and the remaining 357 (84%) as White. Overall, 131/423 (31%) of those who completed a subsequent survey reported no longer being symptomatic. In Cox regression models, male gender, younger age, lesser impact of PCC symptoms at initial visit, and infection when the Omicron strain predominated were all statistically significantly associated with greater likelihood of remission; presence of 'brain fog' or shortness of breath were associated with lesser likelihood of remission. Conclusions and Relevance A minority of individuals reported remission of PCC symptoms, highlighting the importance of efforts to identify treatments for this syndrome or means of preventing it.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | | | | | | |
Collapse
|
16
|
Abstract
Importance Little is known about the functional correlates of post-COVID-19 condition (PCC), also known as long COVID, particularly the relevance of neurocognitive symptoms. Objective To characterize prevalence of unemployment among individuals who did, or did not, develop PCC after acute infection. Design, Setting, and Participants This survey study used data from 8 waves of a 50-state US nonprobability internet population-based survey of respondents aged 18 to 69 years conducted between February 2021 and July 2022. Main Outcomes and Measures The primary outcomes were self-reported current employment status and the presence of PCC, defined as report of continued symptoms at least 2 months beyond initial month of symptoms confirmed by a positive COVID-19 test. Results The cohort included 15 308 survey respondents with test-confirmed COVID-19 at least 2 months prior, of whom 2236 (14.6%) reported PCC symptoms, including 1027 of 2236 (45.9%) reporting either brain fog or impaired memory. The mean (SD) age was 38.8 (13.5) years; 9679 respondents (63.2%) identified as women and 10 720 (70.0%) were White. Overall, 1418 of 15 308 respondents (9.3%) reported being unemployed, including 276 of 2236 (12.3%) of those with PCC and 1142 of 13 071 (8.7%) of those without PCC; 8229 respondents (53.8%) worked full-time, including 1017 (45.5%) of those with PCC and 7212 (55.2%) without PCC. In survey-weighted regression models excluding retired respondents, the presence of PCC was associated with a lower likelihood of working full-time (odds ratio [OR], 0.71 [95% CI, 0.63-0.80]; adjusted OR, 0.84 [95% CI, 0.74-0.96]) and with a higher likelihood of being unemployed (OR, 1.45 [95% CI, 1.22-1.73]; adjusted OR, 1.23 [95% CI, 1.02-1.48]). The presence of any cognitive symptom was associated with lower likelihood of working full time (OR, 0.70 [95% CI, 0.56-0.88]; adjusted OR, 0.75 [95% CI, 0.59-0.84]). Conclusions and Relevance PCC was associated with a greater likelihood of unemployment and lesser likelihood of working full time in adjusted models. The presence of cognitive symptoms was associated with diminished likelihood of working full time. These results underscore the importance of developing strategies to treat and manage PCC symptoms.
Collapse
Affiliation(s)
- Roy H Perlis
- Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Kristin Lunz Trujillo
- Northeastern University, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | - Alauna Safarpour
- Northeastern University, Boston, Massachusetts
- Harvard University, Cambridge, Massachusetts
| | | | | | | | - David Lazer
- Northeastern University, Boston, Massachusetts
| |
Collapse
|
17
|
Perlis RH, Trujillo KL, Safarpour A, Santillana M, Ognyanova K, Druckman J, Lazer D. Research Letter: Association between long COVID symptoms and employment status. medRxiv 2022:2022.11.17.22282452. [PMID: 36415464 PMCID: PMC9681048 DOI: 10.1101/2022.11.17.22282452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background Symptoms of Coronavirus-19 (COVID-19) infection persist beyond 2 months in a subset of individuals, a phenomenon referred to as long COVID, but little is known about its functional correlates and in particular the relevance of neurocognitive symptoms. Method We analyzed a previously-reported cohort derived from 8 waves of a nonprobability-sample internet survey called the COVID States Project, conducted every 4-8 weeks between February 2021 and July 2022. Primary analyses examined associations between long COVID and lack of full employment or unemployment, adjusted for age, sex, race and ethnicity, education, urbanicity, and region, using multiple logistic regression with interlocking survey weights. Results The cohort included 15,307 survey respondents ages 18-69 with test-confirmed COVID-19 at least 2 months prior, of whom 2,236 (14.6%) reported long COVID symptoms, including 1,027/2,236 (45.9%) reporting either 'brain fog' or impaired memory. Overall, 1,418/15,307 (9.3%) reported being unemployed, including 276/2,236 (12.3%) of those with long COVID and 1,142/13,071 (8.7%) of those without; 8,228 (53.8%) worked full-time, including 1,017 (45.5%) of those with long COVID and 7,211 (55.2%) without. In survey-weighted regression models, presence of long COVID was associated with being unemployed (crude OR 1.44, 95% CI 1.20-1.72; adjusted OR 1.23, 95% CI 1.02-1.48), and with lower likelihood of working full-time (crude OR 0.73, 95% CI 0.64-0.82; adjusted OR 0.79, 95% CI 0.70 -0.90). Among individuals with long COVID, the presence of cognitive symptoms - either brain fog or impaired memory - was associated with lower likelihood of working full time (crude OR 0.71, 95% CI 0.57-0.89, adjusted OR 0.77, 95% CI 0.61-0.97). Conclusion Long COVID was associated with a greater likelihood of unemployment and lesser likelihood of working full time in adjusted models. Presence of cognitive symptoms was associated with diminished likelihood of working full time. These results underscore the importance of developing strategies to respond to long COVID, and particularly the associated neurocognitive symptoms.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | | | - Alauna Safarpour
- Northeastern University, Boston, MA
- Harvard University, Cambridge, MA
| | | | | | | | | |
Collapse
|
18
|
Green J, Druckman JN, Baum MA, Lazer D, Ognyanova K, Perlis RH. Depressive Symptoms and Conspiracy Beliefs. Applied Cognitive Psychology 2022. [DOI: 10.1002/acp.4011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Jon Green
- Northeastern University and Harvard University
| | | | | | | | | | | |
Collapse
|
19
|
Perlis RH, Santillana M, Ognyanova K, Safarpour A, Lunz Trujillo K, Simonson MD, Green J, Quintana A, Druckman J, Baum MA, Lazer D. Prevalence and Correlates of Long COVID Symptoms Among US Adults. JAMA Netw Open 2022; 5:e2238804. [PMID: 36301542 PMCID: PMC9614581 DOI: 10.1001/jamanetworkopen.2022.38804] [Citation(s) in RCA: 126] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
IMPORTANCE Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection. OBJECTIVES To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia. MAIN OUTCOMES AND MEASURES Long COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test. RESULTS The 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6%) were women, and 6016 (37.4%) were men; 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White. From this cohort, 2359 individuals (14.7%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9% of those who had tested positive for COVID-19, or 1.7% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86). CONCLUSIONS AND RELEVANCE This study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection.
Collapse
Affiliation(s)
- Roy H. Perlis
- Department of Psychiatry, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Mauricio Santillana
- Department of Political Science, Northeastern University, Boston, Massachusetts
| | - Katherine Ognyanova
- Department of Communication, School of Communication and Information, Rutgers University, New Brunswick, New Jersey
| | - Alauna Safarpour
- Department of Political Science, Northeastern University, Boston, Massachusetts
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | - Kristin Lunz Trujillo
- Department of Political Science, Northeastern University, Boston, Massachusetts
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | | | - Jon Green
- Department of Political Science, Northeastern University, Boston, Massachusetts
| | - Alexi Quintana
- Department of Political Science, Northeastern University, Boston, Massachusetts
| | - James Druckman
- Department of Political Science, Northwestern University, Evanston, Illinois
| | - Matthew A. Baum
- John F. Kennedy School of Government and Department of Government, Harvard University, Cambridge, Massachusetts
| | - David Lazer
- Department of Political Science, Northeastern University, Boston, Massachusetts
| |
Collapse
|
20
|
Klein B, Generous N, Chinazzi M, Bhadricha Z, Gunashekar R, Kori P, Li B, McCabe S, Green J, Lazer D, Marsicano CR, Scarpino SV, Vespignani A. Higher education responses to COVID-19 in the United States: Evidence for the impacts of university policy. PLOS Digit Health 2022; 1:e0000065. [PMID: 36812533 PMCID: PMC9931316 DOI: 10.1371/journal.pdig.0000065] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 05/18/2022] [Indexed: 11/19/2022]
Abstract
With a dataset of testing and case counts from over 1,400 institutions of higher education (IHEs) in the United States, we analyze the number of infections and deaths from SARS-CoV-2 in the counties surrounding these IHEs during the Fall 2020 semester (August to December, 2020). We find that counties with IHEs that remained primarily online experienced fewer cases and deaths during the Fall 2020 semester; whereas before and after the semester, these two groups had almost identical COVID-19 incidence. Additionally, we see fewer cases and deaths in counties with IHEs that reported conducting any on-campus testing compared to those that reported none. To perform these two comparisons, we used a matching procedure designed to create well-balanced groups of counties that are aligned as much as possible along age, race, income, population, and urban/rural categories-demographic variables that have been shown to be correlated with COVID-19 outcomes. We conclude with a case study of IHEs in Massachusetts-a state with especially high detail in our dataset-which further highlights the importance of IHE-affiliated testing for the broader community. The results in this work suggest that campus testing can itself be thought of as a mitigation policy and that allocating additional resources to IHEs to support efforts to regularly test students and staff would be beneficial to mitigating the spread of COVID-19 in a pre-vaccine environment.
Collapse
Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, United States of America
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Nicholas Generous
- Network Science Institute, Northeastern University, Boston, United States of America
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- Biosecurity and Public Health Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Matteo Chinazzi
- Network Science Institute, Northeastern University, Boston, United States of America
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Zarana Bhadricha
- Network Science Institute, Northeastern University, Boston, United States of America
- College of Engineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Rishab Gunashekar
- Network Science Institute, Northeastern University, Boston, United States of America
- College of Engineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Preeti Kori
- Network Science Institute, Northeastern University, Boston, United States of America
- College of Engineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Bodian Li
- Network Science Institute, Northeastern University, Boston, United States of America
- College of Professional Studies, Northeastern University, Boston, Massachusetts, United States of America
| | - Stefan McCabe
- Network Science Institute, Northeastern University, Boston, United States of America
| | - Jon Green
- Network Science Institute, Northeastern University, Boston, United States of America
- Shorenstein Center on Media, Politics and Public Policy, Harvard University, Massachusetts, Boston, United States of America
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, United States of America
| | - Christopher R. Marsicano
- Educational Studies Department, Davidson College, Davidson, North Carolina, United States of America
- College Crisis Initiative, Davidson College, Davidson, North Carolina, United States of America
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Santa Fe Institute, Santa Fe, United States of America
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, United States of America
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| |
Collapse
|
21
|
Abstract
The public often turns to science for accurate health information, which, in an ideal world, would be error free. However, limitations of scientific institutions and scientific processes can sometimes amplify misinformation and disinformation. The current review examines four mechanisms through which this occurs: (1) predatory journals that accept publications for monetary gain but do not engage in rigorous peer review; (2) pseudoscientists who provide scientific-sounding information but whose advice is inaccurate, unfalsifiable, or inconsistent with the scientific method; (3) occasions when legitimate scientists spread misinformation or disinformation; and (4) miscommunication of science by the media and other communicators. We characterize this article as a "call to arms," given the urgent need for the scientific information ecosystem to improve. Improvements are necessary to maintain the public's trust in science, foster robust discourse, and encourage a well-educated citizenry.
Collapse
Affiliation(s)
- Briony Swire-Thompson
- senior research scientist and director of the Psychology of Misinformation Lab at Northeastern University
| | - David Lazer
- professor of political science and computer sciences at Northeastern University
| |
Collapse
|
22
|
Perlis RH, Simonson MD, Green J, Lin J, Safarpour A, Lunz Trujillo K, Quintana A, Chwe H, Della Volpe J, Ognyanova K, Santillana M, Druckman J, Lazer D, Baum MA. Prevalence of Firearm Ownership Among Individuals With Major Depressive Symptoms. JAMA Netw Open 2022; 5:e223245. [PMID: 35311961 PMCID: PMC8938748 DOI: 10.1001/jamanetworkopen.2022.3245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Both major depression and firearm ownership are associated with an increased risk for death by suicide in the United States, but the extent of overlap among these major risk factors is not well characterized. OBJECTIVE To assess the prevalence of current and planned firearm ownership among individuals with depression. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional survey study using data pooled from 2 waves of a 50-state nonprobability internet survey conducted between May and July 7, 2021. Internet survey respondents were 18 years of age or older and were sampled from all 50 US states and the District of Columbia. MAIN OUTCOMES AND MEASURES Self-reported firearm ownership; depressive symptoms as measured by the 9-item Patient Health Questionnaire. RESULTS Of 24 770 survey respondents (64.6% women and 35.4% men; 5.0% Asian, 10.8% Black, 7.5% Hispanic, and 74.0% White; mean [SD] age 45.8 [17.5]), 6929 (28.0%) reported moderate or greater depressive symptoms; this group had mean (SD) age of 38.18 (15.19) years, 4587 were female (66.2%), and 406 were Asian (5.9%), 725 were Black (10.5%), 652 were Hispanic (6.8%), and 4902 were White (70.7%). Of those with depression, 31.3% reported firearm ownership (n = 2167), of whom 35.9% (n = 777) reported purchasing a firearm within the past year. In regression models, the presence of moderate or greater depressive symptoms was not significantly associated with firearm ownership (adjusted odds ratio [OR], 1.07; 95% CI, 0.98-1.17) but was associated with greater likelihood of a first-time firearm purchase during the COVID-19 pandemic (adjusted OR, 1.77; 95% CI, 1.56-2.02) and greater likelihood of considering a future firearm purchase (adjusted OR, 1.53; 95% CI, 1.23-1.90). CONCLUSIONS AND RELEVANCE In this study, current and planned firearm ownership was common among individuals with major depressive symptoms, suggesting a public health opportunity to address this conjunction of suicide risk factors.
Collapse
Affiliation(s)
- Roy H. Perlis
- Department of Psychiatry and Center for Quantitative Health, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Matthew D. Simonson
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
- Department of Political Science, University of Pennsylvania, Philadelphia
| | - Jon Green
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
| | - Jennifer Lin
- Department of Political Science, Northwestern University, Evanston, Illinois
| | - Alauna Safarpour
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
- Harvard Kennedy School of Government, Cambridge, Massachusetts
| | - Kristin Lunz Trujillo
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
- Harvard Kennedy School of Government, Cambridge, Massachusetts
| | - Alexi Quintana
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
| | - Hanyu Chwe
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
| | | | - Katherine Ognyanova
- School of Communication and Information, Rutgers University, New Brunswick, New Jersey
| | - Mauricio Santillana
- Department of Pediatrics, Harvard Medical School, Cambridge, Massachusetts
- Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - James Druckman
- Department of Political Science, Northwestern University, Evanston, Illinois
| | - David Lazer
- Network Science Institute and Institute for Qualitative Social Science, Northeastern University, Boston, Massachusetts
| | - Matthew A. Baum
- Harvard Kennedy School of Government, Cambridge, Massachusetts
| |
Collapse
|
23
|
Swire-Thompson B, Miklaucic N, Wihbey JP, Lazer D, DeGutis J. The backfire effect after correcting misinformation is strongly associated with reliability. J Exp Psychol Gen 2022; 151:1655-1665. [PMID: 35130012 DOI: 10.1037/xge0001131] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The backfire effect is when a correction increases belief in the very misconception it is attempting to correct, and it is often used as a reason not to correct misinformation. The current study aimed to test whether correcting misinformation increases belief more than a no-correction control. Furthermore, we aimed to examine whether item-level differences in backfire rates were associated with test-retest reliability or theoretically meaningful factors. These factors included worldview-related attributes, including perceived importance and strength of precorrection belief, and familiarity-related attributes, including perceived novelty and the illusory truth effect. In 2 nearly identical experiments, we conducted a longitudinal pre/post design with N = 388 and 532 participants. Participants rated 21 misinformation items and were assigned to a correction condition or test-retest control. We found that no items backfired more in the correction condition compared to test-retest control or initial belief ratings. Item backfire rates were strongly negatively correlated with item reliability (ρ = -.61/-.73) and did not correlate with worldview-related attributes. Familiarity-related attributes were significantly correlated with backfire rate, though they did not consistently account for unique variance beyond reliability. While there have been previous papers highlighting the nonreplicable nature of backfire effects, the current findings provide a potential mechanism for this poor replicability. It is crucial for future research into backfire effects to use reliable measures, report the reliability of their measures, and take reliability into account in analyses. Furthermore, fact-checkers and communicators should not avoid giving corrective information due to backfire concerns. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Collapse
|
24
|
Perlis RH, Ognyanova K, Santillana M, Lin J, Druckman J, Lazer D, Green J, Simonson M, Baum MA, Della Volpe J. Association of Major Depressive Symptoms With Endorsement of COVID-19 Vaccine Misinformation Among US Adults. JAMA Netw Open 2022; 5:e2145697. [PMID: 35061036 PMCID: PMC8783266 DOI: 10.1001/jamanetworkopen.2021.45697] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Misinformation about COVID-19 vaccination may contribute substantially to vaccine hesitancy and resistance. OBJECTIVE To determine if depressive symptoms are associated with greater likelihood of believing vaccine-related misinformation. DESIGN, SETTING, AND PARTICIPANTS This survey study analyzed responses from 2 waves of a 50-state nonprobability internet survey conducted between May and July 2021, in which depressive symptoms were measured using the Patient Health Questionnaire 9-item (PHQ-9). Survey respondents were aged 18 and older. Population-reweighted multiple logistic regression was used to examine the association between moderate or greater depressive symptoms and endorsement of at least 1 item of vaccine misinformation, adjusted for sociodemographic features. The association between depressive symptoms in May and June, and new support for misinformation in the following wave was also examined. EXPOSURES Depressive symptoms. MAIN OUTCOMES AND MEASURES The main outcome was endorsing any of 4 common vaccine-related statements of misinformation. RESULTS Among 15 464 survey respondents (9834 [63.6%] women and 5630 [36.4%] men; 722 Asian respondents [4.7%], 1494 Black respondents [9.7%], 1015 Hispanic respondents [6.6%], and 11 863 White respondents [76.7%]; mean [SD] age, 47.9 [17.5] years), 4164 respondents (26.9%) identified moderate or greater depressive symptoms on the PHQ-9, and 2964 respondents (19.2%) endorsed at least 1 vaccine-related statement of misinformation. Presence of depression was associated with increased likelihood of endorsing misinformation (crude odds ratio [OR], 2.33; 95% CI, 2.09-2.61; adjusted OR, 2.15; 95% CI, 1.91-2.43). Respondents endorsing at least 1 misinformation item were significantly less likely to be vaccinated (crude OR, 0.40; 95% CI, 0.36-0.45; adjusted OR, 0.45; 95% CI, 0.40-0.51) and more likely to report vaccine resistance (crude OR, 2.54; 95% CI, 2.21-2.91; adjusted OR, 2.68; 95% CI, 2.89-3.13). Among 2809 respondents who answered a subsequent survey in July, presence of depression in the first survey was associated with greater likelihood of endorsing more misinformation compared with the prior survey (crude OR, 1.98; 95% CI, 1.42-2.75; adjusted OR, 1.63; 95% CI, 1.14-2.33). CONCLUSIONS AND RELEVANCE This survey study found that individuals with moderate or greater depressive symptoms were more likely to endorse vaccine-related misinformation, cross-sectionally and at a subsequent survey wave. While this study design cannot address causation, the association between depression and spread and impact of misinformation merits further investigation.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | | | - Mauricio Santillana
- Harvard Medical School, Boston, Massachusetts
- Boston Children’s Hospital, Boston, Massachusetts
| | | | | | - David Lazer
- Northeastern University, Boston, Massachusetts
| | - Jon Green
- Northeastern University, Boston, Massachusetts
| | | | | | | |
Collapse
|
25
|
Perlis RH, Green J, Simonson M, Ognyanova K, Santillana M, Lin J, Quintana A, Chwe H, Druckman J, Lazer D, Baum MA, Della Volpe J. Association Between Social Media Use and Self-reported Symptoms of Depression in US Adults. JAMA Netw Open 2021; 4:e2136113. [PMID: 34812844 PMCID: PMC8611479 DOI: 10.1001/jamanetworkopen.2021.36113] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Some studies suggest that social media use is associated with risk for depression, particularly among children and young adults. OBJECTIVE To characterize the association between self-reported use of individual social media platforms and worsening of depressive symptoms among adults. DESIGN, SETTING, AND PARTICIPANTS This survey study included data from 13 waves of a nonprobability internet survey conducted approximately monthly between May 2020 and May 2021 among individuals aged 18 years and older in the US. Data were analyzed in July and August 2021. MAIN OUTCOMES AND MEASURES Logistic regression was applied without reweighting, with a 5 point or greater increase in 9-item Patient Health Questionnaire (PHQ-9) score as outcome and participant sociodemographic features, baseline PHQ-9, and use of each social media platform as independent variables. RESULTS In total, 5395 of 8045 individuals (67.1%) with a PHQ-9 score below 5 on initial survey completed a second PHQ-9. These respondents had a mean (SD) age of 55.8 (15.2) years; 3546 respondents (65.7%) identified as female; 329 respondents (6.1%) were Asian, 570 (10.6%) Black, 256 (4.7%) Hispanic, 4118 (76.3%) White, and 122 (2.3%) American Indian or Alaska Native, Pacific Islander or Native Hawaiian, or other. Among eligible respondents, 482 (8.9%) reported 5 points or greater worsening of PHQ-9 score at second survey. In fully adjusted models for increase in symptoms, the largest adjusted odds ratio (aOR) associated with social media use was observed for Snapchat (aOR, 1.53; 95% CI, 1.19-1.96), Facebook (aOR, 1.42; 95% CI, 1.10-1.81), and TikTok (aOR, 1.39; 95% CI, 1.03-1.87). CONCLUSIONS AND RELEVANCE Among survey respondents who did not report depressive symptoms initially, social media use was associated with greater likelihood of subsequent increase in depressive symptoms after adjustment for sociodemographic features and news sources. These data cannot elucidate the nature of this association, but suggest the need for further study to understand how social media use may factor into depression among adults.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Jon Green
- Northeastern University, Boston, Massachusetts
| | - Matthew Simonson
- Northeastern University, Boston, Massachusetts
- University of Pennsylvania, Philadelphia
| | | | - Mauricio Santillana
- Harvard Medical School, Boston, Massachusetts
- Boston Children’s Hospital, Boston, Massachusetts
| | | | | | - Hanyu Chwe
- Northeastern University, Boston, Massachusetts
| | | | - David Lazer
- Northeastern University, Boston, Massachusetts
| | | | | |
Collapse
|
26
|
Kington RS, Arnesen S, Chou WYS, Curry SJ, Lazer D, Villarruel AM. Identifying Credible Sources of Health Information in Social Media: Principles and Attributes. NAM Perspect 2021; 2021:202107a. [PMID: 34611600 DOI: 10.31478/202107a] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
27
|
Perlis RH, Ognyanova K, Quintana A, Green J, Santillana M, Lin J, Druckman J, Lazer D, Simonson MD, Baum MA, Chwe H. Gender-specificity of resilience in major depressive disorder. Depress Anxiety 2021; 38:1026-1033. [PMID: 34370885 PMCID: PMC9544406 DOI: 10.1002/da.23203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/02/2021] [Accepted: 07/12/2021] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION The major stressors associated with the COVID-19 pandemic provide an opportunity to understand the extent to which protective factors against depression may exhibit gender-specificity. METHOD This study examined responses from multiple waves of a 50 states non-probability internet survey conducted between May 2020 and January 2021. Participants completed the PHQ-9 as a measure of depression, as well as items characterizing social supports. We used logistic regression models with population reweighting to examine association between absence of even mild depressive symptoms and sociodemographic features and social supports, with interaction terms and stratification used to investigate sex-specificity. RESULTS Among 73,917 survey respondents, 31,199 (42.2%) reported absence of mild or greater depression-11,011/23,682 males (46.5%) and 20,188/50,235 (40.2%) females. In a regression model, features associated with greater likelihood of depression-resistance included at least weekly attendance of religious services (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.16) and greater trust in others (OR: 1.04 for a 2-unit increase, 95% CI: 1.02-1.06), along with level of social support measured as number of social ties available who could provide care (OR: 1.05, 95% CI: 1.02-1.07), talk to them (OR: 1.10, 95% CI: 1.07-1.12), and help with employment (OR: 1.06, 95% CI: 1.04-1.08). The first two features showed significant interaction with gender (p < .0001), with markedly greater protective effects among women. CONCLUSION Aspects of social support are associated with diminished risk of major depressive symptoms, with greater effects of religious service attendance and trust in others observed among women than men.
Collapse
Affiliation(s)
- Roy H. Perlis
- Center for Quantitative HealthMassachusetts General HospitalBostonMassachusettsUSA
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | | | - Alexi Quintana
- Network Science InstituteNortheastern UniversityBostonMassachusettsUSA
| | - Jon Green
- Network Science InstituteNortheastern UniversityBostonMassachusettsUSA
| | - Mauricio Santillana
- Computational Health Informatics ProgramBoston Children's HospitalBostonMassachusettsUSA
- Department of PediatricsHarvard Medical SchoolBostonMassachusettsUSA
- Department of EpidemiologHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Jennifer Lin
- Department of Political ScienceNorthwestern UniversityEvanstonIllinoisUSA
| | - James Druckman
- Department of Political Science and Institute for Policy ResearchNorthwestern UniversityEvanstonIllinoisUSA
| | - David Lazer
- Network Science InstituteNortheastern UniversityBostonMassachusettsUSA
| | | | - Matthew A. Baum
- John F. Kennedy School of GovernmentHarvard UniversityCambridgeMassachusettsUSA
| | - Hanyu Chwe
- Network Science InstituteNortheastern UniversityBostonMassachusettsUSA
| |
Collapse
|
28
|
Lazer D, Hargittai E, Freelon D, Gonzalez-Bailon S, Munger K, Ognyanova K, Radford J. Meaningful measures of human society in the twenty-first century. Nature 2021; 595:189-196. [PMID: 34194043 DOI: 10.1038/s41586-021-03660-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/20/2021] [Indexed: 02/06/2023]
Abstract
Science rarely proceeds beyond what scientists can observe and measure, and sometimes what can be observed proceeds far ahead of scientific understanding. The twenty-first century offers such a moment in the study of human societies. A vastly larger share of behaviours is observed today than would have been imaginable at the close of the twentieth century. Our interpersonal communication, our movements and many of our everyday actions, are all potentially accessible for scientific research; sometimes through purposive instrumentation for scientific objectives (for example, satellite imagery), but far more often these objectives are, literally, an afterthought (for example, Twitter data streams). Here we evaluate the potential of this massive instrumentation-the creation of techniques for the structured representation and quantification-of human behaviour through the lens of scientific measurement and its principles. In particular, we focus on the question of how we extract scientific meaning from data that often were not created for such purposes. These data present conceptual, computational and ethical challenges that require a rejuvenation of our scientific theories to keep up with the rapidly changing social realities and our capacities to capture them. We require, in other words, new approaches to manage, use and analyse data.
Collapse
Affiliation(s)
- David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA. .,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA.
| | - Eszter Hargittai
- Department of Communication and Media Research, University of Zurich, Zurich, Switzerland
| | - Deen Freelon
- Hussman School of Journalism and Media, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Kevin Munger
- Department of Political Science, Pennsylvania State University, State College, PA, USA
| | - Katherine Ognyanova
- School of Communication and Information, Rutgers University, New Brunswick, NJ, USA
| | - Jason Radford
- Network Science Institute, Northeastern University, Boston, MA, USA
| |
Collapse
|
29
|
Perlis RH, Santillana M, Ognyanova K, Green J, Druckman J, Lazer D, Baum MA. Factors Associated With Self-reported Symptoms of Depression Among Adults With and Without a Previous COVID-19 Diagnosis. JAMA Netw Open 2021; 4:e2116612. [PMID: 34115130 PMCID: PMC8196339 DOI: 10.1001/jamanetworkopen.2021.16612] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This survey study compares features of self-reported symptoms of major depression in adults with or without a prior COVID-19 diagnosis.
Collapse
Affiliation(s)
- Roy H. Perlis
- Departmentof Psychiatry, Massachusetts General Hospital, Boston
- Departmentof Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | - Jon Green
- Network Science Institute, Northeastern University, Boston, Massachusetts
| | - James Druckman
- Departmentof Political Science and Institute for Policy Research, Northwestern University, Evanston, Illinois
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts
| | - Matthew A. Baum
- Kennedy School of Government, Harvard University, Cambridge, Massachusetts
| |
Collapse
|
30
|
Druckman JN, Ognyanova K, Baum MA, Lazer D, Perlis RH, Volpe JD, Santillana M, Chwe H, Quintana A, Simonson M. The role of race, religion, and partisanship in misperceptions about COVID-19. Group Processes & Intergroup Relations 2021. [DOI: 10.1177/1368430220985912] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Concerns about misperceptions among the public are rampant. Yet, little work explores the correlates of misperceptions in varying contexts – that is, how do factors such as group affiliations, media exposure, and lived experiences correlate with the number of misperceptions people hold? We address these questions by investigating misperceptions about COVID-19, focusing on the role of racial/ethnic, religious, and partisan groups. Using a large survey, we find the number of correct beliefs held by individuals far dwarfs the number of misperceptions. When it comes to misperceptions, we find that minorities, those with high levels of religiosity, and those with strong partisan identities – across parties – hold a substantially greater number of misperceptions than those with contrasting group affiliations. Moreover, we show other variables (e.g., social media usage, number of COVID-19 cases in one’s county) do not have such strong relationships with misperceptions, and the group-level results do not reflect acquiescence to believing any information regardless of its truth value. Our results accentuate the importance of studying group-level misperceptions on other scientific and political issues and developing targeted interventions for these groups.
Collapse
|
31
|
Perlis RH, Santillana M, Ognyanova K, Green J, Druckman J, Lazer D, Baum MA. Comparison of post-COVID depression and major depressive disorder. medRxiv 2021:2021.03.26.21254425. [PMID: 33821286 PMCID: PMC8020988 DOI: 10.1101/2021.03.26.21254425] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND During the COVID-19 pandemic rates of depressive symptoms are markedly elevated, particularly among survivors of infection. Understanding whether such symptoms are distinct among those with prior SARS-CoV-2 infection, or simply a nonspecific reflection of elevated stress, could help target interventions. METHOD We analyzed data from multiple waves of a 50-state survey that included questions about COVID-19 infection as well as the Patient Health Questionnaire examining depressive and anxious symptoms. We utilized multiple logistic regression to examine whether sociodemographic features associated with depression liability differed for those with or without prior COVID-19, and then whether depressive symptoms differed among those with or without prior COVID-19. RESULTS Among 91,791 respondents, in regression models, age, gender, race, education, and income all exhibited an interaction with prior COVID-19 in risk for moderate or greater depressive symptoms (p<0.0001 in all cases), indicating differential risk in the two subgroups. Among those with such symptoms, levels of motoric symptoms and suicidality were significantly greater among those with prior COVID-19 illness. Depression risk increased with greater interval following acute infection. DISCUSSION Our results suggest that major depressive symptoms observed among individuals with prior COVID-19 illness may not reflect typical depressive episodes, and merit more focused neurobiological investigation.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | | | | | | | | | | | | |
Collapse
|
32
|
Perlis RH, Green J, Santillana M, Lazer D, Ognyanova K, Simonson M, Baum MA, Quintana A, Chwe H, Druckman J, Volpe JD, Lin J. Persistence of symptoms up to 10 months following acute COVID-19 illness. medRxiv 2021:2021.03.07.21253072. [PMID: 33758896 PMCID: PMC7987055 DOI: 10.1101/2021.03.07.21253072] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
IMPORTANCE COVID-19 symptoms are increasingly recognized to persist among a subset of individual following acute infection, but features associated with this persistence are not well-understood. OBJECTIVE We aimed to identify individual features that predicted persistence of symptoms over at least 2 months at the time of survey completion.Design: Non-probability internet survey. Participants were asked to identify features of acute illness as well as persistence of symptoms at time of study completion. We used logistic regression models to examine association between sociodemographic and clinical features and persistence of symptoms at or beyond 2 months. SETTING Ten waves of a fifty-state survey between June 13, 2020 and January 13, 2021. PARTICIPANTS 6,211 individuals who reported symptomatic COVID-19 illness confirmed by positive test or clinician diagnosis. EXPOSURE symptomatic COVID-19 illness. RESULTS Among 6,211 survey respondents reporting COVID-19 illness, with a mean age of 37.8 (SD 12.2) years and 45.1% female, 73.9% white, 10.0% Black, 9.9% Hispanic, and 3.1% Asian, a total of 4946 (79.6%) had recovered within less than 2 months, while 491 (7.9%) experienced symptoms for 2 months or more. Of the full cohort, 3.4% were symptomatic for 4 months or more and 2.2% for 6 months or more. In univariate analyses, individuals with persistent symptoms on average reported greater initial severity. In logistic regression models, older age was associated with greater risk of persistence (OR 1.10, 95% CI 1.01-1.19 for each decade beyond 40); otherwise, no significant associations with persistence were identified for gender, race/ethnicity, or income. Presence of headache was significantly associated with greater likelihood of persistence (OR 1.44, 95% CI 1.11-1.86), while fever was associated with diminished likelihood of persistence (OR 0.66, 95% CI 0.53-0.83). CONCLUSION AND RELEVANCE A subset of individuals experience persistent symptoms from 2 to more than 10 months after acute COVID-19 illness, particularly those who recall headache and absence of fever. In light of this prevalence, strategies for predicting and managing such sequelae are needed. TRIAL REGISTRATION NA. KEY POINTS Question: Which individuals are at greatest risk for post-acute sequelae of COVID-19?Findings: In this non-probability internet survey, among 6,211 individuals with symptomatic COVID-19 illness, 7.9% experienced persistence of symptoms lasting 2 months or longer. Older age, but not other sociodemographic features, was associated with risk for persistence, as was headache.Meaning: Identifying individuals at greater risk for symptomatic persistence may facilitate development of targeted interventions.
Collapse
|
33
|
Abstract
This survey study investigates where acute coronavirus disease 2019 (COVID-19) is associated with the probability of subsequent depressive symptoms among US adults.
Collapse
Affiliation(s)
- Roy H. Perlis
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
- Associate Editor, JAMA Network Open
| | | | | | | | - David Lazer
- Northeastern University, Boston, Massachusetts
| | | | | |
Collapse
|
34
|
Wojcik S, Bijral AS, Johnston R, Lavista Ferres JM, King G, Kennedy R, Vespignani A, Lazer D. Survey data and human computation for improved flu tracking. Nat Commun 2021; 12:194. [PMID: 33419989 PMCID: PMC7794445 DOI: 10.1038/s41467-020-20206-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Accepted: 11/13/2020] [Indexed: 11/08/2022] Open
Abstract
While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users' online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.
Collapse
Affiliation(s)
| | | | | | | | - Gary King
- Harvard University, Cambridge, MA, USA
| | - Ryan Kennedy
- University of Houston, Philip Guthrie Hoffman Hall, Houston, TX, USA
| | | | - David Lazer
- Harvard University, Cambridge, MA, USA
- Northeastern University, 177 Huntington Ave, Boston, MA, USA
| |
Collapse
|
35
|
Abstract
The internet has become a popular resource to learn about health and to investigate one's own health condition. However, given the large amount of inaccurate information online, people can easily become misinformed. Individuals have always obtained information from outside the formal health care system, so how has the internet changed people's engagement with health information? This review explores how individuals interact with health misinformation online, whether it be through search, user-generated content, or mobile apps. We discuss whether personal access to information is helping or hindering health outcomes and how the perceived trustworthiness of the institutions communicating health has changed over time. To conclude, we propose several constructive strategies for improving the online information ecosystem. Misinformation concerning health has particularly severe consequences with regard to people's quality of life and even their risk of mortality; therefore, understanding it within today's modern context is an extremely important task.
Collapse
Affiliation(s)
- Briony Swire-Thompson
- Network Science Institute, Northeastern University, Boston, Massachusetts 02115, USA; , .,Institute for Quantitative Social Science, Harvard University, Cambridge, Massachusetts 02138, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts 02115, USA; , .,Institute for Quantitative Social Science, Harvard University, Cambridge, Massachusetts 02138, USA
| |
Collapse
|
36
|
Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D. Fake news on Twitter during the 2016 U.S. presidential election. Science 2019; 363:374-378. [PMID: 30679368 DOI: 10.1126/science.aau2706] [Citation(s) in RCA: 251] [Impact Index Per Article: 50.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 01/02/2019] [Indexed: 11/02/2022]
Abstract
The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
Collapse
Affiliation(s)
- Nir Grinberg
- Network Science Institute, Northeastern University, Boston, MA, USA.,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - Kenneth Joseph
- Department of Computer Science and Engineering, University at Buffalo, SUNY, Buffalo, NY, USA
| | - Lisa Friedland
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Briony Swire-Thompson
- Network Science Institute, Northeastern University, Boston, MA, USA.,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, MA, USA. .,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| |
Collapse
|
37
|
Riedl C, Bjelland J, Canright G, Iqbal A, Engø-Monsen K, Qureshi T, Sundsøy PR, Lazer D. Product diffusion through on-demand information-seeking behaviour. J R Soc Interface 2018; 15:rsif.2017.0751. [PMID: 29467257 PMCID: PMC5832727 DOI: 10.1098/rsif.2017.0751] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/29/2018] [Indexed: 11/24/2022] Open
Abstract
Most models of product adoption predict S-shaped adoption curves. Here we report results from two country-scale experiments in which we find linear adoption curves. We show evidence that the observed linear pattern is the result of active information-seeking behaviour: individuals actively pulling information from several central sources facilitated by modern Internet searches. Thus, a constant baseline rate of interest sustains product diffusion, resulting in a linear diffusion process instead of the S-shaped curve of adoption predicted by many diffusion models. The main experiment seeded 70 000 (48 000 in Experiment 2) unique voucher codes for the same product with randomly sampled nodes in a social network of approximately 43 million individuals with about 567 million ties. We find that the experiment reached over 800 000 individuals with 80% of adopters adopting the same product—a winner-take-all dynamic consistent with search engine driven rankings that would not have emerged had the products spread only through a network of social contacts. We provide evidence for (and characterization of) this diffusion process driven by active information-seeking behaviour through analyses investigating (a) patterns of geographical spreading; (b) the branching process; and (c) diffusion heterogeneity. Using data on adopters' geolocation we show that social spreading is highly localized, while on-demand diffusion is geographically independent. We also show that cascades started by individuals who actively pull information from central sources are more effective at spreading the product among their peers.
Collapse
Affiliation(s)
- Christoph Riedl
- Northeastern University, Boston, MA, USA .,Harvard University, Cambridge, MA, USA
| | | | | | | | | | | | | | - David Lazer
- Northeastern University, Boston, MA, USA.,Harvard University, Cambridge, MA, USA
| |
Collapse
|
38
|
Abstract
A breezy, personal guide provides a road map to solid computational social science research
Collapse
Affiliation(s)
- David Lazer
- The reviewer is at the Department of Political Science and the College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA, and the Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
| |
Collapse
|
39
|
Abstract
This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.
Collapse
Affiliation(s)
- Ryan Kennedy
- Center for International and Comparative Studies, University of Houston, Houston, TX, USA.
| | - Stefan Wojcik
- Lazer Lab, Northeastern University, Boston, MA, USA.,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - David Lazer
- Lazer Lab, Northeastern University, Boston, MA, USA.,Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| |
Collapse
|
40
|
Jasny BR, Wigginton N, McNutt M, Bubela T, Buck S, Cook-Deegan R, Gardner T, Hanson B, Hustad C, Kiermer V, Lazer D, Lupia A, Manrai A, McConnell L, Noonan K, Phimister E, Simon B, Strandburg K, Summers Z, Watts D. Fostering reproducibility in industry-academia research. Science 2017; 357:759-761. [PMID: 28839064 DOI: 10.1126/science.aan4906] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
| | - N Wigginton
- University of Michigan, Ann Arbor, MI 48109, USA
| | - M McNutt
- National Academy of Sciences, Washington, DC 20001, USA.
| | - T Bubela
- Faculty of Health Sciences, Simon Fraser University, British Columbia, BC V5A 1S6, Canada
| | - S Buck
- Laura and John Arnold Foundation, Houston, TX 77056, USA
| | - R Cook-Deegan
- Consortium for Science Policy and Outcomes at Arizona State University, Washington, DC 20009, USA
| | - T Gardner
- Riffyn, Inc., Oakland, CA 94612, USA
| | - B Hanson
- American Geophysical Union, Washington, DC 20009, USA
| | - C Hustad
- Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - V Kiermer
- Public Library of Science (PLOS), San Francisco, CA 94111, USA
| | - D Lazer
- Northeastern University, Boston, MA 02115, USA
| | - A Lupia
- University of Michigan, Ann Arbor, MI 48109, USA
| | - A Manrai
- Harvard Medical School, Boston, MA 02115, USA
| | - L McConnell
- Bayer U.S., Research Triangle Park, NC 27709, USA
| | - K Noonan
- McDonnell Boehnen Hulbert & Berghoff LLP, Chicago, IL 60606, USA
| | - E Phimister
- The New England Journal of Medicine, Boston, MA 02115, USA
| | - B Simon
- Thomas Jefferson School of Law, San Diego, CA 92101, USA
| | - K Strandburg
- New York University School of Law, New York, NY 10012, USA
| | - Z Summers
- ExxonMobil Research and Engineering Company, Annandale, NJ 08801, USA
| | - D Watts
- Microsoft Research, New York, NY 10003, USA
| |
Collapse
|
41
|
Affiliation(s)
- Wei Wang
- Discovery Analytics Center, Virginia Tech, Arlington, VA 22203, USA
| | - Ryan Kennedy
- Center for International and Comparative Studies, University of Houston, Houston, TX 77204, USA.
| | - David Lazer
- Lazer Lab, Northeastern University, Boston, MA 02115, USA. Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
| | | |
Collapse
|
42
|
Abstract
Experimental research in traditional laboratories comes at a significant logistic and financial cost while drawing data from demographically narrow populations. The growth of online methods of research has resulted in effective means for social psychologists to collect large-scale survey-based data in a cost-effective and timely manner. However, the same advancement has not occurred for social psychologists who rely on experimentation as their primary method of data collection. The aim of this article is to provide an overview of one online laboratory for conducting experiments, Volunteer Science, and report the results of six studies that test canonical behaviors commonly captured in social psychological experiments. Our results show that the online laboratory is capable of performing a variety of studies with large numbers of diverse volunteers. We advocate for the use of the online laboratory as a valid and cost-effective way to perform social psychological experiments with large numbers of diverse subjects.
Collapse
Affiliation(s)
- Jason Radford
- University of Chicago, Chicago, IL, USA
- Northeastern University, Boston, MA, USA
| | - Andy Pilny
- University of Kentucky, Lexington, KY, USA
| | | | - Brian Keegan
- University of Colorado, Boulder, Boulder, CO, USA
| | | | | | | | | | | |
Collapse
|
43
|
Abstract
This article describes the network approach to small groups. First, the core constructs that compose social network research are explained. The primary theories that provide the intellectual underpinning of the network approach are described, including theories of self-interest, theories of social exchange or dependency, theories of mutual or collective interest, cognitive theories, and theories of homophily. Highlights of the empirical work examining the internal and external networks of small groups is summarized. Finally, the primary challenges researchers face when applying the network perspective to small groups, and the primary benefits that can accrue to researchers who adopt that perspective, are enumerated.
Collapse
|
44
|
Abstract
Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behaviour following the plant's closure. For these affected individuals, we observe significant declines in social behaviour and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviours, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure microeconomic behaviour and improve estimates of critical economic indicators.
Collapse
Affiliation(s)
| | - Yu-Ru Lin
- School of Information Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | | | - Daniel Shoag
- Harvard Kennedy School, Harvard University, Cambridge, MA 02144, USA
| | - Marta C González
- Department of Civil and Environmental Engineering, MIT, Cambridge, MA 02144, USA
| | - David Lazer
- Harvard Kennedy School, Harvard University, Cambridge, MA 02144, USA Lazer Laboratory, Department of Political Science and College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
| |
Collapse
|
45
|
Shore J, Bernstein E, Lazer D. Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces. Organization Science 2015. [DOI: 10.1287/orsc.2015.0980] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
46
|
Lin Y, Margolin D, Lazer D. Uncovering social semantics from textual traces: A theory‐driven approach and evidence from public statements of
U
.
S
.
M
embers of
C
ongress. J Assoc Inf Sci Technol 2015. [DOI: 10.1002/asi.23540] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Yu‐Ru Lin
- School of Information Sciences University of Pittsburgh 710 IS Building, University of Pittsburgh 135 North Bellefield Avenue Pittsburgh PA 15260
| | - Drew Margolin
- Department of Communication Cornell University Ithaca NY 14580
| | - David Lazer
- Department of Political Science and College of Computer and Information Science Northeastern University Boston MA 02115
- John F. Kennedy School of Government Harvard University Cambridge MA 02138
| |
Collapse
|
47
|
Affiliation(s)
- David Lazer
- Department of Political Science and College of Computer and Information Science, Northeastern University. John F. Kennedy School of Government, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
48
|
Hills TT, Todd PM, Lazer D, Redish AD, Couzin ID. Exploration versus exploitation in space, mind, and society. Trends Cogn Sci 2014; 19:46-54. [PMID: 25487706 DOI: 10.1016/j.tics.2014.10.004] [Citation(s) in RCA: 211] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2014] [Revised: 10/23/2014] [Accepted: 10/30/2014] [Indexed: 11/25/2022]
Abstract
Search is a ubiquitous property of life. Although diverse domains have worked on search problems largely in isolation, recent trends across disciplines indicate that the formal properties of these problems share similar structures and, often, similar solutions. Moreover, internal search (e.g., memory search) shows similar characteristics to external search (e.g., spatial foraging), including shared neural mechanisms consistent with a common evolutionary origin across species. Search problems and their solutions also scale from individuals to societies, underlying and constraining problem solving, memory, information search, and scientific and cultural innovation. In summary, search represents a core feature of cognition, with a vast influence on its evolution and processes across contexts and requiring input from multiple domains to understand its implications and scope.
Collapse
Affiliation(s)
- Thomas T Hills
- Department of Psychology, University of Warwick, Coventry, UK.
| | - Peter M Todd
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - David Lazer
- Department of Political Science, Northeastern University, Boston, MA, USA; College of Computer and Information Science, Northeastern University, Boston, MA, USA; Harvard Kennedy School, Harvard University, Cambridge, MA, USA
| | - A David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Iain D Couzin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Department of Collective Behaviour, Max Planck Institute of Ornithology, Konstanz, Germany
| | | |
Collapse
|
49
|
Lin YR, Margolin D, Lazer D. Tracing Coordination and Cooperation Structures via Semantic Burst Detection. EAI Endorsed Transactions on Collaborative Computing 2014. [DOI: 10.4108/cc.1.2.e7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
|
50
|
Affiliation(s)
- David Lazer
- Lazer Laboratory, Northeastern University, Boston, MA 02115, USA. Harvard Kennedy School, Harvard University, Cambridge, MA 02138, USA.
| | - Ryan Kennedy
- Lazer Laboratory, Northeastern University, Boston, MA 02115, USA. Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA. University of Houston, Houston, TX 77204, USA
| | - Gary King
- Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Sociotechnical Systems, Northeastern University, Boston, MA 02115, USA. Institute for Scientific Interchange Foundation, Turin, Italy. Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
| |
Collapse
|