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Kolaski K, Logan LR, Ioannidis JPA. Guidance to best tools and practices for systematic reviews. Br J Pharmacol 2024; 181:180-210. [PMID: 37282770 DOI: 10.1111/bph.16100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 06/08/2023] Open
Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy. A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work. Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, New York, USA
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California, USA
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Kolaski K, Logan LR, Ioannidis JPA. Guidance to best tools and practices for systematic reviews. Acta Anaesthesiol Scand 2023; 67:1148-1177. [PMID: 37288997 DOI: 10.1111/aas.14295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 06/09/2023]
Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy. A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work. Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, New York, USA
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California, USA
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Ng JY, Liu S, Maini I, Pereira W, Cramer H, Moher D. Complementary, alternative, and integrative medicine-specific COVID-19 misinformation on social media: A scoping review. Integr Med Res 2023; 12:100975. [PMID: 37646043 PMCID: PMC10460953 DOI: 10.1016/j.imr.2023.100975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 09/01/2023] Open
Abstract
Background The sharing of health-related information has become increasingly popular on social media. Unregulated information sharing has led to the spread of misinformation, especially regarding complementary, alternative, and integrative medicine (CAIM). This scoping review synthesized evidence surrounding the spread of CAIM-related misinformation on social media during the COVID-19 pandemic. Methods This review was informed by a modified version of the Arksey and O'Malley scoping review framework. AMED, EMBASE, PsycINFO and MEDLINE databases were searched systematically from inception to January 2022. Eligible articles explored COVID-19 misinformation on social media and contained sufficient information on CAIM therapies. Common themes were identified using an inductive thematic analysis approach. Results Twenty-eight articles were included. The following themes were synthesized: 1) misinformation prompts unsafe and harmful behaviours, 2) misinformation can be separated into different categories, 3) individuals are capable of identifying and refuting CAIM misinformation, and 4) studies argue governments and social media companies have a responsibility to resolve the spread of COVID-19 misinformation. Conclusions Misinformation can spread more easily when shared on social media. Our review suggests that misinformation about COVID-19 related to CAIM that is disseminated online contributes to unsafe health behaviours, however, this may be remedied via public education initiatives and stricter media guidelines. The results of this scoping review are crucial to understanding the behavioural impacts of the spread of COVID-19 misinformation about CAIM therapies, and can inform the development of public health policies to mitigate these issues.
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Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Bosch Health Campus, Stuttgart, Germany
| | - Shawn Liu
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Ishana Maini
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Will Pereira
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Bosch Health Campus, Stuttgart, Germany
| | - David Moher
- Centre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
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Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy. A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work. Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - John P.A. Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, CA, USA
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Kolaski K, Logan LR, Ioannidis JPA. Guidance to best tools and practices for systematic reviews. BMC Infect Dis 2023; 23:383. [PMID: 37286949 DOI: 10.1186/s12879-023-08304-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/03/2023] [Indexed: 06/09/2023] Open
Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy.A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work.Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, CA, USA
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Kolaski K, Logan LR, Ioannidis JPA. Guidance to best tools and practices for systematic reviews. Syst Rev 2023; 12:96. [PMID: 37291658 DOI: 10.1186/s13643-023-02255-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/19/2023] [Indexed: 06/10/2023] Open
Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy.A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work.Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, CA, USA
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Kolaski K, Logan LR, Ioannidis JPA. Guidance to Best Tools and Practices for Systematic Reviews. JBJS Rev 2023; 11:01874474-202306000-00009. [PMID: 37285444 DOI: 10.2106/jbjs.rvw.23.00077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
» Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy.» A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work.» Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, New York
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California
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Kolaski K, Romeiser Logan L, Ioannidis JPA. Guidance to best tools and practices for systematic reviews1. J Pediatr Rehabil Med 2023; 16:241-273. [PMID: 37302044 DOI: 10.3233/prm-230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2023] Open
Abstract
Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy.A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well-documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work.Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in-depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field.
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Affiliation(s)
- Kat Kolaski
- Departments of Orthopaedic Surgery, Pediatrics, and Neurology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lynne Romeiser Logan
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, CA, USA
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Weng Z, Lin A. Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16376. [PMID: 36554258 PMCID: PMC9779151 DOI: 10.3390/ijerph192416376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/27/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.
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Curtis C, Stillman J, Remmel M, Pierce JC, Lovrich NP, Adams‐Curtis LE. Partisan polarization, historical heritage, and public health: Exploring COVID-19 outcomes. WORLD MEDICAL & HEALTH POLICY 2022; 15:WMH3543. [PMID: 36248195 PMCID: PMC9537783 DOI: 10.1002/wmh3.543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/23/2022]
Abstract
When the COVID-19 virus first arrived in the United States in early 2020, many epidemiologists and public health officers counseled for shutdowns and advised policymakers to prepare for a major pandemic. In 2020, though, US society was rife with major political and cultural divides. Some elected leaders promoted policies at odds with the experts, and many people refused to heed the public health-based communications about the coming pandemic. Additionally, the capacity to respond to a pandemic was distributed in the country in a highly unequal fashion. This paper analyzes the noteworthy geopolitical patterns of COVID-19 illnesses, subsequent demands on hospitals, and resulting deaths. This description is based on a snapshot of archival data gathered in the midst of the pandemic during late January and early February of 2021. Demographic data, indicators of political party support, indicators of citizen attitudes, and public health compliance behaviors are combined in a multivariate analysis to explain COVID-19 outcomes at the local government (county) level. The analysis suggests strongly that regional political culture and local demographics played a substantial role in determining the severity of the public health impact of the COVID-19 pandemic.
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Affiliation(s)
- Craig Curtis
- Department of Political ScienceBradley UniversityPeoriaIllinoisUSA
| | - John Stillman
- Department of Political ScienceBradley UniversityPeoriaIllinoisUSA
| | - Megan Remmel
- Department of Political ScienceBradley UniversityPeoriaIllinoisUSA
| | - John C. Pierce
- University of Kansas College of Liberal Arts and SciencesLawrenceKansasUSA
| | | | - Leah E. Adams‐Curtis
- Director of Institutional Research at Mount Mary University in MilwaukeeWI
- Present address:
Leah E. Adams-Curtis, Institutional Research, Mount Mary UniversityMilwaukeeWIUSA
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Albalawi Y, Nikolov NS, Buckley J. Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study. JMIR Form Res 2022; 6:e34834. [PMID: 35767322 PMCID: PMC9280463 DOI: 10.2196/34834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 01/26/2023] Open
Abstract
Background In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base. Conclusions Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.
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Affiliation(s)
- Yahya Albalawi
- Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland
- Department of Computer and Information Sciences, College of Arts and Science, University of Taibah, Al-Ula, Saudi Arabia
- The Irish Software Research Centre, Lero, University of Limerick, Limerick, Ireland
| | - Nikola S Nikolov
- Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland
| | - Jim Buckley
- Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland
- The Irish Software Research Centre, Lero, University of Limerick, Limerick, Ireland
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