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Tudehope L, Harris N, Vorage L, Sofija E. What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychol 2024; 12:105. [PMID: 38424653 PMCID: PMC10905888 DOI: 10.1186/s40359-024-01603-1] [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: 07/24/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024] Open
Abstract
There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis.
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Affiliation(s)
- Lucy Tudehope
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia.
| | - Neil Harris
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
| | - Lieke Vorage
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
| | - Ernesta Sofija
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
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2
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Chen A, Zhang J, Liao W, Luo C, Shen C, Feng B. Multiplicity and dynamics of social representations of the COVID-19 pandemic on Chinese social media from 2019 to 2020. Inf Process Manag 2022; 59:102990. [PMID: 35663909 PMCID: PMC9151658 DOI: 10.1016/j.ipm.2022.102990] [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: 01/30/2022] [Revised: 05/23/2022] [Accepted: 05/29/2022] [Indexed: 11/05/2022]
Abstract
Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations’ representations did not align much with the other two groups, suggesting a lack of organizations’ influence in public representations of COVID-19 on social media in China.
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Affiliation(s)
- Anfan Chen
- Science Communication Department, University of Science and Technology of China, Hefei, Anhui, China
| | - Jingwen Zhang
- Department of Communication, Department of Public Health Sciences, University of California Davis, Davis, CA, USA
| | - Wang Liao
- Department of Communication, University of California Davis, Davis, CA, USA
| | - Chen Luo
- School of Journalism and Communication, Tsinghua University, Beijing, China
| | - Cuihua Shen
- Department of Communication, University of California Davis, Davis, CA, USA
| | - Bo Feng
- Department of Communication, University of California Davis, Davis, CA, USA
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3
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Di Y, Li A, Li H, Wu P, Yang S, Zhu M, Zhu T, Liu X. Stigma toward Wuhan people during the COVID-19 epidemic: an exploratory study based on social media. BMC Public Health 2021; 21:1958. [PMID: 34715825 PMCID: PMC8554505 DOI: 10.1186/s12889-021-12001-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 10/12/2021] [Indexed: 11/25/2022] Open
Abstract
Background Stigma associated with infectious diseases is common and causes various negative effects on stigmatized people. With Wuhan as the center of the COVID-19 outbreak in China, its people were likely to be the target of stigmatization. To evaluate the severity of stigmatization toward Wuhan people and provide necessary information for stigma mitigation, this study aimed to identify the stigmatizing attitudes toward Wuhan people and trace their changes as COVID-19 progresses in China by analyzing related posts on social media. Methods We collected 19,780 Weibo posts containing the keyword ‘Wuhan people’ and performed a content analysis to identify stigmatizing attitudes in the posts. Then, we divided our observation time into three periods and performed repeated-measures ANOVA to compare the differences in attitudes during the three periods. Results The results showed that stigma was mild, with 2.46% of related posts being stigmatizing. The percentages of stigmatizing posts differed significantly during the three periods. The percentages of ‘Infectious’ posts and ‘Stupid’ posts were significantly different for the three periods. The percentage of ‘Irresponsible’ posts was not significantly different for the three periods. After government interventions, stigma did not decrease significantly, and stigma with the ‘Infectious’ attitude even increased. It was not until the government interventions took effect that stigma significantly reduced. Conclusions This study found that stigma toward Wuhan people included diverse attitudes and changed at different periods. After government interventions but before they took effect, stigma with the ‘Infectious’ attitude increased. After government interventions took effect, general stigma and stigmas with ‘Infectious’ and ‘Stupid’ attitudes decreased. This study constituted an important endeavor to understand the stigma toward Wuhan people in China during the COVID-19 epidemic. Implications for stigma reduction and improvement of the public’s perception during different periods of epidemic control are discussed.
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Affiliation(s)
- Yazheng Di
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ang Li
- Department of Psychology, Beijing Forestry University, Beijing, 100083, China
| | - He Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Peijing Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Simin Yang
- Department of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Meng Zhu
- Hubei University of Economics, Wuhan, 430205, China
| | - Tingshao Zhu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoqian Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China. .,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
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Pan S, Yu N, Huang Y, Zhang D. Engaging Users on a Q&A Social Media Platform: The Influence of Disease Attributes and Message Features on Public Discussions of Depression. Front Psychol 2021; 12:712346. [PMID: 34630221 PMCID: PMC8498105 DOI: 10.3389/fpsyg.2021.712346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/24/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Shuya Pan
- School of Journalism and Communication, Renmin University of China, Beijing, China
| | - Nan Yu
- Nicholson School of Communication and Media, University of Central Florida, Orlando, FL, United States
| | - Yao Huang
- Domestic News Department, Xinhua News Agency, Beijing, China
| | - Di Zhang
- School of Journalism and Communication, Renmin University of China, Beijing, China
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Sik D, Németh R, Katona E. Topic modelling online depression forums: beyond narratives of self-objectification and self-blaming. J Ment Health 2021; 32:386-395. [PMID: 34582309 DOI: 10.1080/09638237.2021.1979493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Depression raises a double challenge: besides the negative mood and the intrusive thoughts, the relation to the self also becomes difficult. Online forums are analysed as communicative platforms enabling the interactive reconstruction of the self. AIMS The discourses of online depression forums are explored. Firstly, narrative patterns are identified according to their thematic focus (e.g. dysfunctional body, challenges of intimacy) and discursive logic (e.g. information exchange, support). Secondly, narratives are analysed in order to describe various ways of grounding a depressed self. METHODS ∼70.000 depression-related posts from the biggest English-speaking online forums (e.g. www.reddit.com/r/depression, www.healthunlocked.com) were analysed. Quantitative (LDA topic modelling) and qualitative (deep reading) approaches were used simultaneously to determine the optimal number of topics and their interpretation. RESULTS 13 topics were identified and interpreted according to their content and communicative function. Based on the inter-topic distances four clusters were identified (medicalized, intimacy-oriented, critical and uninhabitable self-narratives). CONCLUSIONS The clusters of the 13 topics highlight various ways of narrating depression and the depressed self. Based on a comparison with a systematic review of mental illness recovery narratives, depression forums cover most narrative genres and emotional tones, thus create a unique opportunity for integrating the depressing experiences in the self.
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Affiliation(s)
- Domonkos Sik
- Institute of Sociology, Eötvös Loránd University, Budapest, Hungary
| | - Renáta Németh
- Institute of Empirical Studies, Eötvös Loránd University, Budapest, Hungary
| | - Eszter Katona
- Institute of Empirical Studies, Eötvös Loránd University, Budapest, Hungary
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Németh R, Sik D, Katona E. The asymmetries of the biopsychosocial model of depression in lay discourses - Topic modelling online depression forums. SSM Popul Health 2021; 14:100785. [PMID: 33912649 PMCID: PMC8066842 DOI: 10.1016/j.ssmph.2021.100785] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/22/2021] [Accepted: 03/24/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND One of the most comprehensive approaches to depression is the biopsychosocial model. From this wider perspective, social sciences have criticized the reductionist biomedical discourse, which has been dominating expert discourses for a long time. As these discourses determine the horizon of attributions and interventions, their lay interpretation plays a central role in the coping with depression. METHODS In order to map these patterns, online depression forums are analyzed with natural language processing methods, where computational tools are complemented with a qualitative approach. Latent Dirichlet Allocation topic model of depression-related posts from the most popular English-speaking online health discussion forums (N = ~70 000) reveals the monolog (attributions and self-disclosures) and interactive (consultations and quasi-therapeutic interactions) patterns. RESULTS Following the evaluation of various models 18 topics were differentiated: attributions referring to health, family, partnership and work issues; self-disclosures referring to contemplations, introducing the experience of suffering and well-being, along with diaries of everyday activities and hardships; consultations about psychotherapies, classifications, drugs and the experience; and quasi-therapeutic interactions relying on unconditional positive regards, recovery helpers experience or spirituality. These topics were evaluated from the perspective of the biopsychosocial model: the weight of each dimension was measured along with the discursive function. CONCLUSIONS Biomedical discourse is underrepresented in lay discussions, while psychological discourse plays an overall dominant role. Even if actors are initially aware of the social mechanisms contributing to depression, they neglect these factors when it comes to considering the countermeasures.
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Affiliation(s)
- Renáta Németh
- ELTE Eötvös Loránd University of Budapest, Faculty of Social Sciences, Research Center for Computational Social Science, Budapest, Pázmány Péter Sétány 1/a, 1117, Hungary
| | - Domonkos Sik
- ELTE Eötvös Loránd University of Budapest, Faculty of Social Sciences, Research Center for Computational Social Science, Budapest, Pázmány Péter Sétány 1/a, 1117, Hungary
| | - Eszter Katona
- ELTE Eötvös Loránd University of Budapest, Faculty of Social Sciences, Research Center for Computational Social Science, Budapest, Pázmány Péter Sétány 1/a, 1117, Hungary
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Detecting changes in attitudes toward depression on Chinese social media: A text analysis. J Affect Disord 2021; 280:354-363. [PMID: 33221722 DOI: 10.1016/j.jad.2020.11.040] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 09/16/2020] [Accepted: 11/07/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS Depression is a common and sometimes severe form of mental illness, and public attitudes towards depression can impact the psychological and social functioning of depressed patients. The purpose of the present study was to investigate public attitudes toward depression and three-year trends in these attitudes using big data analysis of social media posts in China. METHODS A search of publically available Sina Weibo posts from January 2014 to July 2017 identified 20,129 hot posts with the keyword term "depression". We first used a Chinese Linguistic Psychological Text Analysis System (TextMind) to analyze linguistic features of the posts. And, then we used topic models to conduct semantic content analysis to identify specific themes in Weibo users' attitudes toward depression. RESULTS Linguistic features analysis showed a significant increase over time in the frequency of terms related to affect, positive emotion, anger, cognition (including the subcategory of insight), and conjunctions. Semantic content analysis identified five common themes: severe effects of depression, stigma, combating stigma, appeals for understanding, and providing support. There was a significant increase over time in references to social (as opposed to professional) support, and a significant decrease over time in references to the severe consequences of depression. CONCLUSIONS Big data analysis of Weibo posts is likely to provide less biased information than other methods about the public's attitudes toward depression. The results suggest that although there is ongoing stigma about depression, there is also an upward trend in mentions of social support for depressed persons. A supervised learning statistical model can be developed in future research to provide an even more precise analysis of specific attitudes.
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Gao S, He L, Chen Y, Li D, Lai K. Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media. J Med Internet Res 2020; 22:e16649. [PMID: 32673231 PMCID: PMC7385634 DOI: 10.2196/16649] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 04/02/2020] [Accepted: 05/31/2020] [Indexed: 01/22/2023] Open
Abstract
Background High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public’s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients’ emotional needs instead of focusing merely on technical issues.
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Affiliation(s)
- Shuqing Gao
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Lingnan He
- School of Communication and Design, Sun Yat-Sen University, Guangzhou, China.,Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion, Guangzhou, China
| | - Yue Chen
- School of Communication and Design, Sun Yat-Sen University, Guangzhou, China
| | - Dan Li
- School of Journalism and Communication, Jinan University, Guangzhou, China
| | - Kaisheng Lai
- School of Journalism and Communication, Jinan University, Guangzhou, China
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