Mental illness and bipolar disorder on Twitter: implications for stigma and social support

Exploring print media coverage of elite athletes' mental illness between 2010 and 2023 in Germany: a quantitative content analysis

Objectives

Recently, the stereotype of elite athletes' invulnerabilty has begun to be challenged by an increasing number of elite athletes who talk openly about struggling with mental health. Relatedly, previous research has focused primarily on specific incidents like the media's portrayal of personal disclosures. The purpose of this study was to expand this perspective and give a systematic overview of media coverage related to elite athletes' mental illness by examining more than one decade (2010-2023) of German print media reporting. Specifically, we were interested in changes over time and between broadsheet and tabloid press regarding content-related and formal aspects.

Methods

Based on a systematic search and screening process in eleven German newspapers and magazines, 699 print media articles were analyzed with a codebook, forming a framework of content-related (reported mental disorder; central thematic focus; sources of comments and quotations; perspectives on the high-performance sports system) and formal categories (article genre; elements of responsible journalism). Univariate analyses and binary logistic regression models were used to examine changes over time (2010-2016 vs. 2017-2023) and differences between types of press (tabloid vs. broadsheet press) regarding content-related and formal characteristics.

Results

The results indicate an enhanced awareness towards the topic of mental illness and those affected in recent years within German print media. This was demonstrated by the increased integration of responsible reporting elements, the inclusion of diversified perspectives and the considerate selection of content. Despite this positive trend over time, the findings also suggest that media reporting in the tabloid press bears an increased risk for inappropriate storytelling, focusing primarily on personal tragedies.

Conclusion

As personal fate of prominent figures like elite athletes will always meet great interest in the public, it is of utmost importance that the media report responsibly and promote critical thinking in society. The study shows the media's willingness to question conventional ideals embedded in the sports culture and take a more critical approach to the topic of mental illness in high-performance sports. By demonstrating a greater understanding of the importance and the seriousness of the issue, the media might also contribute to improved mental health awareness in society.

Machine learning and natural language processing to assess the emotional impact of influencers' mental health content on Instagram

Background

This study aims to examine, through artificial intelligence, specifically machine learning, the emotional impact generated by disclosures about mental health on social media. In contrast to previous research, which primarily focused on identifying psychopathologies, our study investigates the emotional response to mental health-related content on Instagram, particularly content created by influencers/celebrities. This platform, especially favored by the youth, is the stage where these influencers exert significant social impact, and where their analysis holds strong relevance. Analyzing mental health with machine learning techniques on Instagram is unprecedented, as all existing research has primarily focused on Twitter.

Methods

This research involves creating a new corpus labelled with responses to mental health posts made by influencers/celebrities on Instagram, categorized by emotions such as love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness. The study is complemented by modelling a set of machine learning algorithms to efficiently detect the emotions arising when faced with these mental health disclosures on Instagram, using the previous corpus.

Results

Results have shown that machine learning algorithms can effectively detect such emotional responses. Traditional techniques, such as Random Forest, showed decent performance with low computational loads (around 50%), while deep learning and Bidirectional Encoder Representation from Transformers (BERT) algorithms achieved very good results. In particular, the BERT models reached accuracy levels between 86-90%, and the deep learning model achieved 72% accuracy. These results are satisfactory, considering that predicting emotions, especially in social networks, is challenging due to factors such as the subjectivity of emotion interpretation, the variability of emotions between individuals, and the interpretation of emotions in different cultures and communities.

Discussion

This cross-cutting research between mental health and artificial intelligence allows us to understand the emotional impact generated by mental health content on social networks, especially content generated by influential celebrities among young people. The application of machine learning allows us to understand the emotional reactions of society to messages related to mental health, which is highly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms' high accuracy (86-90%) in social contexts like mental health, where detecting negative emotions is crucial, presents a promising research avenue. Achieving such levels of accuracy is highly valuable due to the significant implications of false positives or false negatives in this social context.

How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub

BACKGROUND

Social media, including online health communities (OHCs), are widely used among both healthy people and those with health conditions. Platforms like Twitter (recently renamed X) have become powerful tools for online mental health communities (OMHCs), enabling users to exchange information, express feelings, and socialize. Recognized as empowering processes, these activities could empower mental health consumers, their families and friends, and society. However, it remains unclear how OMHCs empower diverse population levels and groups.

OBJECTIVE

This study aimed to develop an understanding of how empowerment processes are conducted within OMHCs on Twitter by identifying members who shape these communities, detecting the types of empowerment processes aligned with the population levels and groups outlined in Strategy 1 of the Integrated People-Centred Health Services (IPCHS) framework by the World Health Organization (WHO), and clarifying members' involvement tendencies in these processes.

METHODS

We conducted our analysis on a Twitter OMHC called #bipolarclub. We captured 2068 original tweets using its hashtag #bipolarclub between December 19, 2022, and January 15, 2023. After screening, 547 eligible tweets by 182 authors were analyzed. Using qualitative content analysis, community members were classified by examining the 182 authors' Twitter profiles, and empowerment processes were identified by analyzing the 547 tweets and categorized according to the WHO's Strategy 1. Members' tendencies of involvement were examined through their contributions to the identified processes.

RESULTS

The analysis of #bipolarclub community members unveiled 5 main classifications among the 182 members, with the majority classified as individual members (n=138, 75.8%), followed by health care-related members (n=39, 21.4%). All members declared that they experience mental health conditions, including mental health and general practitioner members, who used the community as consumers and peers rather than for professional services. The analysis of 547 tweets for empowerment processes revealed 3 categories: individual-level processes (6 processes and 2 subprocesses), informal carer processes (1 process for families and 1 process for friends), and society-level processes (1 process and 2 subprocesses). The analysis also demonstrated distinct involvement tendencies among members, influenced by their identities, with individual members engaging in self-expression and family awareness support and health care-related members supporting societal awareness.

CONCLUSIONS

The examination of the #bipolarclub community highlights the capability of Twitter-based OMHCs to empower mental health consumers (including those from underserved and marginalized populations), their families and friends, and society, aligning with the WHO's empowerment agenda. This underscores the potential benefits of leveraging Twitter for such objectives. This pioneering study is the very first to analyze how a single OMHC can empower diverse populations, offering various health care stakeholders valuable guidance and aiding them in developing consumer-oriented empowerment programs using such OMHCs. We also propose a structured framework that classifies empowerment processes in OMHCs, inspired by the WHO's Strategy 1 (IPCHS framework).

"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.

Public perception of psychiatry, psychology and mental health professionals: a 15-year analysis

Background

X (previously known as "Twitter") serves as a platform for open discussions on mental health, providing an avenue for scrutinizing public perspectives regarding psychiatry, psychology and their associated professionals.

Objective

To analyze the conversations happening on X about psychiatrists, psychologists, and their respective disciplines to understand how the public perception of these professionals and specialties has evolved over the last 15 years.

Methods

We collected and analyzed all tweets posted in English or Spanish between 2007 and 2023 referring to psychiatry, psychology, neurology, mental health, psychiatrist, psychologist, or neurologist using advance topic modelling and sentiment analysis.

Results

A total of 403,767 tweets were analyzed, 155,217 (38%) were in English and 248,550 (62%) in Spanish. Tweets about mental health and mental health professionals and disciplines showed a consistent volume between 2011 and 2016, followed by a gradual increase from 2016 through 2022. The proportion of tweets discussing mental health doubled from 2016 to 2022, increasing from 20% to 67% in Spanish and from 15% to 45% in English. Several differences were observed on the volume of tweets overtime depending on the language they were written. Users associated each term with varied topics, such as seeking for help and recommendation for therapy, self-help resources, medication and side effects, suicide prevention, mental health in times of crisis, among others. The number of tweets mentioning these topics increased by 5-10% from 2016 to 2022, indicating a growing interest among the population. Emotional analysis showed most of the topics were associated with fear and anger.

Conclusion

The increasing trend in discussions about mental health and the related professionals and disciplines over time may signify an elevated collective awareness of mental health. Gaining insights into the topics around these matters and user's corresponding emotions towards them presents an opportunity to combat the stigma surrounding mental health more effectively.

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.

Bipolar Disorder in Social Media: An Examination of Instagram's Role in Disseminating Accurate Information

Introduction Bipolar disorder is a chronic and recurring condition marked by fluctuations in both energy and mood that can be debilitating to individuals without treatment. While physicians clinically diagnose the condition, patients frequently seek information from alternate channels. Social media platforms, such as Instagram, have facilitated more convenient access to supplementary information about bipolar disorder. Nevertheless, there is apprehension regarding the accuracy of publicly disseminated information through these platforms. The aim of this study is to evaluate the accuracy and dependability of information about Bipolar disorder found on the social media platform, Instagram. Methodology A cross-sectional observational study was conducted by gathering data from Instagram posts linked with popular bipolar disorder hashtags. To evaluate the credibility of the chosen entries, numerical ratings were assigned to each post using the established measurement scales of the Global Quality Score and Reliability Score. Results After analyzing 196 Instagram entries about Bipolar Disorder that fulfilled inclusion criteria, the study revealed that 70.4% (n=138) of these posts were shared by individuals diagnosed with bipolar disorder. Among the content posted by these individuals, a statistically significant global quality score of 2 and a reliability score of 1 were observed. Conclusions Verified medical information of superior global quality should be shared on social media platforms by accountable parties. Individuals with further inquiries about medical knowledge should be advised to reach out to local physicians.

A Randomized Clinical Trial to Assess the Effect of Medication Therapy Plus tDCS on Problem-solving and Emotion Regulation of Patients with Bipolar Disorder Type I

Objective

This study aims to evaluate the effectiveness of medication therapy combined with transcranial Direct Current Stimulation (tDCS) in improving problem-solving and emotion regulation abilities of patients with bipolar disorder (BD) type I.

Methods

This is a randomized clinical trial conducted on 30 patients with BD I, randomly assigned into two groups of Medication (n = 15, receiving mood stabilizers including 2-5 tablets of lithium 300 mg, sodium valproate 200 mg, and carbamazepine 200 mg) and Medication + tDCS (n = 15, receiving mood stabilizers plus tDCS with 2 mA intensity over the right dorsolateral prefrontal cortex for 10 days, two sessions per day each for 20 minutes). The Tower of London (TOL) test and Emotion Regulation Questionnaire (ERQ) were used for assessments before, immediately, and 3 months after interventions.

Results

There was a significant difference between groups in total ERQ (p = 0.001) and its cognitive reappraisal domain (p = 0.000) which were increased, but the difference was not significant in its expressive suppression domain (p > 0.05). After 3 months, their level decreased. In examining problem-solving variable, the combined therapy could significantly reduce only the total number of errors under TOL test (p = 0.00), but it remained unchanged after 3 months.

Conclusion

Medication therapy plus tDCS is effective in improving problem-solving and emotional regulation (cognitive reappraisal) skills of patients with BD I.

"Follow to *actually* heal binge eating": A mixed methods textual content analysis of #BEDrecovery on TikTok

Binge eating disorder (BED) has been relatively overlooked in research on eating disorders and social media. Existing literature suggests that time spent on social media may be associated with increased binge eating. However, more granular details of social media experiences such as the consumption of pro-recovery content have not received sufficient scholarly attention. The present study begins to address this gap through analysis of 1074 captions from public posts on TikTok, a video-based social media platform, tagged with #BEDrecovery between July 2021-2022. We generated six themes by examining word frequencies in the data and engaging in reflexive categorization of commonly used words within the context of different posts. These themes were: (1) diets and eating approaches, (2) help and support, (3) mental health, (4) diet culture critique, (5) body monitoring, and (6) fitness. To understand which videos in the BED recovery community had the broadest reach, we also examined how themes were associated with user engagement - concretely, the number plays (times the post was watched) and shares (times users shared a link to the post with other TikTok users). Notably, we found that the number of shares was significantly lower in posts that included diet culture critique than in those that did not. By contrast plays and shares were higher in posts with body monitoring than in those without. Our findings suggest that highly engaged with #BEDrecovery TikTok content may include the promotion of diet culture and potentially create an unproductive environment for individuals seeking BED recovery support.

Promoting Mental Health on Social Media: A Content Analysis of Organizational Tweets

This study analyzed tweets posted over 1 year from three mental-health organizations in the United States, along with audience engagement data of comments, retweets, and likes. The results revealed that tweets focused on mental illnesses or mental health received more engagement than those that emphasized event promotion or relationship building. In addition, there were more gain-framed than loss-framed messages, although the latter triggered more public engagement. Thematic framing was used more frequently than episodic framing and related to higher levels of engagement. Call-to-action (CTA), other audience engaging methods and multimedia strategies were used in various frequencies in these tweets; and the use of CTA, other engaging methods, photos/pictures, and external links, but not videos, were positively related to public engagement. Theoretical contributions and practical implications regarding using social media for mental health promotion were offered.

Qualitative and Artificial Intelligence-based Sentiment Analyses of Anti-LGBTI+ Hate Speech on Twitter in Turkey

The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were evaluated by sentiment analysis. The researchers then performed a qualitative analysis of the most frequently liked and retweeted tweets (n = 556). Sentiment analysis revealed that 69.5% of tweets were negative, 23.3% were neutral, and 7.2% were positive. The qualitative analysis was grouped under seven themes: LGBTI+ Club; Terrorism and Terrorist Organization Membership; Perversion, Illness, Immorality; Presence in History; Religious References; Insults; and Humiliation. The results of this study show that anti-LGBTI+ hate speech in Turkey is significant in terms of both quality and quantity. As LGBTI+ individuals are at risk for excess mental distress and disorders, it is important to understand the risks and other factors that ameliorate stress and contribute to mental health in social media.

"As if we are branded with the mark of Cain": stigma, guilt, and shame experienced by COVID-19 survivors in Israel - a qualitative study

Stigma is associated with harmful health outcomes, and it fuels social and health inequalities. It can undermine social cohesion and encourage social exclusion of groups, which may contribute to secrecy about disease symptoms, avoidance of disease testing and vaccination, and further spread of a contagious illness. Stigmatization is a social process set to exclude those who are perceived to be a potential source of disease and may pose a threat to effective interpersonal and social relationships. In this qualitative study, we delved into the stigmatization experiences of twenty COVID-19 recovered patients during the COVID-19 first wave, using in-depth semi-structured interviews conducted during November 2020. Using thematic analysis, we found that the process of stigmatization was all-encompassing, from the stage of diagnosis throughout the duration of the disease and the recovery phases. On the basis of the data, we hypothesized that stigma is a significant public health concern, and effective and comprehensive interventions are needed to counteract the damaging and insidious effects during infectious disease pandemics such as COVID-19, and reduce infectious disease-related stigma. Interventions should address provision of emotional support frameworks for the victims of stigmatization and discrimination that accompany the COVID-19 pandemic and future pandemics. This study was conducted in the early days of the COVID-19 pandemic, when uncertainty about the disease was high and fear of contamination fueled high levels of stigmatization against those who became ill with Covid-19.

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.

Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study

BACKGROUND

Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January.

OBJECTIVE

We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets?

METHODS

We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest.

RESULTS

We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters.

CONCLUSIONS

The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Social Media Use and Well-being With Bipolar Disorder During the COVID-19 Pandemic: Path Analysis

Background

Reliable and consistent social support is associated with the mental health and well-being of persons with severe mental illness, including bipolar disorder (BD). Yet the COVID-19 pandemic and associated social distancing measures (eg, shelter in place) reduced access to regular social contacts, while social media use (SMU) increased concomitantly. Little is currently known about associations between the well-being of adults with BD and different types of SMU (eg, passive and active).

Objective

For this study, we had two goals. First, we report descriptive information regarding SMU by persons with BD during COVID-19 (all platforms). Specific to Facebook, we next developed and tested a hypothesized model to identify direct and indirect associations between BD symptoms, social support, loneliness, life satisfaction, and SMU. Responses were collected during the global spread of the Delta variant and prior/concurrent with the Omicron variant, 20 months after the World Health Organization declared COVID-19 a global pandemic.

Methods

Over 8 weeks, we obtained responses from an international sample of 102 adults with BD using the Qualtrics online platform. Most had previously participated in the BADAS (Bipolar Affective Disorders and older Adults) Study (n=89, 87.3%); the remainder were recruited specifically for this research (n=13, 2.7%). The subsamples did not differ in age (t₁₀₀=1.64; P=.10), gender (χ²₂=0.2; P=.90), socioeconomic status (χ²₆=9.9; P=.13), or time since BD diagnosis (t₉₇=1.27; P=.21). Both were recruited using social media advertising micro-targeted to adults with BD. On average, participants were 53.96 (SD 13.22, range 20-77) years of age, they had completed 15.4 (SD 4.28) years of education, and were diagnosed with BD 19.6 (SD 10.31) years ago. Path analyses were performed to develop and test our hypothesized model.

Results

Almost all participants (n=95, 93.1%) reported having both Facebook and LinkedIn accounts; 91.2% (n=93) reported regular use of either or both. During the pandemic, most (n=62, 60.8%) reported accessing social media several times a day; 36.3% (n=37) reported using social media more often since the emergence of COVID-19. Specific to Facebook, the model we hypothesized differed somewhat from what emerged. The resulting model suggests that symptoms of depression predict loneliness and, inversely, social support and life satisfaction. Social support predicts social Facebook use, whereas passive Facebook use predicts life satisfaction. Symptoms of depression emerged as indirect predictors of SMU via social support.

Conclusions

Our findings suggest that the operational definition of passive-active SMU requires further analysis and refinement. In contrast to theory, passive Facebook use appears positively associated with well-being among certain populations. Longitudinal data collection over multiple points is required to identify associations between BD symptoms, SMU, and well-being over time.

The window to the world for individuals with mental disorders: A qualitative study about social media

This study aimed to determine the views of individuals with mental disorders on the experience of social media. This was a qualitative study conducted using the content analysis method. Using purposive sampling, 12 individuals with mental disorders were selected and interviewed. Data were collected using semi-structured interviews and were analyzed using the content analysis method. Four main themes and 10 subthemes were identified. The themes included the window opening to the world (source of information, facilitating life), from invisibility to visibility (feeling good, liberation and socialization), negative experiences (feeling lost, envy, and privacy), and the rejection of society (escape and stigmatization). Individuals with mental disorders stated that social media had both positive as well as negative effects on their lives. The results of the study highlight the various aspects of social media use and its effects on individuals with mental disorders. These results can be used in planning and implementing mental health services for individuals with mental disorders. ACCESSIBLE SUMMARY: What is known about the subject? What does the paper add to existing knowledge? What are the implications for the practice?

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.

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.

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

BACKGROUND

Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature.

OBJECTIVE

This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods.

METHODS

A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology.

RESULTS

Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies.

CONCLUSIONS

The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

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.

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.

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.

Using TikTok in recovery from substance use disorder

BACKGROUND

There are many effective treatment options for substance use disorder (SUD), yet most individuals with SUD do not seek formal treatment services. Given the rising popularity of TikTok and need to foster innovative means through which to attract and engage individuals with SUD with treatment, we sought to characterize how TikTok users in SUD recovery are using this platform to bolster their recovery support and/or give hope to others who are struggling with substance use.

METHODS

Our sample consisted of 82 of the most liked TikTok videos related to attempts to cut down on or abstain from substances and/or strengthen SUD recovery. We employed an iterative process to codebook development resulting in codes for demographics, user-sentiment, video type, and mechanisms of recovery-related behavior change. Videos were independently double-coded and evaluated for inter-rater reliability.

RESULTS

Video in this sample were heavily viewed, accounting for over 2 million views per video and 325,000 likes on average. Most common video themes were sharing a journey from active SUD to recovery (40.2%) and sharing/celebrating a recovery milestone (37.8%), followed by recurrence of substance use (12.2%). Commonly exemplified mechanisms of recovery-related behavior change included embracing a strong social identity as a person in recovery (81.7%), social support (45.1%), and participation in rewarding alternative activities (39.0%).

CONCLUSION

TikTok SUD recovery-focused videos can potentially reach millions with portrayed themes similar to established therapeutic mobilizers and mechanisms. More research is needed to better understand whether digital recovery narratives can effectively normalize experiences of addiction and help-seeking behaviors.

A Twitter discourse analysis of negative feelings and stigma related to NAFLD, NASH and obesity

BACKGROUND

People with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are stigmatized, partly since 'non-alcoholic' is in the name, but also because of obesity, which is a common condition in this group. Stigma is pervasive in social media and can contribute to poorer health outcomes. We examine how stigma and negative feelings concerning NAFLD/NASH and obesity manifest on Twitter.

METHODS

Using a self-developed search terms index, we collected NAFLD/NASH tweets from May to October 2019 (Phase I). Because stigmatizing NAFLD/NASH tweets were limited, Phase II focused on obesity (November-December 2019). Via sentiment analysis, >5000 tweets were annotated as positive, neutral or negative and used to train machine learning-based Natural Language Processing software, applied to 193 747 randomly sampled tweets. All tweets collected were analysed.

RESULTS

In Phase I, 16 835 tweets for NAFLD and 2376 for NASH were retrieved. Of the annotated NAFLD/NASH tweets, 97/1130 (8.6%) and 63/535 (11.8%), respectively, related to obesity and 13/1130 (1.2%) and 5/535 (0.9%), to stigma; they primarily focused on scientific discourse and unverified information. Of the 193 747 non-annotated obesity tweets (Phase II), the algorithm classified 40.0% as related to obesity, of which 85.2% were negative, 1.0% positive and 13.7% neutral.

CONCLUSIONS

NAFLD/NASH tweets mostly indicated an unmet information need and showed no clear signs of stigma. However, the negative content of obesity tweets was recurrent. As obesity-related stigma is associated with reduced care engagement and lifestyle modification, the main NAFLD/NASH treatment, stigma-reducing interventions in social media should be included in the liver health agenda.

Stigmatisation associated with COVID-19 in the general Colombian population

BACKGROUND

As the COVID-19 pandemic progresses, the fear of infection increases and, with it, the stigma-discrimination, which makes it an additional problem of the epidemic. However, studies about stigma associated with coronavirus are scarce worldwide.

AIMS

To determine the association between stigmatisation and fear of COVID-19 in the general population of Colombia.

METHOD

A cross-sectional study was carried out. A total of 1,687 adults between 18 and 76 years old (M = 36.3; SD = 12.5), 41.1% health workers, filled out an online questionnaire on Stigma-Discrimination and the COVID-5 Fear Scale, adapted by the research team.

RESULTS

The proportion of high fear of COVID-19 was 34.1%; When comparing the affirmative answers to the questionnaire on stigma-discrimination towards COVID-19, it was found that the difference was significantly higher in the general population compared to health workers in most of the questions evaluated, which indicates a high level of stigmatisation in that group. An association between high fear of COVID-19 and stigma was evidenced in 63.6% of the questions in the questionnaire.

CONCLUSION

Stigma-discrimination towards COVID-19 is frequent in the Colombian population and is associated with high levels of fear towards said disease, mainly people who are not health workers.

Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution

Few studies have mined social media platforms to assess environmental concerns. In this study, Twitter was scraped to obtain a ~140,000 tweet dataset related specifically to marine plastic pollution. The goal is to understand what kind of users profiles are tweeting and how and when they do it. In addition, topic modelling and graph theory techniques have allowed us to identify main concerns on this topic: i) impact on wildlife, ii) microplastics/water pollution, iii) estimates/reports, iv) legislation/protection, and v) recycling/cleaning initiatives. Results reveal a scarce influence of organizations involved in research and marine environmental awareness, so some guidelines are depicted that could help to adjust their communication plans. This is relevant to engage society through reliable information, change habits and reinforce sustainable behaviour. A visualization tool has been created to analyze the results over time.

Can a social media intervention improve online communication about suicide? A feasibility study examining the acceptability and potential impact of the #chatsafe campaign

There is a need for effective and youth-friendly approaches to suicide prevention, and social media presents a unique opportunity to reach young people. Although there is some evidence to support the delivery of population-wide suicide prevention campaigns, little is known about their capacity to change behaviour, particularly among young people and in the context of social media. Even less is known about the safety and feasibility of using social media for the purpose of suicide prevention. Based on the #chatsafe guidelines, this study examines the acceptability, safety and feasibility of a co-designed social media campaign. It also examines its impact on young people's willingness to intervene against suicide and their perceived self-efficacy, confidence and safety when communicating on social media platforms about suicide. A sample of 189 young people aged 16-25 years completed three questionnaires across a 20-week period (4 weeks pre-intervention, immediately post-intervention, and at 4-week follow up). The intervention took the form of a 12-week social media campaign delivered to participants via direct message. Participants reported finding the intervention acceptable and they also reported improvements in their willingness to intervene against suicide, and their perceived self-efficacy, confidence and safety when communicating on social media about suicide. Findings from this study present a promising picture for the acceptability and potential impact of a universal suicide prevention campaign delivered through social media, and suggest that it can be safe to utilize social media for the purpose of suicide prevention.

Machine Learning for Mental Health in Social Media: Bibliometric Study

Background

Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.

Objective

We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.

Methods

Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.

Results

We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.

Conclusions

The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

“My ADHD Hellbrain”: A Twitter Data Science Perspective on a Behavioural Disorder

Purpose

Attention deficit hyperactivity disorder (ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs.

Design/methodology/approach

Tweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.

Findings

The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image.

Research limitations

This is a single case study and the results may differ for other topics.

Practical implications

Health professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.

Originality/value

Word association thematic analysis can give new insights into the (self-reported) patient perspective.

Space-Time Surveillance of Negative Emotions After Consecutive Terrorist Attacks in London

Terrorist attacks pose significant threats to mental health. There is dearth information about the impact of consecutive terrorist attacks on space-time concentrations of emotional reactions. This study collected (1) Twitter data following the two terrorist attacks in London in March and June of 2017, respectively, and (2) deprivation data at small areal levels in the United Kingdom. The space-time permutation model was used to detect the significant clusters of negative emotions, including fear, sadness, and anger in tweets. Logistic regression models were used to examine the social deprivation of communities associated with negative tweeting. The results reported two significant clusters after the March attack, one was in London, ten days after the attack, and the other was far from the attack site between Manchester and Birmingham, three days after the attack. Attention to the reoccurring attack in June diminished quickly. The socially deprived communities experienced double disadvantage-sending fewer tweets but expressing more negative emotions than their counterparts. The findings suggest that terrorism can affect public emotions far and broad. There is a potential for surveillance to rapidly identify geographically concentrated emotions after consecutive or prolonged disasters using social media data.

Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data

Background

Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society—through in-person and online social interactions—referencing the novel coronavirus as the “Chinese virus” or “China virus” has the potential to create and perpetuate stigma.

Objective

The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases “Chinese virus” and “China virus” on Twitter after the March 16, 2020, US presidential reference of this term.

Methods

Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of “Chinese virus.” We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map.

Results

A total of 16,535 “Chinese virus” or “China virus” tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing “Chinese virus” or “China virus” were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod “Chinese virus” tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod “Chinese virus” tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%).

Conclusions

The rise in tweets referencing “Chinese virus” or “China virus,” along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter.