1
|
Hur JK, Heffner J, Feng GW, Joormann J, Rutledge RB. Language sentiment predicts changes in depressive symptoms. Proc Natl Acad Sci U S A 2024; 121:e2321321121. [PMID: 39284070 DOI: 10.1073/pnas.2321321121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/26/2024] [Indexed: 10/02/2024] Open
Abstract
The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (N = 467), participants provided responses to neutral open-ended questions, narrating aspects of their lives relevant to depression (e.g., mood, motivation, sleep). Participants also completed the Patient Health Questionnaire (PHQ-9) to assess depressive symptoms and a risky decision-making task with periodic measurements of momentary happiness to quantify mood dynamics. The sentiment of written responses was evaluated by human raters (N = 470), Large Language Models (LLMs; ChatGPT 3.5 and 4.0), and the Linguistic Inquiry and Word Count (LIWC) tool. We found that language sentiment evaluated by human raters and LLMs, but not LIWC, predicted changes in depressive symptoms at a three-week follow-up. Using computational modeling, we found that language sentiment was associated with current mood, but language sentiment predicted symptom changes even after controlling for current mood. In summary, we demonstrate a scalable tool that combines brief written responses with sentiment analysis by AI tools that matches human performance in the prediction of future psychiatric symptoms.
Collapse
Affiliation(s)
- Jihyun K Hur
- Department of Psychology, Yale University, New Haven, CT 06510
| | - Joseph Heffner
- Department of Psychology, Yale University, New Haven, CT 06510
| | - Gloria W Feng
- Department of Psychology, Yale University, New Haven, CT 06510
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT 06510
| | - Robb B Rutledge
- Department of Psychology, Yale University, New Haven, CT 06510
- Department of Psychiatry, Yale University, New Haven, CT 06511
- Wu Tsai Institute, Yale University, New Haven, CT 06510
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
| |
Collapse
|
2
|
van Bronswijk SC, Howard J, Lorenzo-Luaces L. Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study. J Affect Disord 2024; 356:115-121. [PMID: 38582129 DOI: 10.1016/j.jad.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. METHODS This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). RESULTS Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LIMITATIONS Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. CONCLUSIONS Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.
Collapse
Affiliation(s)
- Suzanne Catharina van Bronswijk
- Department of Psychiatry and Psychology, Maastricht University Medical Center, Maastricht, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | | | - Lorenzo Lorenzo-Luaces
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| |
Collapse
|
3
|
Funkhouser CJ, Trivedi E, Li LY, Helgren F, Zhang E, Sritharan A, Cherner RA, Pagliaccio D, Durham K, Kyler M, Tse TC, Buchanan SN, Allen NB, Shankman SA, Auerbach RP. Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication. J Child Psychol Psychiatry 2024; 65:932-941. [PMID: 38098445 PMCID: PMC11161327 DOI: 10.1111/jcpp.13931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 06/09/2024]
Abstract
BACKGROUND Cross sectional studies have identified linguistic correlates of major depressive disorder (MDD) in smartphone communication. However, it is unclear whether monitoring these linguistic characteristics can detect when an individual is experiencing MDD, which would facilitate timely intervention. METHODS Approximately 1.2 million messages typed into smartphone social communication apps (e.g. texting, social media) were passively collected from 90 adolescents with a range of depression severity over a 12-month period. Sentiment (i.e. positive vs. negative valence of text), proportions of first-person singular pronouns (e.g. 'I'), and proportions of absolutist words (e.g. 'all') were computed for each message and converted to weekly aggregates temporally aligned with weekly MDD statuses obtained from retrospective interviews. Idiographic, multilevel logistic regression models tested whether within-person deviations in these linguistic features were associated with the probability of concurrently meeting threshold for MDD. RESULTS Using more first-person singular pronouns in smartphone communication relative to one's own average was associated with higher odds of meeting threshold for MDD in the concurrent week (OR = 1.29; p = .007). Sentiment (OR = 1.07; p = .54) and use of absolutist words (OR = 0.99; p = .90) were not related to weekly MDD. CONCLUSIONS Passively monitoring use of first-person singular pronouns in adolescents' smartphone communication may help detect MDD, providing novel opportunities for early intervention.
Collapse
Affiliation(s)
- Carter J. Funkhouser
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Esha Trivedi
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Lilian Y. Li
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Fiona Helgren
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Emily Zhang
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Aishwarya Sritharan
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Rachel A. Cherner
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - David Pagliaccio
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Katherine Durham
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Mia Kyler
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Trinity C. Tse
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | | | | | - Randy P. Auerbach
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
- Division of Clinical Developmental Neuroscience, Sackler Institute
| |
Collapse
|
4
|
Malgaroli M, Hull TD, Calderon A, Simon NM. Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention. J Affect Disord 2024; 352:133-137. [PMID: 38336165 PMCID: PMC10947071 DOI: 10.1016/j.jad.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 01/18/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Somatic Symptom and Related Disorders (SSRD), including chronic pain, result in frequent primary care visits, depression and anxiety symptoms, and diminished quality of life. Treatment access remains limited due to structural barriers and functional impairment. Digital delivery offers to improve access and enables transcript analysis via Natural Language Processing (NLP) to inform treatment. Therefore, we investigated asynchronous message-delivered SSRD treatment, and used NLP methods to identify symptom reduction markers from emotional valence. METHODS 173 individuals diagnosed with SSRD received interventions from licensed therapists via messaging 5 days/week for 8 weeks. Depression and anxiety symptoms were measured with the PHQ-9 and GAD-7 from baseline every three weeks. Symptoms trajectories were identified using unsupervised random forest clustering. Emotional valence expressed and use of emotional words were extracted from patients' de-identified transcripts, respectively using VADER and NCR Lexicon. Valence differences were examined using logistic regression. RESULTS Two subpopulations were identified showing symptoms Improvement (n = 72; 41.62 %) and non-response (n = 101; 58.38 %). Improvement patients expressed more positive valence in the first week of treatment (OR = 1.84, CI: 1.12-3.02; p = .015) and were less likely to express negative valence by the end of treatment (OR = 0.05; CI: 0.30-0.83; p = .008). Non-response patients used more negative valence words, including pain. LIMITATIONS Findings were derived from observational data obtained during an ecological intervention, without the inclusion of a control group. CONCLUSIONS NLP identified linguistic markers distinguishing changes in anxiety and depression symptoms over treatment. Digital interventions offer new forms of delivery and provide the opportunity to automatically collect data for linguistic analysis.
Collapse
Affiliation(s)
- Matteo Malgaroli
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA.
| | - Thomas D Hull
- Research and Development, Talkspace, New York, NY 10023, USA
| | - Adam Calderon
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Psychology, Pennsylvania State University, State College, PA 16801, USA
| | - Naomi M Simon
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA
| |
Collapse
|
5
|
Liu Q, Su F, Mu A, Wu X. Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory. Psychol Res Behav Manag 2024; 17:1587-1609. [PMID: 38628982 PMCID: PMC11020237 DOI: 10.2147/prbm.s450934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Purpose How individuals engage with social media can significantly impact their psychological well-being. This study examines the impact of social media interactions on mental health, grounded in the frameworks of the Elaboration Likelihood Model and Schema Activation Theory. It aims to uncover behavioral differences in information sharing between the general population and individuals with depression, while also elucidating the psychological mechanisms underlying these disparities. Methods A pre-experiment (N=30) and three experiments (Experiment 1a N=200, Experiment 1b N=180, Experiment 2 N=128) were executed online. These experiments investigated the joint effects of information quality, content valence, self-referential processing, and depression level on the intention to share information. The research design incorporated within-subject and between-subject methods, utilizing SPSS and SPSS Process to conduct independent sample t-tests, two-factor ANOVA analyses, mediation analyses, and moderated mediation analyses to test our hypotheses. Results Information quality and content valence significantly influence sharing intention. In scenarios involving low-quality information, individuals with depression are more inclined to share negative emotional content compared to the general population, and this tendency intensifies with the severity of depression. Moreover, self-referential processing acts as a mediator between emotional content and intention to share, yet this mediation effect weakens as the severity of depression rises. Conclusion Our study highlights the importance of promoting viewpoint diversity and breaking the echo chamber effect in social media to improve the mental health of individuals with depression. To achieve this goal, tailoring emotional content on social media could be a practical starting point for practice.
Collapse
Affiliation(s)
- Qiang Liu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China
| | - FeiFei Su
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China
| | - Aruhan Mu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China
| | - Xiang Wu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China
- Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, 650221, People’s Republic of China
| |
Collapse
|
6
|
Rutter LA, ten Thij M, Lorenzo-Luaces L, Valdez D, Bollen J. Negative affect variability differs between anxiety and depression on social media. PLoS One 2024; 19:e0272107. [PMID: 38381769 PMCID: PMC10881019 DOI: 10.1371/journal.pone.0272107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/23/2023] [Indexed: 02/23/2024] Open
Abstract
OBJECTIVE Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications. METHOD Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content. RESULTS Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. LIMITATIONS Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect. CONCLUSIONS Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.
Collapse
Affiliation(s)
- Lauren A. Rutter
- Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States of America
| | - Marijn ten Thij
- Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, NL, United States of America
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States of America
| | - Lorenzo Lorenzo-Luaces
- Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States of America
| | - Danny Valdez
- Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
- Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States of America
| | - Johan Bollen
- Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States of America
| |
Collapse
|
7
|
Zeng J, Xie Z, Chen L, Peng X, Luan F, Hu J, Xie H, Liu R, Zeng N. Rosmarinic acid alleviate CORT-induced depressive-like behavior by promoting neurogenesis and regulating BDNF/TrkB/PI3K signaling axis. Biomed Pharmacother 2024; 170:115994. [PMID: 38070249 DOI: 10.1016/j.biopha.2023.115994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/25/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Rosmarinic acid (RA), a natural phenolic acid compound with a variety of bioactive properties. However, the antidepressant activity and mechanism of RA remain unclear. The aim of this study is to investigate the effects and potential mechanisms of RA on chronic CORT injection induced depression-like behavior in mice. Male C57BL/6 J mice were intraperitoneally injected with CORT (10 mg/kg) and were orally given RA daily (10 or 20 mg/kg) for 21 consecutive days. In vitro, the HT22 cells were exposed to CORT (200 μM) with RA (12.5, 25 or 50 μM) and LY294002 (a PI3K inhibitor) or ANA-12 (a TrkB inhibitor) treatment. The depression-like behavior and various neurobiological changes in the mice and cell injury and levels of target proteins in vitro were subsequently assessed. Here, RA treatment decreased the expression of p-GR/GR, HSP90, FKBP51, SGK-1 in mice hippocampi. Besides, RA increased the average optical density of Nissl bodies and number of dendritic spines in CA3 region, and enhanced Brdu and DCX expression and synaptic transduction in DG region, as well as up-regulated both the BDNF/TrkB/CREB and PI3K/Akt/mTOR signaling. Moreover, RA reduced structural damage and apoptosis in HT22 cells, increased the differentiation and maturation of them. More importantly, LY294002, but not ANA-12, reversed the effect of RA on GR nuclear translocation. Taken together, RA exerted antidepressant activities by modulating the hippocampal glucocorticoid signaling and hippocampal neurogenesis, which related to the BDNF/TrkB/PI3K signaling axis regulating GR nuclear translocation, provide evidence for the application of RA as a candidate for depression.
Collapse
Affiliation(s)
- Jiuseng Zeng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Zhiqiang Xie
- Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Li Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Department of Pharmacy, Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College, Chengdu 610500, China
| | - Xi Peng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Fei Luan
- School of Pharmacy, The Key Laboratory of Basic and New Drug Research of Traditional Chinese Medicine, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China
| | - Jingwen Hu
- Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Hongxiao Xie
- Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Rong Liu
- Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Nan Zeng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Department of Pharmacology, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| |
Collapse
|
8
|
Ge Y, Xiao Y, Li M, Yang L, Song P, Li X, Yan H. Maladaptive cognitive regulation moderates the mediating role of emotion dysregulation on the association between psychosocial factors and non-suicidal self-injury in depression. Front Psychiatry 2023; 14:1279108. [PMID: 38098637 PMCID: PMC10719840 DOI: 10.3389/fpsyt.2023.1279108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/10/2023] [Indexed: 12/17/2023] Open
Abstract
Introduction Non-suicidal self-injury (NSSI) is highly prevalent in depression, and is associated with psychosocial factors, emotion dysregulation, and strategies of cognitive emotion regulation. However, the internal combination and interactions of these risk factors in depression remain unclear. Methods Data from 122 patients with depression, including 56 with NSSI and 66 without NSSI, were analyzed. Self-rating scales were used to assess psychosocial factors, emotion dysregulation, and cognitive regulation strategies. Sparse partial least squares discriminant analysis (sPLS-DA) was employed to explore internal combinations in each profile. A moderated mediation model was applied to examine their interactional relationship. Results The results identified an NSSI-related psychosocial profile characterized by high neuroticism, childhood trauma, poor family functioning, and low psychological resilience. Emotion dysregulation, including high levels of alexithymia, anhedonia, and emotion regulation difficulties, mediated the association between this psychosocial profile and NSSI. The mediated effect was further moderated by maladaptive cognitive regulation strategies. Limitations Lack of sufficient information on NSSI frequency and severity. Relatively small sample size for discussing the impact of gender and age of depressive patients with NSSI. Conclusion These findings hold important implications for the prevention, treatment, and rehabilitation of NSSI.
Collapse
Affiliation(s)
- Yuqi Ge
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yang Xiao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Mingzhu Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Lei Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Peihua Song
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xueni Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| |
Collapse
|
9
|
Cheng T, Han B, Liu Y. Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data. Front Public Health 2023; 11:1193750. [PMID: 37663835 PMCID: PMC10470640 DOI: 10.3389/fpubh.2023.1193750] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023] Open
Abstract
Objectives Vaccination is widely regarded as the paramount approach for safeguarding individuals against the repercussions of COVID-19. Nonetheless, concerns surrounding the efficacy and potential adverse effects of these vaccines have become prevalent among the public. To date, there has been a paucity of research investigating public perceptions and the adoption of COVID-19 vaccines. Therefore, the present study endeavours to address this lacuna by undertaking a spatiotemporal analysis of sentiments towards vaccination and its uptake in England at the local authority level, while concurrently examining the sociodemographic attributes at the national level. Methods A sentiment analysis of Twitter data was undertaken to delineate the distribution of positive sentiments and their demographic correlates. Positive sentiments were categorized into clusters to streamline comparison across different age and gender demographics. The relationship between positive sentiment and vaccination uptake was evaluated using Spearman's correlation coefficient. Additionally, a bivariate analysis was carried out to further probe public sentiment towards COVID-19 vaccines and their local adoption rates. Result The results indicated that the majority of positive tweets were posted by males, although females expressed higher levels of positive sentiment. The age group over 40 dominated the positive tweets and exhibited the highest sentiment polarity. Additionally, vaccination uptake was positively correlated with the number of positive tweets and the age group at the local authority level. Conclusion Overall, public opinions on COVID-19 vaccines are predominantly positive. The number of individuals receiving vaccinations at the local authority level is positively correlated with the prevalence of positive attitudes towards vaccines, particularly among the population aged over 40. These findings suggest that targeted efforts to increase vaccination uptake among younger populations, particularly males, are necessary to achieve widespread vaccination coverage.
Collapse
Affiliation(s)
- Tao Cheng
- SpaceTimeLab, University College London, Civil, Environmental and Geomatic Engineering, London, United Kingdom
| | | | | |
Collapse
|
10
|
Mayor E, Bietti LM, Canales-Rodríguez EJ. Text as signal. A tutorial with case studies focusing on social media (Twitter). Behav Res Methods 2023; 55:2595-2620. [PMID: 35879505 PMCID: PMC9311346 DOI: 10.3758/s13428-022-01917-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/16/2022]
Abstract
Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.
Collapse
Affiliation(s)
- Eric Mayor
- Department of Psychology, Division of Clinical Psychology and Epidemiology, University of Basel, Basel, Switzerland.
| | - Lucas M Bietti
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | | |
Collapse
|
11
|
Parker MA, Valdez D, Rao VK, Eddens KS, Agley J. Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses. J Med Internet Res 2023; 25:e48405. [PMID: 37505795 PMCID: PMC10422173 DOI: 10.2196/48405] [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: 04/21/2023] [Revised: 06/01/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. OBJECTIVE This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. METHODS This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. RESULTS We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. CONCLUSIONS Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.
Collapse
Affiliation(s)
- Maria A Parker
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Danny Valdez
- Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Varun K Rao
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
- Department of Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Katherine S Eddens
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Jon Agley
- Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| |
Collapse
|
12
|
Shearrer GE. The Interaction of Glycemia with Anxiety and Depression Is Related to Altered Cerebellar and Cerebral Functional Correlations. Brain Sci 2023; 13:1086. [PMID: 37509016 PMCID: PMC10377615 DOI: 10.3390/brainsci13071086] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Depression, type 2 diabetes (T2D), and obesity are comorbid, and prevention and treatment of all three diseases are needed. We hypothesized an inverse relationship between the connectivity of the cingulo-opercular task control network with the somatosensory mouth network and the interaction between HbA1c and depression. Three-hundred and twenty-five participants (BMI: 26.11 ± 0.29; Achenbach adult self-report (ASR) DSM depressive problems T-score (depression): 54.60 ± 6.77; Age: 28.26 ± 3.90 y; adult self-report anxiety and depression scale (anxiety and depression): 54.69 ± 7.27; HbA1c: 5.26 ± 0.29; 68% white) were sampled from the Human Connectome Project 1200 subjects PTN release. Inclusion criteria were: four (15 min) resting state fMRI scans; BMI; hemoglobin A1c (HbA1c); and complete adult self-report data. The following models were run to assess the connectivity between 15 independent fMRI components: the interaction of depression with HbA1c; anxiety and depression with HbA1c; depression with BMI; and anxiety and depression with BMI. All models were corrected for a reported number of depressive symptoms, head motion in the scanner, age, and race. Functional connectivity was modeled in FSLNets. Corrected significance was set at pFWE < 0.05. The interaction HbA1c and anxiety and depression was positively related to the connectivity of the cerebellum with the visual network (t = 3.76, pFWE = 0.008), frontoparietal network (t = 3.45, pFWE = 0.02), and somatosensory mouth network (t = 4.29, pFWE = 0.0004). Although our hypotheses were not supported, similar increases in cerebellar connectivity are seen in patients with T2D and overall suggest that the increased cerebellar connectivity may be compensatory for an increasingly poor glycemic control.
Collapse
Affiliation(s)
- Grace E Shearrer
- Department of Family and Consumer Sciences, Neuroscience Program, School of Computing, College of Agriculture, Life Sciences and Natural Resources, University of Wyoming, Laramie, WY 82070, USA
| |
Collapse
|
13
|
Wang B, Zhao Y, Lu X, Qin B. Cognitive distortion based explainable depression detection and analysis technologies for the adolescent internet users on social media. Front Public Health 2023; 10:1045777. [PMID: 36733285 PMCID: PMC9886894 DOI: 10.3389/fpubh.2022.1045777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 12/23/2022] [Indexed: 01/19/2023] Open
Abstract
Nowadays, adolescents would like to share their daily lives via social media platforms, which presents an excellent opportunity for us to leverage these data to develop techniques to measure their mental health status, such as depression. Previous researches focus on the more accurate detection of depression through statistical learning and ignore psychological understanding of depression. However, psychologists have given lots of theoretical evidence for depression. Such as according to cognitive psychology research, cognitive distortions will result in depression. Thus, in this study, we propose a new task, explainable depression detection, to not only automatically detect depression but also try to give clues to depression based on cognitive distortion theory. For this purpose, we construct a multi-task learning model based on a pre-trained model to detect depression and identify cognitive distortion. And we use many analytical means including word clouds for data analysis to draw our conclusion. Previous social media users' depression corpus and our cognitive distortion corpus are utilized for analysis and experiment. Our experimental results outperform the baseline results and interesting conclusions about adolescent depression are drawn.
Collapse
|
14
|
Rutter LA, Howard J, Lakhan P, Valdez D, Bollen J, Lorenzo-Luaces L. "I Haven't Been Diagnosed, but I Should Be"-Insight Into Self-diagnoses of Common Mental Health Disorders: Cross-sectional Study. JMIR Form Res 2023; 7:e39206. [PMID: 36637885 PMCID: PMC9883736 DOI: 10.2196/39206] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In recent years, social media has become a rich source of mental health data. However, there is a lack of web-based research on the accuracy and validity of self-reported diagnostic information available on the web. OBJECTIVE An analysis of the degree of correspondence between self-reported diagnoses and clinical indicators will afford researchers and clinicians higher levels of trust in social media analyses. We hypothesized that self-reported diagnoses would correspond to validated disorder-specific severity questionnaires across 2 large web-based samples. METHODS The participants of study 1 were 1123 adults from a national Qualtrics panel (mean age 34.65, SD 12.56 years; n=635, 56.65% female participants,). The participants of study 2 were 2237 college students from a large university in the Midwest (mean age 19.08, SD 2.75 years; n=1761, 75.35% female participants). All participants completed a web-based survey on their mental health, social media use, and demographic information. Additionally, the participants reported whether they had ever been diagnosed with a series of disorders, with the option of selecting "Yes"; "No, but I should be"; "I don't know"; or "No" for each condition. We conducted a series of ANOVA tests to determine whether there were differences among the 4 diagnostic groups and used post hoc Tukey tests to examine the nature of the differences. RESULTS In study 1, for self-reported mania (F3,1097=2.75; P=.04), somatic symptom disorder (F3,1060=26.75; P<.001), and alcohol use disorder (F3,1097=77.73; P<.001), the pattern of mean differences did not suggest that the individuals were accurate in their self-diagnoses. In study 2, for all disorders but bipolar disorder (F3,659=1.43; P=.23), ANOVA results were consistent with our expectations. Across both studies and for most conditions assessed, the individuals who said that they had been diagnosed with a disorder had the highest severity scores on self-report questionnaires, but this was closely followed by individuals who had not been diagnosed but believed that they should be diagnosed. This was especially true for depression, generalized anxiety, and insomnia. For mania and bipolar disorder, the questionnaire scores did not differentiate individuals who had been diagnosed from those who had not. CONCLUSIONS In general, if an individual believes that they should be diagnosed with an internalizing disorder, they are experiencing a degree of psychopathology similar to those who have already been diagnosed. Self-reported diagnoses correspond well with symptom severity on a continuum and can be trusted as clinical indicators, especially in common internalizing disorders such as depression and generalized anxiety disorder. Researchers can put more faith into patient self-reports, including those in web-based experiments such as social media posts, when individuals report diagnoses of depression and anxiety disorders. However, replication and further study are recommended.
Collapse
Affiliation(s)
- Lauren A Rutter
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Jacqueline Howard
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Prabhvir Lakhan
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Danny Valdez
- Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Johan Bollen
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Lorenzo Lorenzo-Luaces
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| |
Collapse
|
15
|
Isch C, ten Thij M, Todd PM, Bollen J. Quantifying changes in societal optimism from online sentiment. Behav Res Methods 2023; 55:176-184. [PMID: 35318589 PMCID: PMC8939395 DOI: 10.3758/s13428-021-01785-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2021] [Indexed: 11/17/2022]
Abstract
Individuals can hold contrasting views about distinct times: for example, dread over tomorrow's appointment and excitement about next summer's vacation. Yet, psychological measures of optimism often assess only one time point or ask participants to generalize about their future. Here, we address these limitations by developing the optimism curve, a measure of societal optimism that compares positivity toward different future times that was inspired by the Treasury bond yield curve. By performing sentiment analysis on over 3.5 million tweets that reference 23 future time points (2 days to 30 years), we measured how positivity differs across short-, medium-, and longer-term future references. We found a consistent negative association between positivity and the distance into the future referenced: From August 2017 to February 2020, the long-term future was discussed less positively than the short-term future. During the COVID-19 pandemic, this relationship inverted, indicating declining near-future- but stable distant-future-optimism. Our results demonstrate that individuals hold differentiated attitudes toward the near and distant future that shift in aggregate over time in response to external events. The optimism curve uniquely captures these shifting attitudes and may serve as a useful tool that can expand existing psychometric measures of optimism.
Collapse
Affiliation(s)
- Calvin Isch
- Cognitive Science Program, Indiana University Bloomington, 1001 E. 10th St., Bloomington, IN 47405 USA
| | - Marijn ten Thij
- Center for Social and Biomedical Complexity, Indiana University Bloomington, 1015 E. 11th St., Bloomington, IN 47408 USA
- Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands
- Department of Data Science and Knowledge Engineering, Maastricht University, Paul-Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Peter M. Todd
- Cognitive Science Program, Indiana University Bloomington, 1001 E. 10th St., Bloomington, IN 47405 USA
| | - Johan Bollen
- Cognitive Science Program, Indiana University Bloomington, 1001 E. 10th St., Bloomington, IN 47405 USA
- Center for Social and Biomedical Complexity, Indiana University Bloomington, 1015 E. 11th St., Bloomington, IN 47408 USA
| |
Collapse
|
16
|
Edinger A, Valdez D, Walsh-Buhi E, Bollen J. Deep learning for topical trend discovery in online discourse about Pre-Exposure Prophylaxis (PrEP). AIDS Behav 2023; 27:443-453. [PMID: 35916950 PMCID: PMC9344253 DOI: 10.1007/s10461-022-03779-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2022] [Indexed: 11/16/2022]
Abstract
Pre-Exposure Prophylaxis (PrEP) interventions are increasingly prevalent on social media. These data can be mined for insights about PrEP that may not be as apparent in surveys including personal musings about PrEP and barriers/facilitators to PrEP uptake. This study explores online discourse about PrEP using an interdisciplinary public health and computational informatics approach. We collected (N = 4,020) tweets using Twitter's Application Programming Interface (API). These data underwent a three-step neural network/deep learning process to identify clusters within these tweets and relative similarity/dissimilarity between clusters. We identified 25 distinct clusters from our original collection of tweets. These clusters represent general information about PrEP, how PrEP is communicated among diverse groups, and potential pockets of misinformation and disinformation regarding PrEP. Specific clusters of interest include discussions of medication side effects, social perception of PrEP usage, and concerns with costs and barriers to access of PrEP interventions. Our approach revealed diverse ways PrEP is contextualized online. Importantly this information can be leveraged to identify points of possible intervention for disinformation and misinformation about PrEP.
Collapse
Affiliation(s)
- Andy Edinger
- grid.411377.70000 0001 0790 959XDepartment of Applied Health Science, Indiana University School of Public Health, 47405 Bloomington, IN USA
| | - Danny Valdez
- Luddy School of Informatics and Computer Engineering, Indiana University, 47405, Bloomington, IN, USA.
| | - Eric Walsh-Buhi
- grid.411377.70000 0001 0790 959XDepartment of Applied Health Science, Indiana University School of Public Health, 47405 Bloomington, IN USA
| | - Johan Bollen
- grid.411377.70000 0001 0790 959XLuddy School of Informatics and Computer Engineering, Indiana University, 47405 Bloomington, IN USA ,grid.411377.70000 0001 0790 959XDepartment of Psychological and Brain Sciences, Indiana University, 47405 Bloomington, IN USA
| |
Collapse
|
17
|
Wu T, Liu R, Zhang L, Rifky M, Sui W, Zhu Q, Zhang J, Yin J, Zhang M. Dietary intervention in depression - a review. Food Funct 2022; 13:12475-12486. [PMID: 36408608 DOI: 10.1039/d2fo02795j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Depression is a mental illness that affects the normal lives of over 300 million people. Unfortunately, about 30% to 40% of patients do not adequately respond to pharmacotherapy and other therapies. This review focuses on exploring the relationship between dietary nutrition and depression, aiming to find safer and efficient ingredients to alleviate depression. Diet can affect depression in numerous ways. These pathways include the regulation of tryptophan metabolism, inflammation, hypothalamic-pituitary-adrenal (HPA) axis, microbe-gut-brain axis, brain-derived neurotrophic factor (BDNF) and epigenetics. Furthermore, probiotics, micronutrients, and other active substances exhibit significant antidepressant effects by regulating the above pathways. These provide insights for developing antidepressant foods.
Collapse
Affiliation(s)
- Tao Wu
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Ran Liu
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Ling Zhang
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Mohamed Rifky
- Eastern University of Sri Lanka, Chenkalady 999011, Sri Lanka
| | - Wenjie Sui
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Qiaomei Zhu
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Jiaojiao Zhang
- Department of Clinical Sciences, Faculty of Medicine, Università Politecnica delle Marche, Ancona 60100, Italy
| | - Jinjin Yin
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.
| | - Min Zhang
- State Key Laboratory of Food Nutrition and Safety, Food Biotechnology Engineering Research Center of Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China. .,Tianjin Agricultural University, and China-Russia Agricultural Processing Joint Laboratory, Tianjin Agricultural University, Tianjin 300392, China.
| |
Collapse
|
18
|
Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
Collapse
Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
| |
Collapse
|
19
|
Cao XJ, Liu XQ. Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World J Psychiatry 2022; 12:1287-1297. [PMID: 36389087 PMCID: PMC9641379 DOI: 10.5498/wjp.v12.i10.1287] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/09/2022] [Accepted: 09/22/2022] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence-based technologies are gradually being applied to psych-iatric research and practice. This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents. In terms of the practice of psychosis risk screening, the application of two artificial intelligence-assisted screening methods, chatbot and large-scale social media data analysis, is summarized in detail. Regarding the challenges of psychiatric risk screening, ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence, which must comply with the four biomedical ethical principles of respect for autonomy, nonmaleficence, beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings. By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens, we propose that assuming they meet ethical requirements, there are three directions worth considering in the future development of artificial intelligence-assisted psychosis risk screening in adolescents as follows: nonperceptual real-time artificial intelligence-assisted screening, further reducing the cost of artificial intelligence-assisted screening, and improving the ease of use of artificial intelligence-assisted screening techniques and tools.
Collapse
Affiliation(s)
- Xiao-Jie Cao
- Graduate School of Education, Peking University, Beijing 100871, China
| | - Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| |
Collapse
|
20
|
Valdez D, Jozkowski KN, Haus K, Ten Thij M, Crawford BL, Montenegro MS, Lo WJ, Turner RC, Bollen J. Assessing rigid modes of thinking in self-declared abortion ideology: natural language processing insights from an online pilot qualitative study on abortion attitudes. Pilot Feasibility Stud 2022; 8:127. [PMID: 35710466 PMCID: PMC9200936 DOI: 10.1186/s40814-022-01078-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 05/26/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction Although much work has been done on US abortion ideology, less is known relative to the psychological processes that distinguish personal abortion beliefs or how those beliefs are communicated to others. As part of a forthcoming probability-based sampling designed study on US abortion climate, we piloted a study with a controlled sample to determine whether psychological indicators guiding abortion beliefs can be meaningfully extracted from qualitative interviews using natural language processing (NLP) substring matching. Of particular interest to this study is the presence of cognitive distortions—markers of rigid thinking—spoken during interviews and how cognitive distortion frequency may be tied to rigid, or firm, abortion beliefs. Methods We ran qualitative interview transcripts against two lexicons. The first lexicon, the cognitive distortion schemata (CDS), was applied to identify cognitive distortion n-grams (a series of words) embedded within the qualitative interviews. The second lexicon, the Linguistic Inquiry Word Count (LIWC), was applied to extract other psychological indicators, including the degrees of (1) analytic thinking, (2) emotional reasoning, (3) authenticity, and (4) clout. Results People with polarized abortion views (i.e., strongly supportive of or opposed to abortion) had the highest observed usage of CDS n-grams, scored highest on authenticity, and lowest on analytic thinking. By contrast, people with moderate or uncertain abortion views (i.e., people holding more complex or nuanced views of abortion) spoke with the least CDS n-grams and scored slightly higher on analytic thinking. Discussion and conclusion Our findings suggest people communicate about abortion differently depending on their personal abortion ideology. Those with strong abortion views may be more likely to communicate with authoritative words and patterns of words indicative of cognitive distortions—or limited complexity in belief systems. Those with moderate views are more likely to speak in conflicting terms and patterns of words that are flexible and open to change—or high complexity in belief systems. These findings suggest it is possible to extract psychological indicators with NLP from qualitative interviews about abortion. Findings from this study will help refine our protocol ahead of full-study launch.
Collapse
Affiliation(s)
- Danny Valdez
- Indiana University School of Public Health, 1025 E 7th Street, Bloomington, IN, 47405, USA
| | - Kristen N Jozkowski
- Indiana University School of Public Health, 1025 E 7th Street, Bloomington, IN, 47405, USA.
| | - Katherine Haus
- Indiana University School of Public Health, 1025 E 7th Street, Bloomington, IN, 47405, USA
| | - Marijn Ten Thij
- Department of Data Science and Knowledge Engineering, Universiteit Maastricht, P.O. Box 616, 6200 MD, Maastricht, Netherlands
| | - Brandon L Crawford
- Indiana University School of Public Health, 1025 E 7th Street, Bloomington, IN, 47405, USA
| | - María S Montenegro
- Indiana University College of Arts and Sciences, 107 S Indiana Ave, Bloomington, IN, 47405, USA
| | - Wen-Juo Lo
- University of Arkansas, 1 University of Arkansas, Fayetteville, AR, 72701, USA
| | - Ronna C Turner
- University of Arkansas, 1 University of Arkansas, Fayetteville, AR, 72701, USA
| | - Johan Bollen
- Luddy School of Informatics, Computing and Engineering, 919 E. 10th St., Bloomington, IN, 47408, USA
| |
Collapse
|
21
|
Mental disorders on online social media through the lens of language and behaviour: Analysis and visualisation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
22
|
Jun WH, Lee G. The significant mediators between depression and mental health recovery among community-dwelling people with a diagnosed mental disorder. Arch Psychiatr Nurs 2022; 37:18-24. [PMID: 35337434 DOI: 10.1016/j.apnu.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/13/2021] [Accepted: 11/17/2021] [Indexed: 11/29/2022]
Abstract
To investigate the mediating roles of community integration and positive thinking on the relationship between depression and mental health recovery among community dwelling people with mental disorders in South Korea. A cross-sectional, descriptive study was utilized. Data were collected from 167 community-dwelling people with mental disorders who did not use the day program at community mental health centers. Data were collected from November 20, 2018, to February 15, 2019. Both community integration and positive thinking were found to mediate the effects of depression on mental health recovery. The mediating effect of positive thinking was significantly greater than that of community integration. This study added evidence for a significant multiple mediating effect of community integration and positive thinking on the relationship between depression and mental health recovery in community-dwelling people with mental disorders. Although the consumer-centered recovery paradigm of people with mental health difficulties is of global importance, little research has been conducted on mental health recovery among community-dwelling people with mental disorders who do not use the day program at community mental health centers. It was found that community integration and positive thinking mediated the effects of depression on mental health recovery, with positive thinking mediating this relationship the most. Thus, these results suggest a specific direction of community mental health services to promote mental health recovery for people with mental disorders who do not have access to community mental health services.
Collapse
Affiliation(s)
- Won Hee Jun
- College of Nursing, Keimyung University, Daegu, South Korea
| | - Gyungjoo Lee
- The Catholic University of Korea, College of Nursing, 222 Banpo-daero, Seocho-gu, Seoul, South Korea.
| |
Collapse
|
23
|
Stupinski AM, Alshaabi T, Arnold MV, Adams JL, Minot JR, Price M, Dodds PS, Danforth CM. Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study. JMIR Ment Health 2022; 9:e33685. [PMID: 35353049 PMCID: PMC9008521 DOI: 10.2196/33685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 12/14/2021] [Accepted: 12/26/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. OBJECTIVE By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. METHODS We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. RESULTS We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. CONCLUSIONS These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.
Collapse
Affiliation(s)
- Anne Marie Stupinski
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Thayer Alshaabi
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Advanced Bioimaging Center, University of California, Berkeley, CA, United States
| | - Michael V Arnold
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Jane Lydia Adams
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Data Visualization Lab, Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
| | - Joshua R Minot
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Matthew Price
- Department of Psychological Science, University of Vermont, Burlington, VT, United States
| | - Peter Sheridan Dodds
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Department of Computer Science, University of Vermont, Burlington, VT, United States
| | - Christopher M Danforth
- Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, United States
| |
Collapse
|
24
|
Wood IB, Brattig Correia R, Miller WR, Rocha LM. Small cohort of patients with epilepsy showed increased activity on Facebook before sudden unexpected death. Epilepsy Behav 2022; 128:108580. [PMID: 35151186 PMCID: PMC10582639 DOI: 10.1016/j.yebeh.2022.108580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/09/2021] [Accepted: 01/16/2022] [Indexed: 11/03/2022]
Abstract
Sudden Unexpected Death in Epilepsy (SUDEP) remains a leading cause of death in people with epilepsy. Despite the constant risk for patients and bereavement to family members, to date the physiological mechanisms of SUDEP remain unknown. Here we explore the potential to identify putative predictive signals of SUDEP from online digital behavioral data using text and sentiment analysis tools. Specifically, we analyze Facebook timelines of six patients with epilepsy deceased due to SUDEP, donated by surviving family members. We find preliminary evidence for behavioral changes detectable by text and sentiment analysis tools. Namely, in the months preceding their SUDEP event patient social media timelines show: i) increase in verbosity; ii) increased use of functional words; and iii) sentiment shifts as measured by different sentiment analysis tools. Combined, these results suggest that social media engagement, as well as its sentiment, may serve as possible early-warning signals for SUDEP in people with epilepsy. While the small sample of patient timelines analyzed in this study prevents generalization, our preliminary investigation demonstrates the potential of social media data as complementary data in larger studies of SUDEP and epilepsy.
Collapse
Affiliation(s)
- Ian B Wood
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Rion Brattig Correia
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal; CAPES Foundation, Ministry of Education of Brazil, Brasília, DF, Brazil; Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Wendy R Miller
- School of Nursing, Indiana University, Indianapolis, IN 46202, USA.
| | - Luis M Rocha
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA; Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47408, USA; Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal.
| |
Collapse
|
25
|
Kelley SW, Gillan CM. Using language in social media posts to study the network dynamics of depression longitudinally. Nat Commun 2022; 13:870. [PMID: 35169166 PMCID: PMC8847554 DOI: 10.1038/s41467-022-28513-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.
Collapse
Affiliation(s)
- Sean W Kelley
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
| |
Collapse
|
26
|
|
27
|
Alshaabi T, Van Oort CM, Fudolig MI, Arnold MV, Danforth CM, Dodds PS. Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning. Front Artif Intell 2022; 4:783778. [PMID: 35141518 PMCID: PMC8819185 DOI: 10.3389/frai.2021.783778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 12/20/2021] [Indexed: 11/19/2022] Open
Abstract
Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.
Collapse
Affiliation(s)
- Thayer Alshaabi
- Advanced Bioimaging Center, University of California, Berkeley, Berkeley, CA, United States
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Colin M. Van Oort
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- The MITRE Corporation, McLean, VA, United States
| | - Mikaela Irene Fudolig
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Michael V. Arnold
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Christopher M. Danforth
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Department of Mathematics & Statistics, University of Vermont, Burlington, VT, United States
| | - Peter Sheridan Dodds
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
- Department of Computer Science, University of Vermont, Burlington, VT, United States
| |
Collapse
|
28
|
Goldstone RL. Performance, Well-Being, Motivation, and Identity in an Age of Abundant Data: Introduction to the “Well-Measured Life” Special Issue of Current Directions in Psychological Science. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2022. [DOI: 10.1177/09637214211053834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Our lives are being measured in rapidly increasing ways and frequency. These measurements have beneficial and deleterious effects at both individual and social levels. Behavioral measurement technologies offer the promise of helping us to know ourselves better and to improve our well-being by using personalized feedback and gamification. At the same time, they present threats to our privacy, self-esteem, and motivation. At the societal level, the potential benefits of reducing bias and decision variability by using objective and transparent assessments are offset by threats of systematic, algorithmic bias from invalid or flawed measurements. Considerable technological progress, careful foresight, and continuous scrutiny will be needed so that the positive impacts of behavioral measurement technologies far outweigh the negative ones.
Collapse
Affiliation(s)
- Robert L. Goldstone
- Program in Cognitive Science and Department of Psychological and Brain Sciences, Indiana University
| |
Collapse
|
29
|
Valdez D, Goodson P. Neutral or Framed? A Sentiment Analysis of 2019 Abortion Laws. SEXUALITY RESEARCH & SOCIAL POLICY : JOURNAL OF NSRC : SR & SP 2022; 19:936-945. [PMID: 35069923 PMCID: PMC8764246 DOI: 10.1007/s13178-022-00690-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION This study employs sentiment analysis (SA) to examine the semantic structures of restrictive and protective abortion bills enacted in 2019. SA is a Natural Language Processing (NLP) technique that uses automation to extract affective indicators (emotive language) from text data. Assessing these indicators can help identify whether legal texts are framed, or intentionally biased in their wording. Identifying framing is important for understanding potentially biased interpretations of these laws. METHODS We identified a sample of 2019 abortion bills using the legislative tracking tool Legiscan and included those that met specified criteria (N = 19 bills). We categorized each bill as restrictive (n = 12) or protective (n = 7). We ran aggregate (i.e., all bills) and separate (protective × restrictive) SA, generating scores that we interpreted qualitatively (higher scores indicated predominance of positive wording). RESULTS In the aggregate analysis, 56% of text comprised negative terms (44% positive). Restrictive bills contained more negative language than protective bills (67% vs 58%). Although SA scores varied from -222 to +13, two laws scored 0, indicating neutrality. For comparison, the US Constitution's score equaled 1. CONCLUSION Our findings confirm SA is useful to examine legal documents for language biases. The abortion bills we assessed seem framed along political ideologies, although the sample provided evidence that neutral wording is possible. POLICY IMPLICATIONS With the recent additions of conservative-leaning Justices to the US Supreme Court, Roe v. Wade is again at the center of partisan conflict. Thus, how abortion laws are framed draws further implications for how they may be interpreted when challenged in the court system.
Collapse
Affiliation(s)
- Danny Valdez
- Department of Applied Health Science, Indiana University School of Public Health, 1025 E 7th St #111, Bloomington, IN 47405 USA
| | - Patricia Goodson
- Department of Health & Kinesiology, Texas A&M University, 2929 Research Parkway, College Station, TX 77845 USA
| |
Collapse
|
30
|
Zogan H, Razzak I, Wang X, Jameel S, Xu G. Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. WORLD WIDE WEB 2022; 25:281-304. [PMID: 35106059 PMCID: PMC8795347 DOI: 10.1007/s11280-021-00992-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 05/13/2023]
Abstract
The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
Collapse
Affiliation(s)
- Hamad Zogan
- University of Technology Sydney (UTS), Sydney, Australia
- Jazan University, Jazan, Saudi Arabia
| | | | - Xianzhi Wang
- University of Technology Sydney (UTS), Sydney, Australia
| | | | - Guandong Xu
- University of Technology Sydney (UTS), Sydney, Australia
| |
Collapse
|
31
|
Reply to Schmidt et al.: A robust surge of cognitive distortions in historical language. Proc Natl Acad Sci U S A 2021; 118:2115842118. [PMID: 34725168 PMCID: PMC8609227 DOI: 10.1073/pnas.2115842118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/12/2021] [Indexed: 11/18/2022] Open
|
32
|
Historical language records reveal a surge of cognitive distortions in recent decades. Proc Natl Acad Sci U S A 2021; 118:2102061118. [PMID: 34301899 PMCID: PMC8325314 DOI: 10.1073/pnas.2102061118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Can entire societies become more or less depressed over time? Here, we look for the historical traces of cognitive distortions, thinking patterns that are strongly associated with internalizing disorders such as depression and anxiety, in millions of books published over the course of the last two centuries in English, Spanish, and German. We find a pronounced “hockey stick” pattern: Over the past two decades the textual analogs of cognitive distortions surged well above historical levels, including those of World War I and II, after declining or stabilizing for most of the 20th century. Our results point to the possibility that recent socioeconomic changes, new technology, and social media are associated with a surge of cognitive distortions. Individuals with depression are prone to maladaptive patterns of thinking, known as cognitive distortions, whereby they think about themselves, the world, and the future in overly negative and inaccurate ways. These distortions are associated with marked changes in an individual’s mood, behavior, and language. We hypothesize that societies can undergo similar changes in their collective psychology that are reflected in historical records of language use. Here, we investigate the prevalence of textual markers of cognitive distortions in over 14 million books for the past 125 y and observe a surge of their prevalence since the 1980s, to levels exceeding those of the Great Depression and both World Wars. This pattern does not seem to be driven by changes in word meaning, publishing and writing standards, or the Google Books sample. Our results suggest a recent societal shift toward language associated with cognitive distortions and internalizing disorders.
Collapse
|
33
|
Bathina KC, ten Thij M, Valdez D, Rutter LA, Bollen J. Declining well-being during the COVID-19 pandemic reveals US social inequities. PLoS One 2021; 16:e0254114. [PMID: 34237087 PMCID: PMC8266050 DOI: 10.1371/journal.pone.0254114] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/20/2021] [Indexed: 12/23/2022] Open
Abstract
Background The COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups. Purpose This study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas. Methods We used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities. Results Longitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001). Discussion Our results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19. Conclusion The COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.
Collapse
Affiliation(s)
- Krishna C. Bathina
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America
| | - Marijn ten Thij
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America
- Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands
| | - Danny Valdez
- School of Public Health, Indiana University, Bloomington, IN, United States of America
- * E-mail:
| | - Lauren A. Rutter
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
| | - Johan Bollen
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America
| |
Collapse
|
34
|
Dozois DJA. Depressive cognition on Twitter. Nat Hum Behav 2021; 5:414-415. [PMID: 33574603 DOI: 10.1038/s41562-021-01054-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- David J A Dozois
- University of Western Ontario Psychology, Westminster Hall, London, Ontario, Canada.
| |
Collapse
|