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

Unraveling the Impact of Moral Framings within Media Coverage to Promote the (De)stigmatization of Depression on Social Media

Media coverage of depression on social media with specific framings could shape people's perception and attitude, which is significant in reducing the stigma and promoting support for depression sufferers. Adopting the lens of moral foundation theory (MFT), this study aims to explore the effect of inherent moral framings within depression coverage on social media on the stigma and approval attitudes toward depression in audiences' responses. A large language model and a dictionary-based approach were respectively adopted to score depression-related media coverages (n = 919) and corresponding comments (n = 92,505) collected from the Weibo platform against MFT's five dimensions and (de)stigma attitudes. The results indicated that care, purity, and fairness framings are prevalent in depression coverage, surpassing moral framings such as betrayal, harm, and cheating. Most responses expressed approval rather than stigma. Moreover, the use of care and loyalty framings can elicit approval responses but decrease audience engagement.

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.

The Suicide of Egyptian Queer Activist Sarah Hegazi in the News: A Comparative Analysis of the Coverage in Egyptian, Lebanese, and American Media

This study is a textual analysis of Egyptian queer activist Sarah Hegazi's suicide in 2020. Based on a grounded theory approach, qualitative analysis was performed on 23 media articles from Egypt, Lebanon, and the United States, with particular focus on episodic/thematic frames and the stigma/stigma-challenging frames due to their significance in studies about mental illness. The main findings reveal that Egypt had the highest stigma frames coupled with the lowest sympathy theme and the least attack on the Egyptian regime, whereas US and Lebanese outlets displayed high sympathy and vehemently attacked the Egyptian regime. Further, the study explains the findings in relation to country media systems. The significance of the study lies in its analysis of media coverage in three countries to help us understand how Arab and American media discuss the intersection of mental health and queerness in the Arab world. It also enriches the health communication literature as the first study to analyze the framing of the suicide of an Arab woman outside the context of war.

Improving Mental Health Among Communication and Media Scholars: Four Structural Suggestions

In recent years, multiple studies have called attention to the mental health of scholars across the academy. And yet, the mental health of communication and media scholars specifically has not received the focused attention it deserves. In their recent Journal of Communication article, Thomas Hanitzsch and colleagues begin the process of remedying this omission, providing important data on communication and media scholars' mental health and its correlates. Hanitzsch and colleagues stress that their work is a starting point only, and call for their fellow communication and media scholars to engage in discussion about potential solutions. In response to their call, this essay provides four structural recommendations aimed at improving the mental health of early and mid-career scholars.

Instagram for audience engagement: an evaluation of CERC framework in the GCC nations for digital public health during the Covid-19 pandemic

BACKGROUND

In this study, we investigate the utilization of Instagram by public health ministries across the Gulf Cooperation Council (GCC) nations to disseminate health-related information during the COVID-19 pandemic. With Instagram's visual-centric approach and high user engagement, the research aims to investigate its critical yet complex role in information dissemination amid a health crisis.

METHODS

To examine how Instagram communication strategies align with the CDC's Crisis and Emergency Risk Communication (CERC) framework, we employ the content analysis method. This approach helps to evaluate the effectiveness and challenges of employing Instagram for health communication within a region known for its significant social media usage.

RESULTS

Findings indicate that Instagram serves as a vital platform for the rapid dissemination of health information in the GCC, leveraging its visual capabilities and wide reach. The GCC ministries of health utilized Instagram to demonstrate a consistent and strategic approach to communicate essential COVID-19 related information. Kuwait and Bahrain were the most active of all the assessed ministries with respect to the number of engagement metrics (likes and comments). Most of the posts, as per the CERC framework, were informational and related to vaccine infection and death cases. The second most salient theme in line with the CERC framework was about promoting actions, followed by Instagram posts about activities, events, and campaigns.

CONCLUSIONS

The research underscores Instagram's potential as a powerful tool in enhancing public health resilience and responsiveness during health emergencies in the GCC. It suggests that leveraging social media, with careful consideration of its affordances, can contribute significantly to effective health communication strategies in times of crisis.

My Story of Depression: A Content Analysis of Autobiographic Videos on Douyin

Autobiographical accounts on social media could play an essential role in shaping the public's understanding of illnesses and dispelling illness-related stigma. This study examined how people living with depression describe their illness experiences on Douyin, China's leading video-sharing platform, through content analysis. It finds that most videos were created by young women who have lived with the illness for over three years. The top three topics were patient psychology, experience sharing, and knowledge and medical advice. These videos provided an overall non-stigmatizing portrayal of depression, with more than 80% of the influencers using challenge cues. However, stigma cues contributed to the virality of the videos. The theoretical and practical implications of the study were discussed.

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.

Exploring perspectives of stigma and discrimination among people with lived experience of mental health conditions: a co-produced qualitative study

Background

Efforts to understand the mechanisms and consequences of mental health-related stigma and discrimination need to center the perspectives of people affected by these negative impacts, through research efforts that are led or co-led by people with lived experience (PWLE) of mental health conditions.

Methods

This study used co-production principles to explore global perspectives of stigma and discrimination among people meeting the inclusion criteria of identifying as PWLEs and being willing to share their experiences of stigma and discrimination resulting from a diagnosis of a mental health condition, and who had also participated in anti-stigma activities. Participants were recruited online via a self-selecting snowball sampling method. Qualitative data were collected from respondents via an anonymous global online survey conducted between 12/01/2021 and 02/28/2022. The main outcomes assessed were open-ended, qualitative responses to questions exploring experiences of stigma and discrimination, experiences regarding diagnoses, language/terminology related to mental health, impact of stigma and discrimination, and involvement with anti-stigma interventions. Data were synthesised through digital text network analysis and thematic content analysis.

Findings

A total of 198 respondents from over 30 countries across Europe, the Americas, Africa, Asia, and Australia/Oceania were included in the study. The results reflected five themes: 1) the role of language and words; 2) the role of media in perpetuating and reducing stigma; 3) societal reactions to mental health conditions and strategies to cope with these; 4) knowledge about activities to reduce stigma and discrimination and their impact; and 5) personal involvement in activities to reduce stigma and discrimination.

Interpretation

The findings highlight that people with mental health conditions are aware of and experience stigma and discrimination across core domains of daily life. The importance of recognising the key role PWLEs can play in efforts to reduce stigma and discrimination was highlighted, and how they can be appropriately supported to contribute and have their experiential expertise recognised. Meaningful and authentic collaborations between PWLEs and other stakeholders can enhance the quality and relevance of strategies to reduce stigma and discrimination. This is, to our knowledge, the first study of its kind to use a co-production approach to explore experiences and reflections of stigma and discrimination related to mental health from a global perspective. However, the results are not broadly representative of the general PWLE population or suggestive of globally uniform experiences of stigma and discrimination.

Funding

None.

Understanding Mental Health Organizations' Instagram Through Visuals: A Content Analysis

This study analyzed the content, visual features, and audience engagement data of Instagram posts from two mental health organizations over one year (N = 725). For content features, mental health literacy and communicative strategies were examined. Posts that promoted knowledge of mental disorders and treatments, used information and community strategy were more likely to receive higher audience engagement. Visual features of demographic segments, visual composition, and visual framing topics were analyzed. Images that covered an unspecific population, used illustrated images, and focused on anti-stigma topical frames obtained more engagement. Theoretical contributions and practical applications regarding visual message design and management on social media to promote mental health are also offered.

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.

Does Organizational Messaging Make a Difference? Investigating Themes and Language Style in Twitter Discourse and Engagement by Mental Health Organizations

The present study investigated the latent topics and language styles present in mental health organizational discourse on Twitter. The researchers sought to analyze identifying the prevalence of and language used in social support messaging in tweets about mental health care, the overarching topics regarding mental health care, and predicted that tweets with higher engagement will have increased frequency of words with positively valenced emotion and cognitive processing. A GSDMM was run to uncover latent themes that emerged in a data set of 326.9k tweets and 7.2 m words about organizational discussions of mental health. A generalized linear model using the Poisson distribution was used to assess the role of engagement, positive emotion, and cognitive processing. The study found support for both positive emotion and cognitive processing as statistically significant predictors of engagement. Directions for research include the development of health message strategies, policy needs, and online interventions.

Normalizing Anxiety on Social Media Increases Self-Diagnosis of Anxiety: The Mediating Effect of Identification (But Not Stigma)

Normalizing mental health disorders in media communication can have a positive impact on the public by improving awareness. However, normalizing issues like anxiety could lead people to categorize normal anxiety as a disorder. In Study One, viewing social media posts that normalized anxiety resulted in a greater likelihood of self-diagnosis of anxiety disorder compared to social media posts that did not normalize it. This effect was through identification with and liking of the person featured in the social media post. In Study Two, those results were replicated. Additionally, we expected, but did not find, that normalizing anxiety had an impact on perceived stigma of anxiety disorders. Thus, at least in this case, normalization influenced self-diagnosis primarily through increasing identification with another person with anxiety, rather than decreasing stigma. Efforts to maximize positive impacts of normalizing disorders should examine unintended, potentially negative, consequences.

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.

Framing of female medical personnel during the COVID-19 pandemic: a case study of the Chinese official media

This paper analyzes the media frames adopted by the official WeChat and Sina Weibo accounts of the People's Daily between January 1 and December 31, 2020, for reports about female medical personnel involved in pandemic prevention and control. Although the number of female medical personnel involved in pandemic prevention and control far exceeded that of their male counterparts, the extent of media reports on the former was far less than that of the latter. The human interest frame about female medical personnel was mainly applied, while the use of the action frame was less frequent, which highlighted the gender identity and family role of these women but weakened their professional identity. This was not conducive to praising the contributions of female medical personnel in fighting the pandemic. The media frames of reporting medical personnel in WeChat and Sina Weibo accounts of the People's Daily are not always the same. After Wuhan's lockdown ended on April 8, the proportion of the human interest frame of the report text of female medical personnel decreased, and the proportion of the action frame increased, while the proportion of the human interest frame of the report text of male medical personnel increased and the proportion of the action frame decreased. Previous studies mainly analyzed the use of the media frames of female news personalities, but few studies focused on whether women had the possibility of breaking away from the gender media frames. This study shows that some female medical personnel with exceptional professional competence are likely to transcend the gender media frames and receive similar coverage to that of male medical professionals, like Li Lanjuan and Chen Wei.

Understanding of Dementia in the Polish Language: A Frame Semantic Approach

BACKGROUND

With the increasing incidences of dementia in aging societies, attention should be paid to the social context in which people with dementia live. One of its aspects is language transmitting beliefs, perceptions, and behavioral patterns. An analysis of understanding the diagnostic label of dementia may reveal the role of semantics in the process of social cognition of this disease.

OBJECTIVE

The overall aim of this study was to investigate the understanding of the word dementia (otępienie) in the Polish language.

METHODS

Frame semantics approach was applied. The structure of semantic information was uncovered with the concept of frame utilizing The National Corpus of Polish (the biggest corpus of contemporary Polish language of 1,500 million words). Additional data was collected from Polish speaking adults in Poland.

RESULTS

The analyses allowed to identify the otępienie frame for Polish and verify how its elements are filled in by the general population, indicating the selectivity of colloquial knowledge about dementia. Dementia deviates from the prototypical disease. Need to care for the person with dementia outweighs treatment options. The cognitive symptoms and characteristics of the subject are salient. The perceptions of people with dementia embedded in semantics of the diagnostic label might create a basis for prejudicial attitudes among lay part of the society.

CONCLUSION

Findings give foundation to further studies on relationship between semantics and social cognition of dementia which has a real impact on the social and clinical situation of people with dementia and may facilitate formulation of tailored messages aimed at building dementia-friendly society.

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.

Research to Clinical Practice-Youth seeking mental health information online and its impact on the first steps in the patient journey

BACKGROUND

Online searches about anxiety and depression are recorded every 3-5 s. As such, information and communication technologies (ICT) have enormous potential to enable or impair help-seeking and patient-professional interactions. Youth studies indicate that ICT searches are undertaken before initial mental health consultations, but no publications have considered how this online activity affects the first steps of the patient journey in youth mental health settings.

METHODS

State-of-the-art review using an iterative, evidence mapping approach to identify key literature and expert consensus to synthesize and prioritise clinical and research issues.

RESULTS

Adolescents and young adults are more likely to seek health advice via online search engines or social media platforms than from a health professional. Young people not only search user-generated content and social media to obtain advice and support from online communities but increasingly contribute personal information online.

CONCLUSIONS

A major clinical challenge is to raise professional awareness of the likely impact of this activity on mental health consultations. Potential strategies range from modifying the structure of clinical consultations to ensure young people are able to disclose ICT activities related to mental health, through to the development and implementation of 'internet prescriptions' and a youth-focused 'toolkit'.

Depressive symptoms among primary healthcare workers during the novel SARS-CoV-2 coronavirus pandemic in the Muscat governorate

BACKGROUND

With the unprecedented spread of the novel SARS-CoV-2 coronavirus, primary healthcare workers (PHCWs) are having to shoulder the increasing weight of this ongoing pandemic.

AIMS

This study explored the rate and covariates of depressive symptoms among PHCWs in the Muscat governorate.

METHODS

A cross-sectional online survey was conducted from 10 May to 10 June 2020 among PHCWs working in all primary healthcares across the Muscat governorate. Data on sociodemographic and risk factors of having at least one underlying physical health condition, a psychiatric history, family history of psychiatric disorders, and direct involvement with COVID-19 positive patients were sought. The Patient Health Questionnaire (PHQ-9) was then used to solicit the presence of depressive symptoms. Those with a cutoff point ≥10 were considered as showing depressive symptoms. Logistic regression was used to determine risk factors associated with depressive symptoms in PHCWs after adjusting for all sociodemographic factors.

FINDINGS

A total of 432 (72%) out of 600 PHCWs with an average age of 39.2 years (SD = 7.8 years) ranging between 25.0 and 75.0 years responded to the survey. There were more females (n = 281, 65.3%) than males, and more than 45% (n = 195) of them were physicians. Additionally, more than 78% (n = 338) had been in contact with COVID-19 patients. There was a significant association between different age groups and profession (P < .001), having at least one underlying physical health condition (P = 0.001) and depressive symptom status (P = 0.038). A total of 78 out of the 423 subjects (18.1%) were considered to have depressive symptoms. After adjusting for all factors, the logistic regression model showed that an age of 34 years or below (OR = 2.079, P = 0.021) and having at least one underlying physical health condition (OR = 2.216, P = 0.007) were factors contributing significantly to depressive symptoms among the PHCWs.