Measuring attitudes towards mental health using social media: investigating stigma and trivialisation

"It was a reflection of myself, that i was weak": The impact of depression on the sense of self - An interpretive phenomenological analysis

The World Health Organisation states that more than 350 million people experience depression globally. The phenomenological changes in individuals experiencing depression are profound Phenomenological research can further researchers' and clinicians' understanding of this experience. This study aimed to gain a phenomenological understanding of how individuals with depression understood and made sense of their experiences. A methodology of interpretative phenomenological analysis was adopted. In-depth semi-structured interviews explored the lived experience of depression for eight individuals. Data were analysed into the superordinate theme Broken Self - Transforming the Self. The superordinate theme developed from the subordinate themes of 'unknown self, loss of self and one's identity', 'desperate for a way out', and thirdly, 'conflict with self and what's known', which related directly to how individuals made sense of their experience of depression. These research findings highlight the human implications of the experience of depression and the limitations of viewing depression from a biological or medical model lens. Understanding the human impact is essential for the effective, holistic practice of mental health nursing.

Examining Symptoms of Stimulant Misuse and Community Support Among Members of a Recovery-Oriented Online Community

Misuse of prescription and non-prescription stimulants and related overdose deaths represent a growing public health crisis that warrants immediate intervention. We examined 100 posts and their respective comments from a public, recovery-oriented Reddit community in January 2021 to explore content related to DSM-V stimulant use disorder symptoms, access and barriers to recovery, and peer support. Using inductive and deductive methods, a codebook was developed with the following primary themes: 1) DSM-V Symptoms and Risk Factors, 2) Stigma/Shame, 3) Seeking Advice or Information, 4) Supportive or Unsupportive Comments. In 37% of posts community members reported taking high doses and engaging in prolonged misuse of stimulants. Nearly half of posts in the sample (46%) were seeking advice for recovery, but 42% noted fear of withdrawal symptoms or a loss of productivity (18%) as barriers to abstinence or a reduction in use. Concerns related to stigma, shame, hiding use from others (30%), and comorbid mental health conditions (34%) were also noted. Social media content analysis allows for insight into information about lived experiences of individuals struggling with substance use disorders. Future online interventions should address recovery barriers related to stigma and shame as well as fears associated with the physical and psychological impact of quitting stimulant misuse.

Emotional reactions and stigmatization after a parricide in South Tyrol, Italy, among mental health professionals and the general population, including persons with mental disorders, relatives, and persons with no direct or indirect contact

Introduction

This study was conducted on the occasion of the parricide in Bolzano (South Tyrol, Italy) in January 2021. The psychological impact of parricide on the general population and on mental health professionals has scarcely been investigated to the present day. Studies on stigmatization show differences between various groups. The aim was to analyze the emotional reactions to the parricide and the stigmatization of persons with mental disorders in the South Tyrolian population.

Methods

In September 2022, 121 mental health professionals of the Department of Psychiatry in Bolzano were surveyed using an online questionnaire. In addition, from January to March 2023, the general population of South Tyrol was invited to take part in the survey through an online-link and was divided into three groups: 267 persons with mental health problems, 855 relatives and 1,019 persons with no direct or indirect contact to people with mental problems. The validated Reported and Intended Behavior Scale (RIBS) was used together with questions on the emotional reactions to the parricide and the perceived dangerousness of psychiatric patients. Descriptive statistics, one-way Anovas as well as regressions were carried out.

Results and discussion

All groups experienced sadness the most. Relatives experienced more sadness and anger than the other groups. Over 80% of the professionals stated that psychiatric patients were not at greater risk of committing parricide. The population with no contact rated the risk higher than those affected and had the lowest level of openness (RIBS). There were no differences between genders, but there were age differences, with younger people being more stigmatizing. The results suggest that personal contact, appropriate information, and education are associated with less stigmatization.

Social networks use in the context of Schizophrenia: a review of the literature

Schizophrenia is a persistent mental health condition that, while presenting challenges, underscores the dynamic nature of cognitive functions and encourages a unique perspective on how individuals engage with their surroundings. Social networks, as a means of communication of great importance at the present time, are for this type of people a way of interacting with their environment with a high level of security. The aim is to find out how schizophrenia is dealt with in different social networks and to differentiate between different types of articles dealing with the use of Facebook, X (former Twitter), YouTube, TikTok, Instagram, and Weibo. A total of 45 articles to i) Social networks used, ii) Country of analyzed users, iii) age of the users analyzed, iv) focus of the analyzed manuscript (mental health literacy, stigmatization, detection of patterns associated with schizophrenia, and Harmful substance use). It was observed that 45.45% of the studies analyzed were conducted in the USA population, followed by UK and China (13.64%). The most analyzed social networks were those based on audiovisual communication (60%). Furthermore, the two main foci addressed in these articles were: stigmatization of schizophrenia with 16 articles (35.55%), following by the prediction of schizophrenia-detecting patterns with 15 articles (33.33%) and the use of social networks to stigmatize people with schizophrenia (38%) and only 14 articles (31.11%) were focused on mental health literacy. Likewise, it was found that there is great potential in the use of the analysis of the content generated, as possible predictors of the presence of this disease, which would allow rapid detection and intervention for psychosis and schizophrenia.

Areas of interest and sentiment analysis towards second generation antipsychotics, lithium and mood stabilizing anticonvulsants: Unsupervised analysis using Twitter

BACKGROUND

Severe mental disorders like Schizophrenia and related psychotic disorders (SRD) or Bipolar Disorder (BD) require pharmacological treatment for relapse prevention and quality of life improvement. Yet, treatment adherence is a challenge, partly due to patients' attitudes and beliefs towards their medication. Social media listening offers insights into patient experiences and preferences, particularly in severe mental disorders.

METHODS

All tweets posted between 2008 and 2022 mentioning the names of the main drugs used in SRD and BD were analyzed using advanced artificial intelligence techniques such as machine learning, and deep learning, along with natural language processing.

RESULTS

In this 15-year study analyzing 893,289 tweets, second generation antipsychotics received more mentions in English tweets, whereas mood stabilizers received more tweets in Spanish. English tweets about economic and legal aspects displayed negative emotions, while Spanish tweets seeking advice showed surprise. Moreover, a recurring theme in Spanish tweets was the shortage of medications, evoking feelings of anger among users.

LIMITATIONS

This study's analysis of Twitter data, while insightful, may not fully capture the nuances of discussions due to the platform's brevity. Additionally, the wide therapeutic use of the studied drugs, complicates the isolation of disorder-specific discourse. Only English and Spanish tweets were examined, limiting the cultural breadth of the findings.

CONCLUSION

This study emphasizes the importance of social media research in understanding user perceptions of SRD and BD treatments. The results provide valuable insights for clinicians when considering how patients and the general public view and communicate about these treatments in the digital environment.

Can an app increase health literacy and reduce the stigma associated with obsessive-compulsive disorder? A crossover randomized controlled trial

BACKGROUND

Obsessive-compulsive disorder (OCD) is a disabling condition with a high delay in seeking treatment. esTOCma is an app developed to increase mental health literacy (MHL) about OCD, reduce stigma, and increase the intention to seek professional treatment. It is a serious game and participants are asked to fight against the "OCD stigma monster" by accomplishing 10 missions. The aim of this study is to evaluate the effectiveness of this app in a community sample.

METHODS

A randomized controlled trial with a crossover design was carried out. Participants were randomized to two groups: immediate use (iApp, n = 102) and delayed use (dApp, n = 106) of esTOCma. The iApp group started using the app at baseline until the game was over. The dApp group initiated at 10-days until the game finished. Participants were requested to complete a set of questionnaires at baseline and 10-day, 20-day and 3-month follow-ups.

RESULTS

The Time×Group interaction effect was significant for the primary outcome measures: there was an increase in MHL and intention to seek help, and a decrease in stigma and OC symptoms, with large effect sizes, only after using the app. Changes were maintained (or increased) at follow-up.

LIMITATIONS

The study did not include an active control group and some of the scales showed low internal consistency or a ceiling effect.

CONCLUSIONS

This study provides first evidence for the effectiveness of esTOCma as a promising intervention to fight stigma and reduce the treatment gap in OCD.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04777292. Registered February 23, 2021, https://clinicaltrials.gov/ct2/show/NCT04777292.

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.

Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study

BACKGROUND

Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction.

OBJECTIVE

This study aims to explore the perceptions and experiences of Twitter users concerning these drugs.

METHODS

We analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers.

RESULTS

Of classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8%). Among medical content tweets, 80.8% (41,080/50,822) mentioned drug efficacy, with only 6.9% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001).

CONCLUSIONS

Patients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions.

Stigma Toward Substance Dependence: Causes, Consequences, and Potential Interventions

Substance dependence is a prevalent and urgent public health problem. In 2021, 60 million Americans reported abusing alcohol within the month prior to being surveyed, and nearly 20 million Americans reported using illegal drugs (e.g., heroin) or prescription drugs (e.g., opioids) for nonmedical reasons in the year before. Drug-involved overdose rates have been steadily increasing over the past 20 years. This increase has been primarily driven by opioid and stimulant use. Despite its prevalence, drug dependence is one of the most stigmatized health conditions. Stigma has myriad negative consequences for its targets, including limiting their access to employment and housing, disrupting interpersonal relationships, harming physical and mental health, and reducing help-seeking. However, because research on stigma toward people with substance use disorders (SUDs) is relatively sparse compared with research on stigma toward other mental illnesses, the field lacks a comprehensive understanding of the causes and consequences of SUD stigma. Moreover, it remains unclear how, if at all, these factors differ from other types of mental illness stigma. The goal of this review is to take stock of the literature on SUD stigma, providing a clear set of foundational principles and a blueprint for future research and translational activity.

Emotional Distress in a Marginalized Population as a Function of Household-Level Social Determinants of Health

Low-income, underrepresented communities of color are disproportionally affected by emotional distress. Little is known about malleable, household-level determinants of emotional distress, addressable by feasible, stigma-neutral interventions. The present study addressed this knowledge gap by analyzing secondary data from a cross-sectional community needs assessment survey in a marginalized urban community (N = 677). Relying on dominance analyses, authors found that, on average, the largest household-level contributions to respondents' emotional distress included exposures to fellow household members' alcohol use and anger-driven behaviors. Both determinants are arguably feasible to address via household-level interventions and community-level preventive efforts. Household members' physical and serious mental illness and drug use were moderately associated with respondents' emotional distress; household cohesion and communications, residential overcrowding, and child behavior played a minimal role. Article concludes with a discussion of public health implications of the results.

Anti-stigma narratives and emotional comfort against health crisis: a context analysis of UGC short videos from patients with COVID-19 infections

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields.

Inequities Gone or Enduring? Evaluating the Effects of a School-Based Antistigma Intervention on Race/Ethnic and Gender Intersectional Disparities in Mental Illness Stigma

While significant mental illness stigma disparities across race/ethnicity and gender exist, little is known about the efficacy of anti-stigma interventions in reducing these intersectional disparities. We examine the two-year effects of school-based anti-stigma interventions on race/ethnic and gender intersectional stigma disparities among adolescents. An ethnically and socioeconomically diverse sixth grade sample (N = 302) self-completed surveys assessing stigma before randomly receiving an anti-stigma curriculum and/or contact intervention versus no intervention. Surveys were also self-completed two-years post-intervention. Stigma measures assessed general mental illness knowledge/attitudes, awareness/action, and social distance. Stigma towards peers with specific mental illnesses were examined using vignettes-two adolescent characters were described as having bipolar (Julia) and social anxiety (David) disorder. Race/ethnicity and gender were cross-classified into six intersectional groups (Latina/o, Non-Latina/o Black, and Non-Latina/o White girls and boys). Linear regressions adjusting for poverty and mental illness familiarity examined anti-stigma intervention effects across intersectional groups in sixth and eighth grade. The school-based anti-stigma intervention reduced intersectional stigma disparities over the two-year study period. While Non-Latino Black boys and Latino boys/girls reported greater disparities in stigma at baseline compared to Non-Latina White girls, these disparities (14 total) were predominantly eliminated in the two-year follow-up following receipt of the curriculum and contact components to just one remaining disparity post-intervention among Non-Latino Black boys. By identifying differences in how school-based anti-stigma interventions reduce mental illness stigma for unique race/ethnic and gender intersectional groups, we can better understand how to shape future anti-stigma interventions for diverse intersectional populations.

Let's put mental health problems and related issues appropriately in social media: A voice of psychiatric nurses

Social media is one convenient way to express ourselves. Much information is offered; most is difficult to filter and can be consumed by anyone, anywhere, anytime. However, sometimes it crosses the boundaries of someone else's life or privacy, especially when discussing sensitive issues, such as mental health problems. There are a lot of discussions about whether bringing the personal experiences of people with mental health problems to the public domain can potentially increase the community's attitudes toward them or not. Still, one thing is for sure, this kind of content has caught public attention by having more viewers. Unfortunately, it potentially brings other consequences for people with mental health problems, such as stigmatization, discrimination, and sadfishing. Therefore, this paper aims to provide the viewpoints of psychiatric nurses regarding how to address mental health-related issues and appropriately put content about mental health problems on social media.

Assessment of beliefs and attitudes about electroconvulsive therapy posted on Twitter: An observational study

BACKGROUND

Electroconvulsive therapy (ECT) is an effective and safe medical procedure that mainly indicated for depression, but is also indicated for patients with other conditions. However, ECT is among the most stigmatized and controversial treatments in medicine. Our objective was to examine social media contents on Twitter related to ECT to identify and evaluate public views on the matter.

METHODS

We collected Twitter posts in English and Spanish mentioning ECT between January 1, 2019 and October 31, 2020. Identified tweets were subject to a mixed method quantitative-qualitative content and sentiment analysis combining manual and semi-supervised natural language processing machine-learning analyses. Such analyses identified the distribution of tweets, their public interest (retweets and likes per tweet), and sentiment for the observed different categories of Twitter users and contents.

RESULTS

"Healthcare providers" users produced more tweets (25%) than "people with lived experience" and their "relatives" (including family members and close friends or acquaintances) (10% combined), and were the main publishers of "medical" content (mostly related to ECT's main indications). However, more than half of the total tweets had "joke or trivializing" contents, and such had a higher like and retweet ratio. Among those tweets manifesting personal opinions on ECT, around 75% of them had a negative sentiment.

CONCLUSIONS

Mixed method analysis of social media contents on Twitter offers a novel perspective to examine public opinion on ECT, and our results show attitudes more negative than those reflected in studies using surveys and other traditional methods.

Promoting young people's mental health: the role of community nurses

There are growing concerns about the mental health and well-being of young people, including how these have been negatively affected by factors such as the coronavirus disease 2019 (COVID-19) pandemic and social media. Community nurses are in an ideal position to promote positive mental health and ensure timely referral to appropriate services to enable young people to access the support they need. This article explores how the pandemic and social media have affected young people's mental health, particularly in relation to anxiety. It also explains how nurses can discuss these issues with young people and their parents or guardians.

Using Twitter to Assess Stigma to Schizophrenia and Psychosis: A Qualitative Study

OBJECTIVE

The aim of the study was to evaluate stigmatizing attitudes towards schizophrenia among Turkish Twitter users.

METHODS

In the study designed with the qualitative research method, the tweets containing the keywords "schizophrenia", "schizophrenic", "psychotic" and "psychosis" in Turkish on Twitter were collected using the Knime program. The main themes and sub-themes were created by content analysis.

RESULTS

The studies revealed three major themes: "insult", "negative point of view", and "anti-stigma". While the sub-themes of "swearing" and "mocking" were determined under the main theme of "insult", the sub-theme of "false beliefs" was determined under the theme of "negative point of view", and the sub-themes of "medically appropriate" and "defensive" were determined under the main theme of "antistigma". In the results, it was determined that the word schizophrenia was commonly used to humiliate others and used as a way of addressing with slang words or to mock and that there were stigmatizing statements revealing negative feelings and thoughts in such a way that they would be inconsistent with medical information.

CONCLUSION

The results of this study can be used to develop programs to combat stigma against schizophrenia disorder and to determine content.

Mpox panic, infodemic, and stigmatization of the 2SLGBTQIAP+ community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database (Preprint)

BACKGROUND

The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community.

OBJECTIVE

The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)-related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination.

METHODS

In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets' main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets' and Facebook posts' languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries.

RESULTS

The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04).

CONCLUSIONS

The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.

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.

Intertextuality and trivialisation in subcultural depictions of violence and criminality related to mental disorders: the case of Spanish punk music

Previous research remarks on the role of the mass media in shaping our world-view and values. It is relevant for the psychiatric field since the literature suggests that the media and artistic representations emphasise violent and criminal behaviours of people with mental disorders. In contrast to the study of other artistic manifestations, depictions in music are much less explored. This article examines the subcultural portrayals of psychiatry-related violent and criminal behaviours in Spanish popular music; particularly, the dimensions of intertextuality and trivialisation. These aspects are relevant since trivialisation may contribute to a distorted and oversimplified view of mental disorders, while intertextuality can play a role in the dissemination, amplification and reinforcement of social beliefs regarding psychiatric problems.

Development, Validation, and Utilization of a Social Media Use and Mental Health Questionnaire among Middle Eastern and Western Adults: A Pilot Study from the UAE

OBJECTIVES

We aimed to develop and validate a mental health stigma measurement tool for use within the social media context, utilizing the tool to assess whether the stigma shown in face-to-face interactions translates to social media, coupled with comparing whether social media use can cause the stigma among a sample of Middle Eastern and Western populations.

METHODS

The development and validation phase comprised a systematic process that was used to develop an assessment tool that could be used within the social media context and establish its validity and reliability. A 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree) was developed to assess mental health stigma. The anonymous questionnaire was distributed from June 2022 to August 2022 on various social media platforms and groups predominated by the two demographics of interest, enrolling 1328 participants (with only 1001 responses deemed valid). The utilization phase consisted of bivariate and multivariable analysis of the data. The cutoff points for low, medium, and high scores were the 25th, 50th, and 75th percentil, respectively.

RESULTS

The instrument comprised three dimensions: acceptance, intolerance, and digital care sentiment. In the Middle Eastern subset of participants, a higher score of intolerance (more stigma) toward mental illness was found in 72.4% of the participants, with a higher score of acceptance being 35.1% and of digital care sentiment being 46.4%. The mean scores for all the scales were as follows: intolerance (3.08 ± 0.64), acceptance (3.87 ± 0.71), and digital care sentiment (3.18 ± 0.69). For Westerners, a higher score of intolerance toward mental illness was found in 24.0% of the participants, with a higher score of acceptance being 56.8% and of digital care sentiment being 38.2%. The mean scores for all the scales were as follows: intolerance (2.28 ± 0.73), acceptance (4.21 ± 0.61), and digital care sentiment (3.08 ± 0.62). Various results were obtained regarding the effect of individual social media platforms on the different subscales.

CONCLUSIONS

Stigma does follow people on social media, whether they are Middle Easterners or Westerners, although to varying degrees. The results of social media interaction and activity varied based on the group that used them, with some having an impact on one group but not the other. For these reasons, proper guidance is advised when utilizing and interacting with social media platforms.

How threatening are people with mental disability? it depends on the type of threat and the disability

There is clear evidence that people with mental disability suffer from discrimination at school, at work, and in society. Less is known about the psychological processes and perceptions that guide such behaviors and even less if these perceptions vary according to the type of disability. Our objective was to build on well-established social psychological models and investigate the perceptions (i.e., stereotypes, perceived threats, and emotions) of people towards different types of mental disability. Participants from two francophone countries completed a questionnaire measuring their perceptions towards 18 mental disabilities and their familiarity with disability (N = 560). As expected, results revealed heterogeneous perceptions across groups. Moreover, perceived threats mediated the link between the stereotype of warmth and emotions. Surprisingly, greater familiarity with mental disability went along with greater derogation. This research nuances the overly generalized perceptions often associated with mental disability. We discuss implications for the reduction of discrimination against people with mental disability.

The Stigma Toward Dementia on Twitter: A Sentiment Analysis of Dutch Language Tweets

People living with dementia are often faced with attitudes indicating stigma. Social media platforms, such as Twitter, can allow for self-expression and support, but can also be used to disseminate misinformation, which can reinforce existing stigma. In the present study, we explore whether the stigma toward dementia is present in Dutch language tweets. In total, 969 tweets containing dementia-related keywords were collected during a period of five months in 2019 and 2020. These were analyzed by means of a sentiment analysis, which we approached as a classification task. The tweets were coded into seven dimensions, i.e., information, joke, metaphor, organization, personal experience, politics, and ridicule, using a semi-automatic machine learning approach. The emerging correlations with our use of Linguistic Inquiry and Word Count software for sentiment analysis validate our approach. In the present study, 9.29% of tweets contain ridicule, propagating stigmatic attitudes on Twitter.

'Maybe I Shouldn't Talk': The Role of Power in the Telling of Mental Health Recovery Stories

Mental health 'recovery narratives' are increasingly used within teaching, learning and practice environments. The mainstreaming of their use has been critiqued by scholars and activists as a co-option of lived experience for organisational purposes. But how people report their experiences of telling their stories has not been investigated at scale. We present accounts from 71 people with lived experience of multiple inequalities of telling their stories in formal and informal settings. A reflexive thematic analysis was conducted within a critical constructivist approach. Our overarching finding was that questions of power were central to all accounts. Four themes were identified: (1) Challenging the status quo; (2) Risky consequences; (3) Producing 'acceptable' stories; (4) Untellable stories. We discuss how the concept of narrative power foregrounds inequalities in settings within which recovery stories are invited and co-constructed, and conclude that power imbalances complicate the seemingly benign act of telling stories of lived experience.

The impact of social media coverage on attitudes towards mental illness and violent offending

The aim of this study is to better understand stigma towards individuals with mental illness who commit violent offences, and examine ways to mitigate the negative impact of social media news stories of schizophrenia and violent offending. Psychology undergraduate students (N = 255) were exposed to Instagram images and captions of recent real news stories of violent offending by individuals with schizophrenia. In the experimental condition, contextual clinical explanatory information was integrated. Pre- and post-measures of stigma were completed. There was a significant increase in negative attitudes towards individuals with mental illness who committed violent offences following the no-context condition, which was clearly mitigated in the experimental condition where context was provided. In both conditions, there were significant increases in intended social-distancing behaviours towards and perceptions of dangerousness of individuals with schizophrenia, and negative beliefs about mental illness more generally. There appears to be utility in incorporating knowledge-based clinical information to mitigate some facets of stigma.

Social media and schizophrenia: An update on clinical applications

Social media has redesigned the landscape of human interaction, and data obtained through these platforms are promising for schizophrenia diagnosis and management. Recent research shows mounting evidence that machine learning analysis of social media content is capable of not only differentiating schizophrenia patients from healthy controls, but also predicting conversion to psychosis and symptom exacerbations. Novel platforms such as Horyzons show promise for improving social functioning and providing timely access to therapeutic resources. Social media is also a considerable means to assess and lessen the stigma surrounding schizophrenia. Herein, the relevant literature pertaining to social media and its clinical applications in schizophrenia over the past five years are summarized, followed by a discussion centered on user feedback to highlight future directions. Social media provides valuable contributions to a multifaceted digital phenotype that may improve schizophrenia care in the near future.

Investigating mental health service user views of stigma on Twitter during COVID-19: a mixed-methods study

Background: Mental health stigma on social media is well studied, but not from the perspective of mental health service users. Coronavirus disease-19 (COVID-19) increased mental health discussions and may have impacted stigma.

Objectives: (1) to understand how service users perceive and define mental health stigma on social media; (2) how COVID-19 shaped mental health conversations and social media use.

Methods: We collected 2,700 tweets related to seven mental health conditions: schizophrenia, depression, anxiety, autism, eating disorders, OCD, and addiction. Twenty-seven service users rated them as stigmatising or neutral, followed by focus group discussions. Focus group transcripts were thematically analysed.

Results: Participants rated 1,101 tweets (40.8%) as stigmatising. Tweets related to schizophrenia were most frequently classed as stigmatising (411/534, 77%). Tweets related to depression or anxiety were least stigmatising (139/634, 21.9%). A stigmatising tweet depended on perceived intention and context but some words (e.g. “psycho”) felt stigmatising irrespective of context.

Discussion: The anonymity of social media seemingly increased stigma, but COVID-19 lockdowns improved mental health literacy. This is the first study to qualitatively investigate service users' views of stigma towards various mental health conditions on Twitter and we show stigma is common, particularly towards schizophrenia. Service user involvement is vital when designing solutions to stigma.

Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments

Background

With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing-related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored.

Objective

This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing-related videos on YouTube by exploring topics discussed and users' attitudes toward these videos.

Methods

We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing-related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing-related videos, as expressed in their comments.

Results

We collected 84,082 comments from the 248 most viewed DTC genetic testing-related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing-related videos.

Conclusions

With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires.

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.

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.

Integrating time into stigma and health research

Stigma changes over time: it waxes and wanes through history, is manifested within humans who develop over time and is tied to statuses (such as attributes, illnesses and identities) that have varying courses. Despite the inherent fluidity of stigma, theories, research and interventions typically treat associations between stigma and health as stagnant. Consequently, the literature provides little insight into when experiences of stigma are most harmful to health and when stigma interventions should be implemented. In this Perspective, we argue that integrating time into stigma research can accelerate progress towards understanding and intervening in associations between stigma and health inequities. We situate time in relation to key concepts in stigma research, identify three timescales that are relevant for understanding stigma (historical context, human development and status course), and outline a time-based research agenda to improve scientists’ ability to understand and address stigma to improve health.

Associations between stigma and health are typically treated as stagnant. In this Perspective, Earnshaw et al. argue that considering stigma in relation to historical, human development and status course timescales can advance progress in understanding and addressing stigma to improve health.

"Mental Health" as Defined by Twitter: Frames, Emotions, Stigma

This study analyzes the general public's framing of 'mental health' and critically assesses the implications of these findings. A mismatch between how people think about mental health and what messages are used in mental health campaigns may hinder attempts to improve mental health awareness and reduce stigma. We have conducted frame analysis by using a combination of topic modeling and sentiment analysis, examining 10 years of mental health-related tweets (n = 695,414). The results reveal seven distinctive mental health frames: 'Awareness', 'Feelings and Problematization', 'Classification', 'Accessibility and Funding', 'Stigma', 'Service', and 'Youth' (arranged by salience). In analyzing these frames, we have learned that (1) the general awareness about mental health relates to mental illness, while health and well-being framing, although present, is prone to low quality of information, (2) mental health discourse is often used to problematize social issues and externalize personal anxieties, which tends toward trivialization and, possibly, treatment delays, (3) mental health discourse often revolves around popularized mental illness (e.g., depression, anxiety, but not neurocognitive diseases), (4) the mental health 'Stigma' frame is not overly pronounced; it revolves around violence, fear, and madness, (5) mental health is frequently politicized, especially concerning gun laws in the US and service accessibility and funding in the UK. Additionally, some narrower frames discovered may warrant further examination. For instance, PTSD is mostly framed around veterans and suicide, ADHD around youth, and substance abuse in relation to women, teens, and impoverished.

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.

Schizophrenia on Turkish Twitter: an exploratory study investigating misuse, stigmatization and trivialization

PURPOSE

This study aims to investigate use and misuse of the word 'schizophrenia' and its derivatives to assess the prevalence of stigmatizing and trivializing attitudes and the meanings attributed to the condition on Turkish Twitter.

METHODS

Using R programming language, we collected Turkish Twitter posts containing the terms used for schizophrenia in Turkish through Twitter's Search API over a 47-day period between July and June 2019. After removing retweets, we randomly sampled 3000 tweets and manually categorized them in three dimensions: use type (metaphorical/non-metaphorical), topic and attitude. Qualitative analysis on representative tweets were performed and word frequencies were calculated.

RESULTS

In total 44,266 tweets were collected and after removing retweets, 24,529 tweets were obtained. Overwhelming majority of the tweets (91.7%) used the terms metaphorically and the majority displayed stigmatizing (68.3%) and trivializing (23%) attitudes. Politics was the most common topic (58.2%) followed by everyday/social chatter (28.5%). Only a small number of tweets were part of awareness campaigns (0.2%) or displayed a supportive attitude (0.8%). Terms were often used metaphorically in a stigmatizing manner as personal or political insults, while in everyday/social contexts, they were used in a trivializing manner to label eccentricity, oddness, overthinking and suspiciousness. Popularity and reach metrics show that these tweets were extensively retweeted, liked and reached millions of users.

CONCLUSION

This is the first study investigating attitudes towards schizophrenia on Turkish Twitter. Significantly higher rates of stigmatizing attitudes demonstrate the urgent need for public health and social awareness campaigns targeting stigma surrounding schizophrenia in Turkey.

Leveraging Neuroscience to Fight Stigma Around Mental Health

Labels serve as identifiers and convenient descriptors of inanimate and animate objects. In humans, given labels can easily become part of an individual’s self-perceived identity. Negative labels ascribed to a person can result in internalized stigma, a state that will shape the subject’s biography. This can ultimately impact the person’s mental and physical health since perceived and/or anticipated stigma discourages the use of social and health services. Per definition, stigma involves labeling of persons with physical, mental, or social characteristics that do not match the observer’s arbitrarily conditioned and calibrated sense of norms (public stigma); such labeling may eventually become embedded in rules, regulations, and laws (structural stigma). Internalized stigma projects onto a person’s emotions and actions. Public (enacted) stigma results from stereotyping (collectively agreed-upon notions about a group of persons that are used to categorize these people) and devaluation, which subsequently leads to social distancing, discrimination, and blatant abuse of human rights. Much of what we know about stigma results from research in the psychosocial sciences and, more recently, from social neuroscience. The stigma around mental health has generated much attention in the field of psychiatry where, to date, most research has focussed on epidemiology and anti-stigma interventions. This essay intends to stimulate thought, debate, and research within the behavioral neuroscience community and, therefore, to inform evidence-based design and implementation of neuroscience-based approaches by other professionals working towards the elimination of the stigma attached to mental illness. The article starts by considering the concept of stigma and the psychological processes that give rise to the phenomenon; it also considers how projected and perceived stigma are multiplied. Finally, after a brief review of the few existing neuroscientific explorations of stigma, gaps in our knowledge of the neurobiological basis of stigma are identified and discussed.

Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning

Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p < 0.001 [95% CI: 0.196–0.273]). Our linear Support Vector Machine was the best performing model with fewest false negatives and higher service user validation. This model identified public stigma in 47% of English tweets (n5,676) which were more negative in sentiment (t (12,143) = 64.38, p < 0.001 [95% CI: 0.29–0.31]). Machine learning can identify stigmatising tweets at large scale, with service user involvement. Given the prevalence of stigma, there is an urgent need for education and online campaigns to reduce it. Machine learning can provide a real time metric on their success.

COVID-19 information received by the Peruvian population, during the first phase of the pandemic, and its association with developing psychological distress: Information about COVID-19 and distress in Peru

It is suspected that the information the population has about coronavirus disease 2019 (COVID-19) determines both its preventive measures and its effects on mental health. The internet and social media are the sources that have largely replaced the official and traditional channels of information. The objective of this study is to analyse the influence of the sources used by the population in Peru to obtain information on COVID-19 and its association with developing psychological distress (PD) and preventive measures against contagion.1699 questionnaires were analysed. A previously validated instrument adapted to Peru was used. Participants were questioned about the information received regarding COVID-19, its sources, time of exposition, assessment, or beliefs about it. Mental health was measured with the Goldberg General Health Questionnaire. Descriptive and bivariate analysis were performed, developing a classification and regression tree for PD based on beliefs and information about the pandemic.The most used source of information on COVID-19 in Peru was social media and this is associated with developing PD, both in the general population and among health professionals. The quality of the information about treatments for COVID-19 is associated with PD in the general population, whereas prognosis generates more distress among healthcare professionals. The biggest concern is transmitting the virus to family members, close persons, or patients, with more confidence in health professionals than in the health system.The health authorities should use the social media to transmit quality information about COVID-19 and, at the same time, to gather in real time the opinions on the implemented preventive measures. For all, this it is necessary to have higher credibility in the population to increase the confidence in the health system, looking at basic aspects for compliance with prevention measures and improvement of mental health.

Areas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study

Background: Antidepressants are the foundation of the treatment of major depressive disorders. Despite the scientific evidence, there is still a sustained debate and concern about the efficacy of antidepressants, with widely differing opinions among the population about their positive and negative effects, which may condition people’s attitudes towards such treatments. Our aim is to investigate Twitter posts about antidepressants in order to have a better understanding of the social consideration of antidepressants. Methods: We gathered public tweets mentioning antidepressants written in English, published throughout a 22-month period, between 1 January 2019 and 31 October 2020. We analysed the content of each tweet, determining in the first place whether they included medical aspects or not. Those with medical content were classified into four categories: general aspects, such as quality of life or mood, sleep-related conditions, appetite/weight issues and aspects around somatic alterations. In non-medical tweets, we distinguished three categories: commercial nature (including all economic activity, drug promotion, education or outreach), help request/offer, and drug trivialization. In addition, users were arranged into three categories according to their nature: patients and relatives, caregivers, and interactions between Twitter users. Finally, we identified the most mentioned antidepressants, including the number of retweets and likes, which allowed us to measure the impact among Twitter users. Results: The activity in Twitter concerning antidepressants is mainly focused on the effects these drugs may have on certain health-related areas, specifically sleep (20.87%) and appetite/weight (8.95%). Patients and relatives are the type of user that most frequently posts tweets with medical content (65.2%, specifically 80% when referencing sleep and 78.6% in the case of appetite/weight), whereas they are responsible for only 2.9% of tweets with non-medical content. Among tweets classified as non-medical in this study, the most common subject was drug trivialization (66.86%). Caregivers barely have any presence in conversations in Twitter about antidepressants (3.5%). However, their tweets rose more interest among other users, with a ratio 11.93 times higher than those posted by patients and their friends and family. Mirtazapine is the most mentioned antidepressant in Twitter (45.43%), with a significant difference with the rest, agomelatine (11.11%). Conclusions: This study shows that Twitter users that take antidepressants, or their friends and family, use social media to share medical information about antidepressants. However, other users that do not talk about antidepressants from a personal or close experience, frequently do so in a stigmatizing manner, by trivializing them. Our study also brings to light the scarce presence of caregivers in Twitter.

Investigation of Carers’ Perspectives of Dementia Misconceptions on Twitter: Focus Group Study

Background

Dementia misconceptions on social media are common, with negative effects on people with the condition, their carers, and those who know them. This study codeveloped a thematic framework with carers to understand the forms these misconceptions take on Twitter.

Objective

The aim of this study is to identify and analyze types of dementia conversations on Twitter using participatory methods.

Methods

A total of 3 focus groups with dementia carers were held to develop a framework of dementia misconceptions based on their experiences. Dementia-related tweets were collected from Twitter’s official application programming interface using neutral and negative search terms defined by the literature and by carers (N=48,211). A sample of these tweets was selected with equal numbers of neutral and negative words (n=1497), which was validated in individual ratings by carers. We then used the framework to analyze, in detail, a sample of carer-rated negative tweets (n=863).

Results

A total of 25.94% (12,507/48,211) of our tweet corpus contained negative search terms about dementia. The carers’ framework had 3 negative and 3 neutral categories. Our thematic analysis of carer-rated negative tweets found 9 themes, including the use of weaponizing language to insult politicians (469/863, 54.3%), using dehumanizing or outdated words or statements about members of the public (n=143, 16.6%), unfounded claims about the cures or causes of dementia (n=11, 1.3%), or providing armchair diagnoses of dementia (n=21, 2.4%).

Conclusions

This is the first study to use participatory methods to develop a framework that identifies dementia misconceptions on Twitter. We show that misconceptions and stigmatizing language are not rare. They manifest through minimizing and underestimating language. Web-based campaigns aiming to reduce discrimination and stigma about dementia could target those who use negative vocabulary and reduce the misconceptions that are being propagated, thus improving general awareness.

Examining Tweet Content and Engagement of Users With Tweets About Hikikomori in Japanese: Mixed Methods Study of Social Withdrawal

BACKGROUND

Hikikomori is a form of severe social withdrawal that is particularly prevalent in Japan. Social media posts offer insight into public perceptions of mental health conditions and may also inform strategies to identify, engage, and support hard-to-reach patient populations such as individuals affected by hikikomori.

OBJECTIVE

In this study, we seek to identify the types of content on Twitter related to hikikomori in the Japanese language and to assess Twitter users' engagement with that content.

METHODS

We conducted a mixed methods analysis of a random sample of 4940 Japanese tweets from February to August 2018 using a hashtag (#hikikomori). Qualitative content analysis included examination of the text of each tweet, development of a codebook, and categorization of tweets into relevant codes. For quantitative analysis (n=4859 tweets), we used bivariate and multivariate logistic regression models, adjusted for multiple comparisons, and estimated the predicted probabilities of tweets receiving engagement (likes or retweets).

RESULTS

Our content analysis identified 9 codes relevant to tweets about hikikomori: personal anecdotes, social support, marketing, advice, stigma, educational opportunities, refuge (ibasho), employment opportunities, and medicine and science. Tweets about personal anecdotes were the most common (present in 2747/4859, 56.53% of the tweets), followed by social support (902/4859, 18.56%) and marketing (624/4859, 12.84%). In the adjusted models, tweets coded as stigma had a lower predicted probability of likes (-33 percentage points, 95% CI -42 to -23 percentage points; P<.001) and retweets (-11 percentage points, 95% CI -18 to -4 percentage points; P<.001), personal anecdotes had a lower predicted probability of retweets (-8 percentage points, 95% CI -14 to -3 percentage points; P=.002), marketing had a lower predicted probability of likes (-13 percentage points, 95% CI -21 to -6 percentage points; P<.001), and social support had a higher predicted probability of retweets (+15 percentage points, 95% CI 6-24 percentage points; P=.001), compared with all tweets without each of these codes.

CONCLUSIONS

Japanese tweets about hikikomori reflect a unique array of topics, many of which have not been identified in prior research and vary in their likelihood of receiving engagement. Tweets often contain personal stories of hikikomori, suggesting the potential to identify individuals with hikikomori through Twitter.

Social media analytics and reachability evaluation - #Diabetes

BACKGROUND AND AIMS

Diabetes as a lifestyle disorder could be effectively managed by creating awareness among people through social media. Understanding the content of Twitter messages will aid in strategizing health communication about diabetes to the community through Twitter. This study aimed to analyze the content, sentiment, and reachability of diabetes related tweets posted in India.

METHODS

Diabetes related messages from India were collected via Twitter's Application Programming Interface for April 2019. Themes and subthemes of tweet content were identified from randomly selected tweets. The tweets were coded as the source, themes, and subthemes manually. Sentiment analysis of the tweets was done by a lexicon-based approach. The reachability of tweets was assessed based on re-tweet and favorite counts.

RESULTS

Out of 1840 tweets, 57.28% were from organizations and 42.72% were from individuals. The largest proportion of tweet messages were informative (50.76%), followed by promotional tweets (21.52%). The largest proportion of tweets were positive (40.4%) followed by neutral (31.14%) tweets. Among the six major themes, the diabetes story had the highest reachability.

CONCLUSIONS

The outcome of this study would aid public health professionals in planning information dissemination and communication regarding diabetes on Twitter so that the right information reaches a wider population.

Mental Health Stigma Reduction in the Midwestern United States: Evidence from a Digital Campaign Using a Collective Impact Model

Addressing mental stigma is a key component of improving mental health outcomes. A digital media campaign was implemented to reduce mental health stigma in the Omaha Metropolitan area. The campaign used evidence-based approaches within a collective impact framework. Two surveys were conducted at baseline and at 10-month follow-up to evaluate the campaign within the Omaha and Council Bluffs intervention region, and a control region in Iowa. Analysis revealed significant improvements in desires for social distance and perceptions toward treatment efficacy within the intervention group. Improvements were seen across measures of personal and community attitudes towards mental health conditions, confidence in supporting others, and likelihood of disclosing a mental health condition. The trends were generally not replicated within the control group. Respondents who were aware of the campaign showed fewer stigmatizing views, including lower desires for social distance, improved attitudes toward treatment, and significant improvements in providing support and caring for their own mental health. The results suggest that the implemented evidenced-based approach could potentially create positive shifts in stigma reduction. This evaluation further supports the potential for scaling and adapting digital media campaigns for stigma reduction in different geographic locations.

Stigma and quality of life among people diagnosed with mental disorders: a Narrative Review

INTRODUCTION

The anti-psychiatric movements that emerged in the early 1960s led to the appearance of stigma in psychiatry. The misunderstanding of the concept of mental disorder, the negative way in which associated hospitalization was perceived, the inclination to treat patients through psychological therapies, and the criticism of pharmacological treatment led to the discrediting of psychiatry.

AIM

The current paper aims to review the available literature regarding the impact of stigma on the quality of life of people diagnosed with mental disorders.

MATERIAL AND METHODS

A narrative review of relevant literature published between 1999 and 2021 was conducted. The authors analysed studies found on PubMed and the Web of Science electronic databases. The search terms combined two overlapping areas with keywords such as "stigma" and "mental disorders". A descriptive analysis was employed to synthesize the obtained data.

RESULTS

Stigma continues to be an important challenge to the management of health conditions in people with mental disorders. A lack of comprehension may give the impression that all psychiatric patients are aggressive and are unable to function adequately. Such stigmatizing beliefs and habits have proven to be very difficult to change.

CONCLUSIONS

Due to the stigmatization and repulsive attitudes in society, patients are reluctant to be linked to any form of mental disorder or to be seen as having any contact with mental health professionals. This undermines the beneficial effects of treatment, resulting in a poor quality of life and diminished socio-occupational functioning.

Play the Pain: A Digital Strategy for Play-Oriented Research and Action

The value of understanding patients' illness experience and social contexts for advancing medicine and clinical care is widely acknowledged. However, methodologies for rigorous and inclusive data gathering and integrative analysis of biomedical, cultural, and social factors are limited. In this paper, we propose a digital strategy for large-scale qualitative health research, using play (as a state of being, a communication mode or context, and a set of imaginative, expressive, and game-like activities) as a research method for recursive learning and action planning. Our proposal builds on Gregory Bateson's cybernetic approach to knowledge production. Using chronic pain as an example, we show how pragmatic, structural and cultural constraints that define the relationship of patients to the healthcare system can give rise to conflicted messaging that impedes inclusive health research. We then review existing literature to illustrate how different types of play including games, chatbots, virtual worlds, and creative art making can contribute to research in chronic pain. Inspired by Frederick Steier's application of Bateson's theory to designing a science museum, we propose DiSPORA (Digital Strategy for Play-Oriented Research and Action), a virtual citizen science laboratory which provides a framework for delivering health information, tools for play-based experimentation, and data collection capacity, but is flexible in allowing participants to choose the mode and the extent of their interaction. Combined with other data management platforms used in epidemiological studies of neuropsychiatric illness, DiSPORA offers a tool for large-scale qualitative research, digital phenotyping, and advancing personalized medicine.

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

INTRODUCTION

Opioid misuse is a public health crisis in the US, and misuse of synthetic opioids such as fentanyl have driven the most recent waves of opioid-related deaths. Because those who misuse fentanyl are often a hidden and high-risk group, innovative methods for identifying individuals at risk for fentanyl misuse are needed. Machine learning has been used in the past to investigate discussions surrounding substance use on Reddit, and this study leverages similar techniques to identify risky content from discussions of fentanyl on this platform.

METHODS

A codebook was developed by clinical domain experts with 12 categories indicative of fentanyl misuse risk, and this was used to manually label 391 Reddit posts and comments. Using this data, we built machine learning classification models to identify fentanyl risk.

RESULTS

Our machine learning risk model was able to detect posts or comments labeled as risky by our clinical experts with 76% accuracy and 76% sensitivity. Furthermore, we provide a vocabulary of community-specific, colloquial words for fentanyl and its analogues.

DISCUSSION

This study uses an interdisciplinary approach leveraging machine learning techniques and clinical domain expertise to automatically detect risky discourse, which may elicit and benefit from timely intervention. Moreover, our vocabulary of online terms for fentanyl and its analogues expands our understanding of online "street" nomenclature for opiates. Through an improved understanding of substance misuse risk factors, these findings allow for identification of risk concepts among those misusing fentanyl to inform outreach and intervention strategies tailored to this at-risk group.

Self-disclosure in public and private groups of people with mental illnesses in Facebook

Purpose

The Internet enables various voices and opinions that previously did not participate in the community discourse to express themselves. People with mental illnesses make use of social networks to advance their special needs in varied ways. The study aims to examine the nature of the discourse that takes place in public and private groups of people with mental illnesses.

Design/methodology/approach

The research corpus consisted of the content of 615 messages taken from public and private groups of people with mental illnesses in Facebook. Linguistic parameters (the total number of words, the number of words in the first person) were examined for each message. Two skilled judges classified the messages on a self-disclosure scale to determine the degree of disclosure of personal information, thoughts and emotions.

Findings

The results of the study indicate that the messages published in public groups are longer than the messages in private groups; however, the level of personal disclosure in messages written in private groups is deeper than in messages written in public groups. In addition, the level of self-disclosure in opening posts was found to be greater than the level of self-disclosure in comments.

Practical implications

In the study, the authors focus on the ways people in excluded populations make use of virtual tools to advance both their personal and social needs.

Originality/value

The study is innovative, as it explores the discourse of people with mental illnesses in public and private groups on Facebook.

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.

Personal and Perceived Stigmas in Adolescents toward Peers with Mental Disorders in West Sumatra Indonesia

BACKGROUND: The number of mental disorders in adolescents tends to increase every year in Indonesia. However, the stigma of mental disorders is a crucial factor that makes teens hide their mental health problem. AIM: This study aimed to examine personal and perceived among adolescents towards peers with mental disorders (PMD) and to identify predictors of these constructs. METHODS: This quantitative study with a cross-sectional design recruited 977 adolescents using a cluster random sampling technique. Adolescent stigma was assessed using the Peer Mental Health Stigmatization Scale (PMHSS). Multivariable general linear models (GLMs) was used for analysis. RESULTS: The study shows that the perceived stigma (M = 36.62, SD = 5,183) tends to be higher than personal stigma (M = 39.49, SD = 5,495).  Higher personal stigma was predicted by a lower level of academic (P < 0.01), lower levels of family monthly income (P < 0.01), and higher perceived stigma (P < 0.001). Higher perceived stigma was predicted by younger age (P < 0.05), lower levels of academic (P < 0.05), higher levels of family monthly income (P < 0.05), and higher personal stigma (P < 0.001). CONCLUSION: The Findings suggest that stigmatization towards PMD is common among adolescents. The development of intervention programs should be directed at reducing negative perceptions of the environment. The identified predictors must also be considered in the development of future anti-stigma programs.