Detecting depression stigma on social media: A linguistic analysis

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study

Background

Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels.

Methods

938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results.

Results

For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer's V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer's V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer's V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74.

Conclusion

This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

"It's Tough to Be a Black Man with Schizophrenia": Randomized Controlled Trial of a Brief Video Intervention to Reduce Public Stigma

BACKGROUND AND HYPOTHESIS

Racial discrimination and public stigma toward Black individuals living with schizophrenia create disparities in treatment-seeking and engagement. Brief, social-contact-based video interventions efficaciously reduce stigma. It remains unclear whether including racial identity experiences in video narrative yields greater stigma reduction. We hypothesized that we would replicate findings showing sustained stigma reduction in video-intervention groups vs control and that Black participants would show greater stigma reduction and emotional engagement than non-Black participants only for a racial-insights video presenting a Black protagonist.

STUDY DESIGN

Recruiting using a crowdsourcing platform, we randomized 1351 participants ages 18-30 to (a) brief video-based intervention, (b) racial-insights-focused brief video, or (c) non-intervention control, with baseline, post-intervention, and 30-day follow-up assessments. In 2-minute videos, a young Black protagonist described symptoms, personal struggles, and recovery from schizophrenia, with or without mentioning race-related experiences.

STUDY RESULTS

A 3 × 3 ANOVA showed a significant group-by-time interaction for total scores of each of five stigma-related domains: social distance, stereotyping, separateness, social restriction, and perceived recovery (all P < .001). Linear mixed modeling showed a greater reduction in stigma from baseline to post-intervention among Black than non-Black participants in the racial insights video group for the social distance and social restriction domains.

CONCLUSIONS

This randomized controlled trial replicated and expanded previous findings, showing the anti-stigma effects of a brief video tailored to race-related experiences. This underscores the importance of personalized, culturally relevant narratives, especially for marginalized groups who, more attuned to prejudice and discrimination, may particularly value identification and solidarity. Future studies should explore mediators/moderators to improve intervention efficacy.

What methods are used to examine representation of mental ill-health on social media? A systematic review

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.

Embitterment and metacognition in obsessive-compulsive disorder

OBJECTIVE

Embitterment is a persistent emotion that is known to everybody in reaction to injustice and being let down, associated with feelings of helplessness and hopelessness. People with psychiatric disorders can develop bitterness, which is to be understood as a form of reactive embitterment to the illness. The aim of this explorative study was to investigate the occurrence of embitterment in obsessive-compulsive patients compared to healthy volunteers and in the context of their metacognitions and other biographical and clinical characteristics.

METHOD

Following a semi-structured diagnostic interview, a number of measures were administered to 31 patients with obsessive-compulsive disorder (OCD) [ICD-10 F42.X: mean age 35.2 (SD = 10.7) years] and 31 healthy volunteers [mean age 39.1 (SD = 15.0) years]. These measures included the Post-Traumatic Embitterment Disorder questionaire (PTEDq) for measuring embitterment, the Yale-Brown Obsessive-Compulsive Scale, the Metacognition Questionnaire and other psychometric questionnaires such as the Beck Depression Inventory and the State-Trait Anxiety Inventory.

RESULTS

Patients with OCD scored more than three times higher (mean = 2.0, SD = 1.1) than the healthy participants in the PTEDq (mean = 0.6, SD = 0.8; p < 0.001), but the cut-off of < 2.5 for a clinically relevant embitterment disorder was not reached. Dysfunctionally distorted metacognition (MCQ-30), which is a consistent finding in OCD, as well as a generally high degree of clinical impairment were significantly cor related to the degree of embitterment.

CONCLUSION

Our findings suggest that embitterment as measured by PTEDq is important in patients with OCD, who are characterized by metacognitive distortions with an injustice of fate as well as a mortification of their self-image. In future, it would be necessary to screen patients with OCD not only for depressive symptoms but also specifically for feelings of embitterment in order to be able to initiate appropriate psychotherapeutic measures at an early stage.

COVID vaccine stigma: detecting stigma across social media platforms with computational model based on deep learning

The study presents the first computational model of COVID vaccine stigma that can identify stigmatised sentiment with a high level of accuracy and generalises well across a number of social media platforms. The aim of the study is to understand the lexical features that are prevalent in COVID vaccine discourse and disputes between anti-vaccine and pro-vaccine groups. This should provide better insight for healthcare authorities, enabling them to better navigate those discussions. The study collected posts and their comments related to COVID vaccine sentiment in English, from Reddit, Twitter, and YouTube, for the period from April 2020 to March 2021. The labels used in the model, "stigma", "not stigma", and "undefined", were collected from a smaller Facebook (Meta) dataset and successfully propagated into a larger dataset from Reddit, Twitter, and YouTube. The success of the propagation task and consequent classification is a result of state-of-the-art annotation scheme and annotated dataset. Deep learning and pre-trained word vector embedding significantly outperformed traditional algorithms, according to two-tailed P(T≤t) test and achieved F1 score of 0.794 on the classification task with three classes. Stigmatised text in COVID anti-vaccine discourse is characterised by high levels of subjectivity, negative sentiment, anxiety, anger, risk, and healthcare references. After the first half of 2020, anti-vaccination stigma sentiment appears often in comments to posts attempting to disprove COVID vaccine conspiracy theories. This is inconsonant with previous research findings, where anti-vaccine people stayed primarily within their own in-group discussions. This shift in the behaviour of the anti-vaccine movement from affirming climates to ones with opposing opinions will be discussed and elaborated further in the study.

Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model

Depression is a kind of emotion that negatively impacts people's daily lives. The number of people suffering from long-term feelings is increasing every year across the globe. Depressed patients may engage in self-harm behaviors, which occasionally result in suicide. Many psychiatrists struggle to identify the presence of mental illness or negative emotion early to provide a better course of treatment before they reach a critical stage. One of the most challenging problems is detecting depression in people at the earliest possible stage. Researchers are using Natural Language Processing (NLP) techniques to analyze text content uploaded on social media, which helps to design approaches for detecting depression. This work analyses numerous prior studies that used learning techniques to identify depression. The existing methods suffer from better model representation problems to detect depression from the text with high accuracy. The present work addresses a solution to these problems by creating a new hybrid deep learning neural network design with better text representations called "Fasttext Convolution Neural Network with Long Short-Term Memory (FCL)." In addition, this work utilizes the advantage of NLP to simplify the text analysis during the model development. The FCL model comprises fasttext embedding for better text representation considering out-of-vocabulary (OOV) with semantic information, a convolution neural network (CNN) architecture to extract global information, and Long Short-Term Memory (LSTM) architecture to extract local features with dependencies. The present work was implemented on real-world datasets utilized in the literature. The proposed technique provides better results than the state-of-the-art to detect depression with high accuracy.

Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study

Background

Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.

Objective

This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.

Methods

Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time.

Results

A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions.

Conclusions

Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time.

Exploring the potential of delivering mental health care services using digital technologies in Bangladesh: A qualitative analysis

BACKGROUND

Bangladesh is a lower-middle-income country affected by a severe lack of mental health service availability due to a scarcity of mental health experts, limited mental health literacy, and community stigma. In other low and middle-income countries, the online provision of mental health care services has addressed issues affecting service availability, accessibility, mass awareness of services, and stigma.

OBJECTIVE

The current study sought to understand stakeholders' perceptions of the potential of digital media-based mental health care delivery in strengthening Bangladesh's mental health system.

METHOD

Online in-depth interviews were conducted with seven psychiatrists and eleven people with lived experiences of mental health issues. In addition, two online focus groups were conducted with ten psychologists and nine mental health entrepreneurs. A thematic analysis of the audio transcriptions was used to identify themes.

RESULT

Stakeholders perceived that the benefits of digital media-based mental health services included the potential of increasing the awareness, availability, and accessibility of mental health services. Participants recommended: the rehabilitation of existing pathways; the use of social media to raise awareness; and the implementation of strategies that integrate different digital-based services to strengthen the mental health system and foster positive mental health-seeking behaviors.

CONCLUSION

Growing mental health awareness, combined with the appropriate use of digital media as a platform for distributing information and offering mental services, can help to promote mental health care. To strengthen mental health services in Bangladesh, tailored services, increased network coverage, and training are required on digital mental health.

Reducing public stigma toward individuals with psychosis across race and gender: A randomized controlled trial of young adults

BACKGROUND

Social contact-based interventions effectively reduce stigma toward psychosis. We recently demonstrated the efficacy of a 90-second video intervention in reducing stigma. The current randomized controlled study presents four briefer videos differing in presenter's gender/race, with baseline, postintervention, and 30-day follow-up assessments. The study replicates previous findings and examine whether concordance of presenter's and viewer's race/gender enhanced the anti-stigma effect.

METHODS

Using a crowdsourcing platform, we recruited 1993 participants ages 18-35 years to one of four brief video-based interventions (Black/White female, Black/White male presenters) or a nonintervention control condition. In the videos, a young presenter with psychosis humanized their illness through an evocative description of living a meaningful and productive life.

RESULTS

Group-by-time ANOVA showed a significant group-by-time interaction for the total score of all five stigma domains: social distance, stereotyping, separateness, social restriction, and perceived recovery. One-way ANOVA showed greater reductions in video intervention groups than control at post-intervention and 30-day follow-up, but no differences between video groups. Matching race/gender did not further reduce stigma.

CONCLUSIONS

This randomized controlled study replicated and extended previous research findings, by showing stigma reduction across videos that differ in the presenter's gender and race, thus enhancing generalizability. The videos described the experience of psychosis and reduced stigma, suggesting their potential utility on social media platforms to increase the likelihood of seeking services and ultimately may improve access to care among young individuals with psychosis. Future research should address intersectional stigma experiences by focusing on race/gender and culturally tailoring the narrative.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo

Background

The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience’s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited.

Objective

This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics.

Methods

A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide.

Results

In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ²₁=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ²₁=28.9; P<.001), false representation (χ²₁=144.4; P<.001), weak and pathetic (χ²₁=20.4; P<.001), glorified and normalized (χ²₁=177.6; P<.001), and immoral (χ²₁=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85.

Conclusions

The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

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.

Analysis of the causes of inferiority feelings based on social media data with Word2Vec

Feelings of inferiority are complex emotions that usually indicate perceived weakness and helplessness. A lack of timely and effective interventions may bring serious consequences to individuals with inferiority feelings. Due to privacy concerns, those people often hesitate to seek face-to-face help, but they usually spontaneously share their feelings on social media, which makes social media a good resource for ample inferiority-related data. We randomly selected a sample of posts indicating inferiority feelings to explore the causes of inferiority. Through language analysis and natural language processing, we constructed a Word2Vec model of inferiority based on social media data and applied it to the cause analysis of inferiority feelings. The main causes of inferiority feelings are personal experience, social interaction, love relationship, etc. People feeling inferior about their personal experiences usually are largely influenced by their ways of thinking and life attitudes. Social and emotional factors overlap somewhat in the development of inferiority. In love relationships, males are more prone to inferiority feeling than females. These findings will help relevant institutions and organizations better understand people with inferiority feelings and facilitate the development of targeted treatment for those with potential self-esteem problems.

Detecting Depression Signs on Social Media: A Systematic Literature Review

Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients.

RETRACTED ARTICLE: The 'rhetorical concession': a linguistic analysis of debates and arguments in mental health

We, the editors and publisher of Journal of Mental Health, have retracted the following article: Garner, B., Kinderman, P., & Davis, P. (2021). 'The "rhetorical concession": a linguistic analysis of debates and arguments in mental health', DOI: 10.1080/09638237.2021.2022631Since publication, a conflict of interest has been brought to our attention. Blog 'F', which is one of a series of blogs analysed in this paper, has been identified as the blog of Peter Kinderman, co-author of the paper. This conflict of interest was not disclosed upon submission of the article, and we consequently believe that this compromises the reliability of the reviews and the paper's findings. We are therefore retracting the article.Our decision has been informed by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'. .

Revealing traces of depression through personal statements analysis in social media

Depression is a common and very important health issue with serious effects in the daily life of people. Recently, several researchers have explored the analysis of user-generated data in social media to detect and diagnose signs of this mental disorder in individuals. In this regard, we tackled the depression detection task in social media considering the idea that terms located in phrases exposing personal statements (i.e., phrases characterized by the use of singular first person pronouns) have a special value for revealing signs of depression. First, we assessed the value of the personal statements for depression detection in social media. Second, we adapted an automatic approach that emphasizes the personal statements by means of a feature selection method and a term weighting scheme. Finally, we addressed the task in hand as an early detection problem, where the aim is to detect traces of depression with as much anticipation as possible. For evaluating these ideas, benchmark Reddit data for depression detection was used. The obtained results indicate that the personal statements have high relevance for revealing traces of depression. Furthermore, the results on early scenarios demonstrated that the proposed approach achieves high competitiveness compared with state-of-the-art methods, while maintaining its simplicity and interpretability.

Stigma toward Wuhan people during the COVID-19 epidemic: an exploratory study based on social media

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.

Reddit use in the LIS community

This study examined online communications within library and information science (LIS)-related subreddits through the perspectives of social identity theory and social media engagement. A total of 17,049 Reddit posts from three major LIS-related subreddits were harvested through Pushshift by Python. Manual and computational coding methods were used to identify linguistic characteristics of the posts as well as the topicality of the content. Multiple logistic regressions were used to analyze the relationship between psychological and emotional characteristics of the posts and user engagement. Results showed that the majority of posts did not mention a specific type of LIS organization; of the subset that did, public libraries and academic libraries were most frequently referenced. “Library services and usage” was the topic most mentioned within LIS-related subreddits, followed by “librarians and jobs.” Various psychological features of posts including emotional tone score, authentic score, clout score, analytic score, and usage of words related to affective processes, anxiety, social processes, causation, and time were significantly related to user engagement. This study contributes to further understanding of social identity theory and social media engagement within the online communication context of LIS community. Key topics and issues in the LIS field and practical suggestions for strategies to improve social media engagement across LIS community were discussed.

Topic modelling online depression forums: beyond narratives of self-objectification and self-blaming

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.

What Happens When People with Depression Gather Online?

Depression is a common mental disease that impacts people of all ages and backgrounds. To meet needs that cannot otherwise be met, people with depression or who tend to suffer from depression often gather in online depression communities. However, since joining a depression community exposes members to the depression of others, the impact of such communities is not entirely clear. This study therefore explored what happens when people with depression gather in Sina Weibo’s Depression Super Topic online community. Through website crawling, postings from Depression Super Topic were compared with postings from members’ regular timelines with respect to themes, emotions disclosed, activity patterns, and the number of likes and comments. Topics of distilled postings covering support, regulations, emotions and life sharing, and initiating discussions were then coded. From comparison analysis, it was found that postings in the Depression Super Topic community received more comments and disclosed more emotions than regular timelines and that members were more active in the community at night. This study offers a picture of what occurs when people with depression gather online, which helps better understand their issues and therefore provide more targeted support.

Effect of income inequality, community infrastructure and individual stressors on adult depression

This paper considers the impact of variables at three different levels-city, community and individual-on the depression of adults aged 45 years and above in China. Evidence shows that community factors, such as infrastructure and elderly centres, are critical to reduce depression but the effect of city-level factors such as lowering income inequality and improving public health investment is different for the segments of poor and non-poor as well as the rural and urban residents. This highlights the need to consider targeted policy mix options to avoid resource misallocation. Lastly, Chinese females' depression has worsened over time with ageing, particularly those who drink alcohol or are less educated are prone to depression prompting the need for specialist women health centres.

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

BACKGROUND

Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation.

OBJECTIVE

This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised.

METHODS

We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis.

RESULTS

We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance.

CONCLUSIONS

Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

The asymmetries of the biopsychosocial model of depression in lay discourses - Topic modelling online depression forums

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.

Detecting changes in attitudes toward depression on Chinese social media: A text analysis

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.

Witnessing Acute Stress Reaction in Team Members: The Moderating Effect of Peer-Based Training

Individuals who witness team members exhibiting symptoms of an acute stress reaction (ASR) in the middle of a high-stress operational event may be negatively affected; ASR-related training may moderate this impact. In the present study, 560 Israeli soldiers were surveyed about ASR exposure, posttraumatic stress disorder (PTSD) symptoms, public stigma, and whether they had received ASR-related training. This training, called YaHaLOM, is a Hebrew acronym that outlines steps for managing ASR in team members. Controlling for combat exposure, greater exposure to ASR symptoms was associated with more overall PTSD symptoms, PTSD cluster symptoms, and public stigma. YaHaLOM training buffered these relationships for PTSD, intrusion and avoidance symptoms, and public stigma. The findings suggest that such training may help teams in high-risk occupations better manage ASR exposure.

A Comparison of the Psycholinguistic Styles of Schizophrenia-Related Stigma and Depression-Related Stigma on Social Media: Content Analysis

BACKGROUND

Stigma related to schizophrenia is considered to be the primary focus of antistigma campaigns. Accurate and efficient detection of stigma toward schizophrenia in mass media is essential for the development of targeted antistigma interventions at the population level.

OBJECTIVE

The purpose of this study was to examine the psycholinguistic characteristics of schizophrenia-related stigma on social media (ie, Sina Weibo, a Chinese microblogging website), and then to explore whether schizophrenia-related stigma can be distinguished from stigma toward other mental illnesses (ie, depression-related stigma) in terms of psycholinguistic style.

METHODS

A total of 19,224 schizophrenia- and 15,879 depression-related Weibo posts were collected and analyzed. First, a human-based content analysis was performed on collected posts to determine whether they reflected stigma or not. Second, by using Linguistic Inquiry and Word Count software (Simplified Chinese version), a number of psycholinguistic features were automatically extracted from each post. Third, based on selected key features, four groups of classification models were established for different purposes: (a) differentiating schizophrenia-related stigma from nonstigma, (b) differentiating a certain subcategory of schizophrenia-related stigma from other subcategories, (c) differentiating schizophrenia-related stigma from depression-related stigma, and (d) differentiating a certain subcategory of schizophrenia-related stigma from the corresponding subcategory of depression-related stigma.

RESULTS

In total, 26.22% of schizophrenia-related posts were labeled as stigmatizing posts. The proportion of posts indicating depression-related stigma was significantly lower than that indicating schizophrenia-related stigma (χ²₁=2484.64, P<.001). The classification performance of the models in the four groups ranged from .71 to .92 (F measure).

CONCLUSIONS

The findings of this study have implications for the detection and reduction of stigma toward schizophrenia on social media.

Overview of Stigma against Psychiatric Illnesses and Advancements of Anti-Stigma Activities in Six Asian Societies

Background: In psychiatry, stigma is an attitude of disapproval towards people with mental illnesses. Psychiatric disorders are common in Asia but some Asians receive inadequate treatment. Previous review found that Asians with mental illness were perceived to be dangerous and aggressive. There is a need for renewed efforts to understand stigma and strategies which can effectively reduce stigma in specific Asian societies. The objective of this systematic review was to provide an up-to-date overview of existing research and status on stigma experienced by psychiatric patients and anti-stigma campaigns in China, Hong Kong, Japan, Singapore, Korea, and Thailand. Methods: A systematic literature search was conducted in the following databases, including PubMed, PsycINFO, Embase, Web of Science, and local databases. Studies published in English and the official language of included countries/territories were considered for inclusion in the systematic review. Any article on stigma related to any form of psychiatric illness in the six Asian societies was included. Results: One hundred and twenty-three articles were included for this systematic review. This review has six major findings. Firstly, Asians with mental illnesses were considered as dangerous and aggressive, especially patients suffering from schizophrenia and bipolar disorder; second, psychiatric illnesses in Asian societies were less socially-acceptable and were viewed as being personal weaknesses; third, stigma experienced by family members was pervasive and this is known as family stigma; fourth, this systemic review reported more initiatives to handle stigma in Asian societies than a decade ago; fifth, there have been initiatives to treat psychiatric patients in the community; and sixth, the role of supernatural and religious approaches to psychiatric illness was not prevailing. Conclusion: This systematic review provides an overview of the available scientific evidence that points to areas of needed intervention to reduce and ultimately eliminate inequities in mental health in Asia.

Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo

Background: This study investigates negative emotional patterns of people with depression on Sina Weibo™ and gives an in-depth analysis of how their negative emotions change over time. Materials and Methods: A text classifier using deep learning methods was built to identify people on Sina Weibo with depression and associated negative emotions. The longitudinal changes in negative emotions were assessed using time series and cluster analysis. Results: Results indicate that people with depression (n = 616) were more active and expressed more negative emotions on social media compared with control users (n = 3,176). Furthermore, negative emotions of people with depression were mostly about their depression issues, such as treatment, hopelessness, suicidal ideation, or self-injury. Both groups of users usually expressed negative emotions in the late evening and early morning hours. Finally, longitudinal changes in negative emotions illustrate that users with depression tended to have relatively high negative emotions in the month when they started using social media to reveal their depression issues and that they exhibited three main evolutionary patterns of negative emotions on social media. Conclusions: Findings from the study could be used to track and monitor negative emotional states of people with depression on social media, in addition to providing an in-depth understanding of how negative emotions change over time, so that better intervention strategies can be adopted to assist them.

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

Live-stream suicide has become an emerging public health problem in many countries. Regular users are often the first to witness and respond to such suicides, emphasizing their impact on the success of crisis intervention. In order to reduce the likelihood of suicide deaths, this paper aims to use psycholinguistic analysis methods to facilitate automatic detection of negative expressions in responses to live-stream suicides on social media. In this paper, a total of 7212 comments posted on suicide-related messages were collected and analyzed. First, a content analysis was performed to investigate the nature of each comment (negative or not). Second, the simplified Chinese version of the LIWC software was used to extract 75 psycholinguistic features from each comment. Third, based on 19 selected key features, four classification models were established to differentiate between comments with and without negative expressions. Results showed that 19.55% of 7212 comments were recognized as "making negative responses". Among the four classification models, the highest values of Precision, Recall, F-Measure, and Screening Efficacy reached 69.8%, 85.9%, 72.9%, and 47.1%, respectively. This paper confirms the need for campaigns to reduce negative responses to live-stream suicides and support the use of psycholinguistic analysis methods to improve suicide prevention efforts.

Longitudinal associations of depressive symptoms, subjective memory decline, and cognitive functioning among Chinese older adults: Between-person and within-person perspective

OBJECTIVES

We examined between- and within-person associations between depressive symptoms and cognitive functioning among Chinese older adults (aged 60+) over time. Furthermore, we also investigated whether subjective memory decline (SMD) is uniquely associated with cognitive functioning above and beyond depressive symptoms for both between-person and within-person associations.

METHODS

About 7385 older adults from the China Health and Retirement Longitudinal Study reported their demographic and health status, and completed self-report measures of depressive symptoms and SMD, as well as a battery of cognitive tests, every two years at three times between 2011 and 2015.

RESULTS

There were significant between-person and within-person associations between depressive symptoms and cognitive functioning. Furthermore, SMD was uniquely associated with cognitive functioning for both within-person and between-person associations after controlling for depressive symptoms.

CONCLUSIONS

The results highlight the importance of careful screening and monitoring of depressive symptoms and SMD for the benefits of cognitive functioning among Chinese older adults. More importantly, SMD has practical implications for the care of Chinese older adults given significant cultural stigma attached to mental illness within Chinese culture.

A content analysis of depression-related discourses on Sina Weibo: attribution, efficacy, and information sources

BACKGROUND

Depression is a mood disorder that may lead to severe outcomes including mental breakdown, self-injury, and suicide. Potential causes of depression include genetic, sociocultural, and individual-level factors. However, public understandings of depression guided by a complex interplay of media and other societal discourses might not be congruent with the scientific knowledge. Misunderstandings of depression can lead to under-treatment and stigmatization of depression. Against this backdrop, this study aims to achieve a holistic understanding of the patterns and dynamics in discourses about depression from various information sources in China by looking at related posts on social media.

METHOD

A content analysis was conducted with 902 posts about depression randomly selected within a three-year period (2014 to 2016) on the mainstream social media platform in China, Sina Weibo. Posts were analyzed with a focus on attributions of and solutions to depression, attitudes towards depression, and efficacy indicated by the posts across various information sources.

RESULTS

Results suggested that depression was most often attributed to individual-level factors. Across all the sources, individual-level attributions were often adopted by state-owned media whereas health and academic experts and organizations most often mentioned biological causes of depression. Citizen journalists and unofficial social groups tended to make societal-level attributions. Overall, traditional media posts suggested the lowest efficacy in coping with depression and the most severe negative outcomes as compared with other sources.

CONCLUSIONS

The dominance of individual-level attributions and solutions regarding depression on Chinese social media on one hand manifests the public's limited understanding of depression and on the other hand, may further constrain adoption of scientific explanations about depression and exacerbate stigmatization towards depressed individuals. Mass media's posts centered on description of severe outcomes of depression without suggestions of solutions' effectiveness, which may induce more anxiety among depressed individuals. Campaigns promoting comprehensive understandings about depression and popular works translating scientific findings on depression to the public are called for.