#WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems

Leveraging Media Monitoring to Inform Targeted Health Communications for Adolescents, Older Adults, and LGBTQ+ People

OBJECTIVE

This manuscript assesses how media monitoring can guide the tailoring of public health communications for adolescents, older adults, and LGBTQ+ individuals, addressing their unique mental health challenges.

DESIGN

The study employed a thematic analysis of publicly available digital and social media data.

SETTING

Research was conducted using media monitoring platforms, with a focus on conversations relevant to specific groups within the United States.

PARTICIPANTS

The dataset included millions of public posts relevant to mental health issues among adolescents, older adults, and LGBTQ+ communities, collected over a one-year period. Main Outcome Measures: Key themes and patterns within mental health discussions were identified.

RESULTS

Discussions around the "youth mental health crisis" were prominent, with 25% of adolescent-related posts reflecting this sentiment, often highlighting the impact of social media and academic pressures. For older adults, themes of loneliness and financial struggles were recurrent, with discussions showing a lack of practical support and resources. In LGBTQ+ conversations, there was an emphasis on the challenges of discrimination and identity-related misconceptions, with 28% of the mentions relating to mental health crises involving suicide or self-harm. These discussions underscored the critical need for inclusive and affirmative support tailored to the unique challenges faced by these groups.

CONCLUSIONS

Media monitoring provides essential insights that can improve public health messaging by identifying trending mental health discussions and sentiments. By acknowledging and addressing the specific needs and challenges of specific population groups, public health communicators can develop more effective strategies that not only highlight problems but also offer clear, actionable solutions to promote better mental health outcomes and support. This approach is crucial for adapting health communications to the evolving landscape of media and public discourse, ensuring that messages are both relevant and supportive.

Postpartum Depression and Maternal-Infant Bonding Experiences in Social Media Videos: Qualitative Content Analysis

Background

While the negative effects of postpartum depression on maternal-infant bonding are well-documented, our understanding of how it exerts these effects remains incomplete. A better understanding of how maternal postpartum depression affects bonding can enable clinicians to better identify and support mothers with difficulties bonding with their children.

Objective

This study aims to describe the bonding experiences of mothers with postpartum depression through an analysis of short-form videos and user engagement.

Methods

We collected publicly available highly-viewed TikTok videos using hashtags associated with postpartum depression and associated engagement metrics in May 2023. After manual screening, we extracted 533 videos related to the mother-infant bond, from which we analyzed a random subset of 159 videos. We abstracted categories from videos using a hybrid deductive and inductive approach. Negative binomial regression models of video likes, views, shares, and comment count were used with content categories and the creator's numbers of followers as independent variables.

Results

Abstraction of content from mother-infant bond videos resulted in six categories: (1) navigating anxiety and anger, (2) creating physical and emotional boundaries, (3) overwhelmed by demands of caregiving, (4) subverted expectations, (5) enduring and finding strength through the challenge of postpartum depression, and (6) can't remember early life. Subverted expectations and navigating anxiety and anger categories were associated with increased views (rate ratio [RR] 1.72, 95% CI 1.22-2.43; RR 1.61, 95% CI 1.09-2.38, respectively), likes (RR 3.61, 95% CI 2.55-5.11; RR 3.96, 95% CI 2.69-5.85, respectively), shares (RR 2.95, 95%CI 2.09-4.18; RR 2.45, 95% CI 1.66-3.61, respectively), and comments (RR 2.78, 95% CI 1.97-3.94; RR 1.89, 95% CI 1.28-2.79, respectively). Sensitivity analysis with creators with fewer followers mostly aligned with these results.

Conclusions

This qualitative content analysis of short-form videos identified specific ways postpartum depression impacts the mother-infant bond, highlighting strategies for clinicians to support bonding. Analysis of engagement metrics further demonstrated the types of experiences that most resonate with viewers. Our findings demonstrate the potential of this qualitative method to augment understanding of lived experiences.

The use of trigger warnings on social media: a text analysis study of X

Trigger warnings are placed at the beginning of potentially distressing content to provide individuals with the opportunity to avoid the content before exposure. Social media platforms use artificial intelligence to add automatic trigger warnings to certain images and videos, but are less commonly applied to textual content. This leaves the responsibility of adding trigger warnings to the authors, and a failure to do so may expose vulnerable users to sensitive or upsetting content. Due to limited research attention, there is a lack of understanding concerning what content is or is not considered triggering by social media users. To address this gap, we examine the use of trigger warnings in tweets on X, previously known as Twitter. We used a large language model (LLM) for zero-shot learning to identify the types of trigger warnings (e.g., violence, abuse) used in tweets and their prevalence. Additionally, we employed sentiment and emotion analysis to examine each trigger warning category, aiming to identify prevalent emotions and overall sentiment. Two datasets were collected: 48,168 tweets with explicit trigger warnings and 4,980,466 tweets with potentially triggering content. The analysis of the smaller dataset indicates that users have applied trigger warnings more frequently over the years and are applying them to a broader range of content categories than they did in the past. These findings may reflect users' growing interest in creating a safe space and a supportive online community that is aware of diverse sensitivities among users. Despite these findings, our analysis of the larger dataset confirms a lack of trigger warnings in most potentially triggering content.

Social media content about anxiety, theories of anxiety, and coping strategies in college students

Objective: To address current anxiety rates in college students by examining the impact of anxiety mindset on generalized anxiety symptoms and coping strategies and by characterizing student interaction with online anxiety content. Methods: Five hundred and eleven college students (aged 18-23) completed a cross-sectional, online survey that included validated self-report measures and additional questions regarding social media content. Results: Anxiety mindset moderated the relationship between generalized anxiety symptoms and problem-focused and emotional coping. Participants reported high rates of encountering anxiety content on social media, and often this content was encountered without intentionally searching for it. Seeing more hopeless anxiety content was related to greater generalized anxiety symptoms, avoidance coping, and more fixed mindsets of anxiety. Conclusion: Our results support the benefits of a growth mindset of anxiety over a fixed mindset of anxiety, and provide preliminary evidence of the harmful effects of anxiety content on social media.

Factors influencing decisions to seek mental healthcare in the Arab Gulf states: a qualitative thematic analysis

BACKGROUND

There is a lack of comprehensive research on the mental health needs of individuals in the Gulf Cooperation Council (GCC) countries, so further exploration is needed to understand the barriers to accessing available mental healthcare services. This study examined the factors influencing individuals' decisions to seek mental healthcare in these countries by analysing the public's engagement with a mental health campaign on social media.

METHODS

We conducted a reflexive thematic analysis of people's comments in response to a mental health campaign posted by the Gulf Health Council on four social media platforms: TikTok, YouTube, Instagram, and X (previously Twitter).

RESULTS

The analysis included 2,146 comments. Barriers to seeking mental healthcare fell into several themes: personal, family, societal, religious, cultural, and healthcare service barriers. Misconceptions and misinformation were key barriers to seeking mental healthcare, as well as religious and societal perceptions of mental health and the stigma surrounding mental illness. Factors influencing accessibility to professional help included the cost of mental healthcare, lack of competent healthcare professionals, and concerns over privacy and confidentiality.

CONCLUSION

Our research identified several factors that affect accessing mental healthcare in the GCC, including personal, family, societal, cultural, religious, and healthcare system factors. We expect that the findings will inform the development of policies and interventions designed to facilitate access to mental healthcare services in the GCC.

Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis

BACKGROUND

Despite a dramatic increase in the number of people with generalized anxiety disorder (GAD), a substantial number still do not seek help from health professionals, resulting in reduced quality of life. With the growth in popularity of social media platforms, individuals have become more willing to express their emotions through these channels. Therefore, social media data have become valuable for identifying mental health status.

OBJECTIVE

This study investigated the social media posts and behavioral patterns of people with GAD, focusing on language use, emotional expression, topics discussed, and engagement to identify digital markers of GAD, such as anxious patterns and behaviors. These insights could help reveal mental health indicators, aiding in digital intervention development.

METHODS

Data were first collected from Twitter (subsequently rebranded as X) for the GAD and control groups. Several preprocessing steps were performed. Three measurements were defined based on Linguistic Inquiry and Word Count for linguistic analysis. GuidedLDA was also used to identify the themes present in the tweets. Additionally, users' behaviors were analyzed using Twitter metadata. Finally, we studied the correlation between the GuidedLDA-based themes and users' behaviors.

RESULTS

The linguistic analysis indicated differences in cognitive style, personal needs, and emotional expressiveness between people with and without GAD. Regarding cognitive style, there were significant differences (P<.001) for all features, such as insight (Cohen d=1.13), causation (Cohen d=1.03), and discrepancy (Cohen d=1.16). Regarding personal needs, there were significant differences (P<.001) in most personal needs categories, such as curiosity (Cohen d=1.05) and communication (Cohen d=0.64). Regarding emotional expressiveness, there were significant differences (P<.001) for most features, including anxiety (Cohen d=0.62), anger (Cohen d=0.72), sadness (Cohen d=0.48), and swear words (Cohen d=2.61). Additionally, topic modeling identified 4 primary themes (ie, symptoms, relationships, life problems, and feelings). We found that all themes were significantly more prevalent for people with GAD than for those without GAD (P<.001), along with significant effect sizes (Cohen d>0.50; P<.001) for most themes. Moreover, studying users' behaviors, including hashtag participation, volume, interaction pattern, social engagement, and reactive behaviors, revealed some digital markers of GAD, with most behavior-based features, such as the hashtag (Cohen d=0.49) and retweet (Cohen d=0.69) ratios, being statistically significant (P<.001). Furthermore, correlations between the GuidedLDA-based themes and users' behaviors were also identified.

CONCLUSIONS

Our findings revealed several digital markers of GAD on social media. These findings are significant and could contribute to developing an assessment tool that clinicians could use for the initial diagnosis of GAD or the detection of an early signal of worsening in people with GAD via social media posts. This tool could provide ongoing support and personalized coping strategies. However, one limitation of using social media for mental health assessment is the lack of a demographic representativeness analysis.

Phubbing Behavior and its Association With Depression, Anxiety, and Stress in Rehabilitation Students in Iran: A Cross-Sectional Study

Background and Aims

The study aimed to determine the relationship between phubbing behavior and its association with depression, anxiety, and stress among college students of the School of Rehabilitation at Shahid Beheshti University of Medical Sciences.

Methods

This cross-sectional survey design used a self-reported questionnaire that included demographic data, DASS21, and a phubbing scale. The Generic Scale of Phubbing was administered to 320 students from four fields of school of Rehabilitation at Shahid Beheshti University of Medical Sciences. Data analysis was done using the 22nd SPSS software version. The Kolmogorov-Smirnov, Spearman correlation, Mann-Whitney and Kruskal-Wallis tests were used for analysis.

Results

According to the non-parametric tests, the study revealed a significant association between mental health components and the primary study variables. Specifically, there was a statistically significant positive correlation (p < 0.05) between all components of mental health (depression, anxiety, and stress) and phubbing. Furthermore, a significant positive correlation (p < 0.05) was identified between all dimensions of phubbing (nomophobia, conflict, self-isolation, and problem confirmation) and the components of mental health.

Conclusion

The final results indicate that 15% of the total variation in phubbing phubbing can be attributed to the variables of depression and educational background. We suggest that phubbing behavior is linked to lower levels of well-being and mental health.

Online disclosure of suicide method: What can online posts tell us about suicidal planning?

Rates of self-disclosure of, and subsequently formal help-seeking for, suicidal thoughts and behaviors (STBs) remains low. However, online discussions of STBs have become increasingly prevalent, creating potential opportunities to identify those at potentially elevated suicide risk (i.e., engaging in suicidal planning). This study used linguistic analysis to examine online STB discussions to identify posts that reference a suicide method, as well as the lethality and timeframe of the identified suicide method. A corpus of 3974 posts from the online forum Reddit SuicideWatch were extracted for data analysis. Approximately one-fifth of analyzed posts mentioned a suicide method; over 35% were classified as severe risk with regard to lethality and almost 70% discussed a suicide method yet to occur. Analyses demonstrated that three categories of words - Substances, Physical, and Past Focus - were associated with increased odds of the presence of a suicide method in a post, whereas five different word categories were associated with a decrease in odds of method presence; most notably was the use of Social words. Together, results support the utility of this approach in detecting discussions of a suicide method. However, they may be less fruitful in distinguishing planning severity among posts including method discussions.

Mental Health Staff's Views on Social Media Use Among People with Psychosis: A Cross-Sectional Survey

Objectives

The use of social media is prevalent in society; however, existing evidence is not sufficient to conclude whether the benefits of social media use can outweigh the risks for people with psychosis. In response to a recent call for staff to take a more active role in asking and advising service users about the impact of using social media platforms on their mental health in clinical practice, we sought to understand staff's attitudes toward service users with psychosis using social media in day-to-day life.

Methods

A cross-sectional survey was disseminated from April 2018 to September 2020 in Mental Health Trusts in the Northwest of England.

Results

A total of 155 staff completed the survey. We found that staff's social media use was high; however, as expected, social media was less used for communicating between staff and service users than other digital technologies (i.e., email and text messages). Moreover, staff's awareness of NHS Trust guidelines around communicating with service users via digital technology was limited.

Conclusion

Despite staff views towards service user engagement with social media being mixed, as both benefits and concerns were reported, we need to ensure staff do not overestimate its risks or underestimate its benefits, so that they can offer tailored advice on social media use based on service users' individual circumstances.

Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study

Depression affects over 280 million people globally, yet many cases remain undiagnosed or untreated due to stigma and lack of awareness. Social media platforms like X (formerly Twitter) offer a way to monitor and analyze depression markers. This study analyzes Twitter data 90 days before and 90 days after a self-disclosed clinical diagnosis. We gathered 246,637 tweets from 229 diagnosed users. CorEx topic modeling identified seven themes: causes, physical symptoms, mental symptoms, swear words, treatment, coping/support mechanisms, and lifestyle, and conditional logistic regression assessed the odds of these themes occurring post-diagnosis. A control group of healthy users (284,772 tweets) was used to develop and evaluate machine learning classifiers-support vector machines, naive Bayes, and logistic regression-to distinguish between depressed and non-depressed users. Logistic regression and SVM performed best. These findings show the potential of Twitter data for tracking depression and changes in symptoms, coping mechanisms, and treatment use.

Analyzing public discourse of dementia from Spanish and English tweets: a comparative analysis with other neurological disorders

Introduction

Dementia comprise a broad spectrum of cognitive declines affecting 47 million people worldwide, with numbers projected to reach 131 million by 2050. Predominantly associated with older adults, dementia can also impact younger individuals, having a significant impact on daily functioning of the affected patients, relatives, caregivers and the socioeconomic system. Recent research underscores the utility of social media, particularly X (previously designed as Twitter), in understanding public perceptions and sentiments related to neurological disorders. Despite some initial studies have explored social perceptions of dementia in X, broader and deeper analysis of this condition is still warranted.

Materials and methods

In this retrospective study, we collected and examined all tweets posted in English or Spanish from 2007 to 2023 that mentioned dementia and compare the information with other highly representative neurological disorders like migraines, epilepsy, multiple sclerosis, spinal cord injury, or Parkinson's disease. We developed a codebook to analyze tweets, classifying them by themes such as trivialization, treatment perceptions, and etiopathogenesis. Manually categorized tweets trained machine learning models, BERTWEET for English and BETO for Spanish, which then classified larger datasets with high accuracy. Statistical analysis, including ANOVA, Kruskal-Wallis, and chi-square tests, was conducted to explore linguistic and cultural differences in perceptions of neurological disorders, with results visualized.

Results

Our study reveals that dementia is by far the most frequently discussed neurological disorder on X. Likewise, this condition appears to be the most trivialized neurological disorder in Spanish tweets and the second most trivialized in English tweets, with notable differences in geolocation data. Additionally, we found significant differences in perceptions of dementia treatment and associated sentiments between Spanish and English tweets. Furthermore, our study identified varying perceptions of medical content (etiology) and non-medical content (positive/negative experiences and aid requests) related to dementia and other neurological disorders, unveiling a complex landscape of these topics on X.

Conclusions

This study explores the importance of X as a social platform for addressing various critical issues related to dementia, comparing it with other neurological disorders in English and Spanish tweets. Future research could further investigate the valuable role of social media in understanding public perceptions and needs regarding dementia and neurological disorders among X users.

Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis

BACKGROUND

In 2021, the United States experienced a 14% rise in fatal drug overdoses totaling 106,699 deaths, driven by harmful opioid use, particularly among individuals in the perinatal period who face increased risks associated with opioid use disorders (OUDs). Increased concerns about the impacts of escalating harmful opioid use among pregnant and postpartum persons are rising. Most of the current limited perinatal OUD studies were conducted using traditional methods, such as interviews and randomized controlled trials to understand OUD treatment, risk factors, and associated adverse effects. However, little is known about how social media data, such as X, formerly known as Twitter, can be leveraged to explore and identify broad perinatal OUD trends, disclosure and communication patterns, and public health surveillance about OUD in the perinatal period.

OBJECTIVE

The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort.

METHODS

We extracted 6 million raw perinatal-themed tweets posted by global X users during the opioid epidemic from May 2019 to October 2021. After data cleaning and sampling, we used 500 tweets related to OUD in the perinatal period by US X users for a thematic analysis using NVivo (Lumivero) software.

RESULTS

Seven major themes emerged from our thematic analysis: (1) political views related to harmful opioid and other substance use, (2) perceptions of others' substance use, (3) lived experiences of opioid and other substance use, (4) news reports or papers related to opioid and other substance use, (5) health care initiatives, (6) adverse effects on children's health due to parental substance use, and (7) topics related to nonopioid substance use. Among these 7 themes, our user engagement analysis revealed that themes 4 and 5 received the highest average retweet counts, and theme 3 received the highest average tweet reply count. We further found that different computational methods excel in analyzing different themes.

CONCLUSIONS

Social media platforms such as X can serve as a valuable tool for analyzing real-time discourse and exploring public perceptions, opinions, and behaviors related to maternal substance use, particularly, harmful opioid use in the perinatal period. More health promotion strategies can be carried out on social media platforms to provide educational support for the OUD perinatal population.

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).

World Psychiatric Association-Asian Journal of Psychiatry Commission on Public Mental Health

Although there is considerable evidence showing that the prevention of mental illnesses and adverse outcomes and mental health promotion can help people lead better and more functional lives, public mental health remains overlooked in the broader contexts of psychiatry and public health. Likewise, in undergraduate and postgraduate medical curricula, prevention and mental health promotion have often been ignored. However, there has been a recent increase in interest in public mental health, including an emphasis on the prevention of psychiatric disorders and improving individual and community wellbeing to support life trajectories, from childhood through to adulthood and into older age. These lifespan approaches have significant potential to reduce the onset of mental illnesses and the related burdens for the individual and communities, as well as mitigating social, economic, and political costs. Informed by principles of social justice and respect for human rights, this may be especially important for addressing salient problems in communities with distinct vulnerabilities, where prominent disadvantages and barriers for care delivery exist. Therefore, this Commission aims to address these topics, providing a narrative overview of relevant literature and suggesting ways forward. Additionally, proposals for improving mental health and preventing mental illnesses and adverse outcomes are presented, particularly amongst at-risk populations.

Twitter Discussions on #digitaldementia: Content and Sentiment Analysis

BACKGROUND

Digital dementia is a term that describes a possible decline in cognitive abilities, especially memory, attributed to the excessive use of digital technology such as smartphones, computers, and tablets. This concept has gained popularity in public discourse and media lately. With the increasing use of social media platforms such as Twitter (subsequently rebranded as X), discussions about digital dementia have become more widespread, which offer a rich source of information to understand public perceptions, concerns, and sentiments regarding this phenomenon.

OBJECTIVE

The aim of this research was to delve into a comprehensive content and sentiment analysis of Twitter discussions regarding digital dementia using the hashtag #digitaldementia.

METHODS

Retrospectively, publicly available English-language tweets with hashtag combinations related to the topic of digital dementia were extracted from Twitter. The tweets were collected over a period of 15 years, from January 1, 2008, to December 31, 2022. Content analysis was used to identify major themes within the tweets, and sentiment analysis was conducted to understand the positive and negative emotions associated with these themes in order to gain a better understanding of the issues surrounding digital dementia. A one-way ANOVA was performed to gather detailed statistical insights regarding the selected tweets from influencers within each theme.

RESULTS

This study was conducted on 26,290 tweets over 15 years by 5123 Twitter users, mostly female users in the United States. The influencers had followers ranging from 20,000 to 1,195,000 and an average of 214,878 subscribers. The study identified four themes regarding digital dementia after analyzing tweet content: (1) cognitive decline, (2) digital dependency, (3) technology overload, and (4) coping strategies. Categorized according to Glaser and Strauss's classifications, most tweets (14,492/26,290, 55.12%) fell under the categories of wretched (purely negative) or bad (mostly negative). However, only a small proportion of tweets (3122/26,290, 11.86%) were classified as great (purely positive) or swell sentiment (mostly positive). The ANOVA results showed significant differences in mean sentiment scores among the themes (F3,3581=29.03; P<.001). The mean sentiment score was -0.1072 (SD 0.4276).

CONCLUSIONS

Various negative tweets have raised concerns about the link between excessive use of digital devices and cognitive decline, often known as digital dementia. Of particular concern is the rapid increase in digital device use. However, some positive tweets have suggested coping strategies. Engaging in digital detox activities, such as increasing physical exercise and participating in yoga and meditation, could potentially help prevent cognitive decline.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSION

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

"Anxiety is not cute" analysis of twitter users' discourses on romanticizing mental illness

BACKGROUND

The proliferation of social media platforms has provided a unique space for discourse on mental health, originally intended to destigmatize mental illness. However, recent discourses on these platforms have shown a concerning shift towards the romanticization of mental health issues. This research focuses on Twitter (now called X) users' authentic discussions on the phenomenon of romanticizing mental health, aiming to uncover unique perspectives, themes, and language used by users when engaging with this complex topic.

METHODS

A comprehensive content analysis was conducted on 600 relevant tweets, with the application of topic modeling techniques. This methodology allowed for the identification and exploration of six primary themes that emerged from Twitter users' discussions. Statistical tests were not applied in this qualitative analysis.

RESULTS

The study identified six primary themes resulting from Twitter users' discussions on the romanticization of mental health. These themes include rejecting/critiquing the glamorization of mental health, monetization of mental health by corporate organizations, societal misconceptions of mental health, the role of traditional media and social media, unfiltered realities of depression, and the emphasis on not romanticizing mental health.

CONCLUSIONS

This study provides valuable insights into the multifaceted discourses surrounding the romanticization of mental health on Twitter. It highlights users' critiques, concerns, and calls for change, emphasizing the potential harm caused by romanticizing mental illness. The findings underscore the importance of fostering responsible and empathetic discussions about mental health on social media platforms. By examining how Twitter users interact with and respond to the romanticization of mental health, this research advances our understanding of emerging perspectives on mental health issues among social media users, particularly young adolescents. The study also underscores the effects of this phenomenon on individuals, society, and the mental health community. Overall, this research emphasizes the need for more responsible and knowledgeable discussions around mental health in the digital age.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

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.

"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community

BACKGROUND

Alzheimer disease or related dementias (ADRD) are severe neurological disorders that impair the thinking and memory skills of older adults. Most persons living with dementia receive care at home from their family members or other unpaid informal caregivers; this results in significant mental, physical, and financial challenges for these caregivers. To combat these challenges, many informal ADRD caregivers seek social support in online environments. Although research examining online caregiving discussions is growing, few investigations have distinguished caregivers according to their kin relationships with persons living with dementias. Various studies have suggested that caregivers in different relationships experience distinct caregiving challenges and support needs.

OBJECTIVE

This study aims to examine and compare the online behaviors of adult-child and spousal caregivers, the 2 largest groups of informal ADRD caregivers, in an open online community.

METHODS

We collected posts from ALZConnected, an online community managed by the Alzheimer's Association. To gain insights into online behaviors, we first applied structural topic modeling to identify topics and topic prevalence between adult-child and spousal caregivers. Next, we applied VADER (Valence Aware Dictionary for Sentiment Reasoning) and LIWC (Linguistic Inquiry and Word Count) to evaluate sentiment changes in the online posts over time for both types of caregivers. We further built machine learning models to distinguish the posts of each caregiver type and evaluated them in terms of precision, recall, F1-score, and area under the precision-recall curve. Finally, we applied the best prediction model to compare the temporal trend of relationship-predicting capacities in posts between the 2 types of caregivers.

RESULTS

Our analysis showed that the number of posts from both types of caregivers followed a long-tailed distribution, indicating that most caregivers in this online community were infrequent users. In comparison with adult-child caregivers, spousal caregivers tended to be more active in the community, publishing more posts and engaging in discussions on a wider range of caregiving topics. Spousal caregivers also exhibited slower growth in positive emotional communication over time. The best machine learning model for predicting adult-child, spousal, or other caregivers achieved an area under the precision-recall curve of 81.3%. The subsequent trend analysis showed that it became more difficult to predict adult-child caregiver posts than spousal caregiver posts over time. This suggests that adult-child and spousal caregivers might gradually shift their discussions from questions that are more directly related to their own experiences and needs to questions that are more general and applicable to other types of caregivers.

CONCLUSIONS

Our findings suggest that it is important for researchers and community organizers to consider the heterogeneity of caregiving experiences and subsequent online behaviors among different types of caregivers when tailoring online peer support to meet the specific needs of each caregiver group.

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.

Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study

BACKGROUND

Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance.

OBJECTIVE

The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition.

METHODS

In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug.

RESULTS

Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery.

CONCLUSIONS

The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use.

The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study

BACKGROUND

Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed.

OBJECTIVE

In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries.

METHODS

This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms "depress" and "sad" in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users.

RESULTS

Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P<.001; negative: P=.01); for example, Eastern users used more words associated with sadness (P=.01). However, Western users were more likely to share vulnerable topics such as mental health (P<.001), and this group also had a greater tendency to discuss sensitive topics such as swear words (P<.001) and death (P<.001). In addition, when talking to chatbots, people expressed their depressive moods differently than on other platforms. Users were more open to expressing emotional vulnerability related to depressive or sad moods to chatbots (74,045/148,590, 49.83%) than on social media (149/1978, 7.53%). Chatbot conversations tended not to broach topics that require social support from others, such as seeking advice on daily life difficulties, unlike on social media. However, chatbot users acted in anticipation of conversational agents that exhibit active listening skills and foster a safe space where they can openly share emotional states such as sadness or depression.

CONCLUSIONS

The findings highlight the potential of chatbot-assisted mental health support, emphasizing the importance of continued technical and policy-wise efforts to improve chatbot interactions for those in need of emotional assistance. Our data indicate the possibility of chatbots providing helpful information about depressive moods, especially for users who have difficulty communicating emotions to other humans.

Helicopter parenting through the lens of reddit: A text mining study

The study aimed to understand Reddit users' experience with helicopter parenting through first-hand accounts. Text mining and natural language processing techniques were employed to extract data from the subreddit r/helicopterparents. A total of 713 original posts were processed from unstructured texts to tidy formats. Latent Dirichlet Allocation (LDA), a popular topic modeling method, was used to discover hidden themes within the corpus. The data revealed common environmental contexts of helicopter parenting (i.e., school, college, work, and home) and its implication on college decisions, privacy, and social relationships. These collectively suggested the importance of autonomy-supportive parenting and mindfulness interventions as viable solutions to the problems posed by helicopter parenting. In addition, findings lent support to past research that has identified more maternal than paternal models of helicopter parenting. Further research on the implications of the COVID-19 pandemic on helicopter parenting is warranted.

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult-to-treat depression

INTRODUCTION

Treatment-resistant depression (TRD) is depression unresponsive to antidepressants and affects 55% of British primary care users with depression. Current evidence is from secondary care, but long referral times mean general practitioners (GPs) manage TRD. Studies show that people with depression use Twitter to form community and document symptoms. However, Twitter remains a largely unexplored space of documented patient experience. Twitter data could provide valuable insights into learning about primary care experiences of TRD. In this study, we explored Twitter comments and conversations about TRD and produced patient-driven recommendations.

METHODS

Tweets from UK-based users were collected manually and using a browser extension in June 2021. Conventional content analysis was used to provide an overview of the Tweets, followed by interpretation to understand why Twitter may be important to people with TRD.

RESULTS

A total of 415 Tweets were organised into five clusters: self-diagnosis, symptoms, support, small wins and condition experts. These Tweets were interpreted as showing Twitter as a community for people with TRD. People had a collective sense of illness identity and were united in their experiences of TRD. However, users in the community also highlighted the absence of effective GP care, leading users to position themselves as condition experts. Users shared advice from a place of lived experience with the community but also shared potentially harmful information, including recommendations about nonevidence-based medications.

CONCLUSIONS

Findings illuminate the benefits of the TRD Twitter community and also highlight that the perception of a lack of knowledge and support from GPs may lead community members to advise nonevidenced-based medications.

PATIENT AND PUBLIC CONTRIBUTION

This study was led by a person with lived experience of TRD and bipolar. Two public contributors with mental health conditions gave feedback on our study protocol and results.

The effect of mobile-based logotherapy on depression, suicidal ideation, and hopelessness in patients with major depressive disorder: a mixed-methods study

Major depressive disorder is one of the most common psychiatric disorders in the world. It is essential to study and use effective, available, and affordable psychotherapy methods along with drug therapy to manage the symptoms of this disease. Therefore, the current study aimed to determine the effect of mobile phone-based logotherapy on depression, suicidal ideation, and hopelessness in patients with major depressive disorder by using a mixed-methods approach. In the first phase of this mixed-methods study, 70 patients completed the quantitative phase (control group = 35, intervention group = 35). The intervention group received an 8-week mobile-based logotherapy program via WhatsApp (one 180-min module per week) combined with sertraline, while the control group received just sertraline plus education about pharmacotherapy. Data was collected before, immediately after the intervention, and 3 months later using the Beck depression inventory short form items (BDI-13), the Beck hopelessness scale (BHS), and the Beck scale for suicide ideation (BSSI). Then, a qualitative study on the intervention group was conducted to explain the findings of the quantitative phase. The repeated measure MANOVA revealed a significant interaction effect of time and group on the set of dependent variables (F(6,63) = 25.218, P < 0.001). Qualitative analysis confirmed the efficacy of sertraline plus mobile-based logotherapy on depression, suicidal ideation, and hopelessness in the intervention group. Three key themes extracted from the participants' experiences of mobile-based logotherapy were "efficient instruction", "user-friendly intervention" and "constructive change". Mobile-based logotherapy through WhatsApp was an effective psychotherapy method for decreasing depression, hopelessness, and suicidal ideation in patients with major depressive disorder. It is suggested that educational, institutional, and technological infrastructure for providing and using mobile-based logotheapy for patients with major depressive disorder be considered in the mental health care system.

A Scoping Review of Physician Advocacy on Twitter

Twitter has been adopted by physicians across most medical specialties; it allows for the wide dissemination of information and calls to action, brings new stakeholders into collations, promotes academic engagement, and fosters collaboration between academia and private practice. In this review of the literature, we briefly outline the state of advocacy in health care and summarize current Twitter-based advocacy efforts in the major specialties of health care, identifying both successful strategies as well as gaps in Twitter advocacy research. Relevant articles were obtained via PubMed and Google Scholar searches using the phrases "Twitter advocacy healthcare," "[specialty name] Twitter" and "[specialty name] Twitter advocacy." Several overarching themes were found to be widely utilized in specialty-specific discussions of Twitter advocacy efforts: organizing under a specific hashtag, fostering dialogue between stakeholders, and tweeting using personalized, action-oriented language. Fields such as pediatrics, heme/onc, ENT, and ophthalmology have most thoroughly embraced the desire to learn how to most effectively advocate on Twitter. Other fields such as OBGYN, cardiology, and surgery have less academic focus on online advocacy. Outside of advocacy efforts, the research and academic benefits of Twitter are well described in nearly every specialty. In conclusion, while clinicians are encouraged to advocate online, only broad strategies for online engagement are currently offered. Additional research into the details of how to successfully create an online profile and Twitter presence is needed to ensure all physicians are able to maximize their advocacy efforts, with clarification of the goals and objectives of this engagement also required.

Leveraging Implementation Science in the Treatment of Tobacco Use Disorder

The tobacco use disorder field has an armamentarium of approaches to help people quit smoking: medication-based treatment for tobacco use, digital therapeutics for just-intime behavioral interventions, genetic and metabolic biomarkers to guide tobacco treatment, to name a few. Whether the treatment approach is old or new, an underlying truth remains: the benefit is only as great as the extent to which these treatment approaches reach individuals who need them most and prove effective and feasible to implement in real-world settings. Further, certain treatments tend to be used more robustly in practice, namely, those that address a great need yet are low in cost, burden, and risk of clinical harms. This is where implementation science comes in, providing guidance on how best to get effective treatments adopted and used in clinical and community settings. Implementation science holds the keys to the uptake and routine use of evidence-based treatments and should be more fully leveraged in the tobacco use disorder field. At the same time, disruptive technologies in treatment are breaking new ground, pushing the field of implementation science to build a bigger "toolbox" of ways to improve access and quality of treatment in an ever-evolving landscape. In this paper, we underscore this synergy between tobacco treatment and implementation science. We spotlight emerging trends in tobacco use, effective and emerging treatment approaches for tobacco use, and ways that implementation science intersects with the current and evolving landscape of tobacco use and substance use disorder more broadly.

Sharing health information across online platforms: A systematic review

Advanced Web 2.0 communication technologies have facilitated health-related information (HRI) sharing on the Internet. Especially, the COVID-19 pandemic and lockdowns around the world have forced more people to turn to the Internet for HRI. A better understanding of users' sharing content and sharing behavior can help communicators improve health literacy, raise community awareness, and facilitate social support exchanges. This paper reports the results of a systematic review of online HRI sharing literature, including key research topics, theories and methods used in past studies, and key factors of sharing behavior across online platforms. Following the PRISMA procedure for a systematic review, 58 articles were identified and analyzed using keyword matching, thematic analysis, and expert review. Guided by the platform theory, our findings differentiated five types of online platforms that differently influenced online users' sharing content and sharing purposes, including micro-blogs, social network sites, online health communities, social question and answer sites, and Wikis. The findings also clarify five main research topics and applicable theories used in each topic, including personal health sharing, health-related knowledge sharing, general health message diffusion, outcomes of HRI sharing, and exploratory research. Key factors of sharing behavior and potential sharing outcomes are also reviewed and summarized in the research framework developed from the motivation theory. Our study contributes to the understanding of online sharing behavior and provides implications for health communicators to develop effective health campaigns. Potential research directions are also identified and discussed.

Investigating the Content of #UequalsU on Twitter

Undetectable = Untransmittable (U = U) is the scientific fact that HIV cannot be transmitted when an individual is virally suppressed. This breakthrough discovery has the potential to greatly reduce HIV stigma and its negative effects. However, U = U is not widely known. Given that Twitter has the potential to raise awareness of health issues, the purpose of this study was to analyze the content of the #UequalsU on Twitter. The results showed that mentioning sex and mentioning love were strong predictors that a tweet would be liked and retweeted. This information could help to spread the message of U = U more widely and potentially lessen HIV stigma.

Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis

BACKGROUND

Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited.

OBJECTIVE

This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health.

METHODS

This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics).

RESULTS

The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the "zone diet" network and the least prominent in the "soy-free," "vegan," "dairy-free," and "gluten-free" diet networks.

CONCLUSIONS

Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web.

Selfie Videos to Reduce Stigma and Increase Treatment Seeking Among Youths: Two Noninferiority Randomized Controlled Trials

OBJECTIVE

Confronting stigma early in life could enhance treatment seeking. In two randomized controlled trials (RCTs), one focused on psychosis and the other on adolescent depression, the efficacy and equivalence of brief social contact-based videos were evaluated and compared with a control condition. The outcomes of interest were changes in illness-related stigma and treatment-seeking intention. The hypotheses were that the intervention videos would show greater efficacy than control conditions and that traditional and selfie videos would demonstrate similar efficacy.

METHODS

Young adults (study 1, N=895) and adolescents (study 2, N=637) were randomly assigned to view intervention videos (in traditional or selfie styles) or to a control condition. In short videos (58-102 seconds), young presenters humanized their illness by emotionally describing their struggles and discussing themes of recovery and hope.

RESULTS

Repeated-measures analyses of variance and paired t tests showed significant differences in stigma and treatment seeking between the intervention and control groups and similar efficacy of the traditional and selfie videos. Cohen's d effect sizes ranged from 0.31 to 0.76 for changes in stigma from baseline to 30-day follow-up in study 1 and from 0.13 to 0.47 for changes from baseline to postintervention in study 2.

CONCLUSIONS

The RCTs demonstrated the efficacy of brief videos, both traditional and selfie, in reducing illness-related stigma among young adults and adolescents and in increasing treatment-seeking intention among adolescents. Future studies should explore the effects of brief videos presented by social media influencers on mental health stigma and treatment engagement.

Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study

BACKGROUND

Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use-associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment.

OBJECTIVE

This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media.

METHODS

Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts).

RESULTS

Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI -0.4289 to -0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting.

CONCLUSIONS

Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms.

Social media-based interventions for adolescent and young adult mental health: A scoping review

Background

Mental health conditions are common among adolescents and young adults, yet few receive adequate mental health treatment. Many young people seek support and information online through social media, and report preferences for digital interventions. Thus, digital interventions deployed through social media have promise to reach a population not yet engaged in treatment, and at risk of worsening symptoms.

Objective

In this scoping review, we aimed to identify and review empirical research on social media-based interventions aimed at improving adolescent and young adult mental health. A secondary objective was to identify the features and functionalities of platforms described as social media.

Methods

Adhering to the PRISMA-ScR guidelines for scoping reviews, the search was conducted in PubMed MEDLINE; Embase Central Register of Controlled Trials (Wiley); PsycINFO (Ebsco); Scopus; Web of Science; IEEE Xplore; ACM Digital Library; and ClinicalTrials.gov from inception until November 2021. Studies were included if they involved adolescents or young adults (10-26 years of age) that meet clinical, or subclinical, levels of a mental health condition and include a pre- and post-assessment of mental health outcomes.

Results

Among the 18,380 references identified, 15 met full inclusion criteria and were published between 2017 and 2021-this included four randomized controlled trials, seven non-randomized pre-post trials, and four were experimental or quasi-experimental designs. Just five studies were delivered through an existing social media site (Facebook or Pixtori), with the remainder focused on purpose-built networks. Three studies involved adolescents or young adults who self-reported a mental health condition, seven involved young people diagnosed with a mental health condition by a clinician or who scored above a clinical threshold on valid clinical measure, three involved college students without a mental health inclusion criterion, and two studies focused on young people with a cancer diagnosis.

Conclusions

The review highlights innovations in the delivery of mental health interventions, provides preliminary evidence of the ability of social media interventions to improve mental health outcomes, and underscores the need for, and merit of, future work in this area. We discuss opportunities and challenges for future research, including the potential to leveragei existing peer networks, the use of just-in-time interventions, and scaling interventions to meet need.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review

BACKGROUND

Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear.

OBJECTIVE

This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers.

METHODS

We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention.

RESULTS

We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented.

CONCLUSIONS

Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed.

TRIAL REGISTRATION

PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis

Background

“Data Saves Lives” is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area.

Objective

This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users’ medical data.

Methods

Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory.

Results

Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag.

Conclusions

The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to “Twitter bubbles”: groups of similar-minded Twitter users.

Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study

BACKGROUND

The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies.

OBJECTIVE

This study aimed to build a social media-based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic.

METHODS

This study used COVID-19-related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health-related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states.

RESULTS

We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to "depression," whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to "anxiety," "insomnia," and "addiction," respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, "students' pressure," "panic buying," and "fuel problems") than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05).

CONCLUSIONS

The impact of COVID-19 and the corresponding control measures on the public's mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct.

Exploring ADHD Users’ Behavior on Twitter: A Comparative Analysis of Tweet Content and User Interactions (Preprint)

BACKGROUND

With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media.

OBJECTIVE

This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets.

METHODS

First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users' posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups.

RESULTS

In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001).

CONCLUSIONS

This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection.

A Theoretically Based Analysis of Twitter Conversations about Trauma and Mental Health: Examining Responses to Storylines on the Television Show Queen Sugar

Entertainment programming in the United States has long addressed major public health issues. In the present study, we used a culture-centric approach to systematically investigate the role of television storylines in facilitating health-related conversations on social media. In particular, we examined Twitter conversations about sexual and police-involved trauma prompted by portrayals on the fictional television drama Queen Sugar. Guided by the culture-centric model of narratives in health promotion, we classified the tweets (N = 1,671) into four main thematic categories: identification, social proliferation, emotions, and intentions. The analysis also revealed several subthemes, including identification with characters and cultural elements, expressions of pain and joy, information seeking and sharing, and the need to address intergenerational trauma and promote intergenerational conversations. The data suggests that Twitter may provide a vehicle for engaging in difficult conversations. We discuss the theoretical and practical implications of the study for mental health communication with Black Americans.

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.

Patterns of Internet Use, and Associations with Loneliness, amongst Middle-Aged and Older Adults during the COVID-19 Pandemic

Loneliness among older adults is a major societal problem with consequences for health and wellbeing; this has been exacerbated by the coronavirus pandemic. The present study investigated associations between internet use, including frequency and type of use, and loneliness in a large UK sample of middle-aged and older adults, aged 55–75 (n = 3500) from the English Longitudinal Study of Ageing (ELSA) cohort study. Our findings indicated a clear relationship between the frequency of internet use and subjective loneliness. Those who used the internet more than once a day reported feeling less lonely than those who used the internet once a week or less. We also found that those who used the internet for e-mail communication were less lonely. However, individuals indicated higher levels of loneliness when the internet was used for information searches about health. Regarding sociodemographic factors underlying internet usage, less frequent use was seen amongst individuals who lived alone, people who were not employed, who had lower education levels, and lower sociodemographic status. Additionally, gender differences were found in the type of internet use: males report using the internet for e-mail communication more than females, while females’ internet use for health-related information searches was higher than in males. In sum, findings suggest that intervention strategies that promote internet access amongst middle-aged and older people could be useful for tackling loneliness and point to the groups within society that should be the focus of such interventions.

An Update of Peer Support/Peer Provided Services Underlying Processes, Benefits, and Critical Ingredients

The purpose of this article is to delineate the current state-of-the-knowledge of peer support following the framework employed in the 2004 article (Solomon, Psychiatr Rehabil J. 2004;27(4):392-401 1). A scoping literature was conducted and included articles from 1980 to present. Since 2004, major growth and advancements in peer support have occurred from the development of new specializations to training, certification, reimbursement mechanisms, competency standards and fidelity assessment. Peer support is now a service offered across the world and considered an indispensable mental health service. As the field continues to evolve and develop, peer support is emerging as a standard of practice throughout various, diverse settings and shows potential to impact clinical outcomes for service users throughout the globe. While these efforts have enhanced the professionalism of the peer workforce, hopefully this has enhanced the positive elements of these services and not diluted them.

Seeking and Sharing Mental Health Information on Social Media During COVID-19: Role of Depression and Anxiety, Peer Support, and Health Benefits

This study conducted a cross-sectional online survey (N = 865) to determine whether self-ratings of depression and anxiety, perceived peer support, and perceived health benefits of social media predicted mental health–related information seeking and sharing behaviors during the COVID-19 pandemic. Hierarchical regression models showed only depression self-ratings, anxiety self-ratings, and perceived health benefits predicted information seeking, whereas depression self-ratings, anxiety self-ratings, perceived peer support, and perceived health benefits all predicted information sharing. There was a statistically significant positive interaction of anxiety self-ratings and perceived peer support on information sharing. Participants’ experience of COVID-19 predicted both information seeking and sharing. Mental health–related information seeking and sharing differed across social media platforms, with YouTube, Facebook, and Instagram used most for information seeking and Facebook, Instagram, and Twitter used most for information sharing. Findings suggest social media mental health–related seeking and sharing behaviors have the potential to facilitate coping surrounding mental health.

Using Twitter to understand perspectives and experiences of dementia and caregiving at the beginning of the COVID-19 pandemic

The COVID-19 pandemic has placed a tremendous burden on all of society, particularly among vulnerable populations such as people living with dementia and their caregivers. Efforts to understand the impact of the COVID-19 pandemic on those living with dementia are crucial towards addressing needs during the pandemic and beyond. This qualitative descriptive study includes a thematic analysis of 6938 tweets from March 17–24, 2020, that included direct or indirect references to COVID-19 and at least one of the following terms/hashtags: Alzheimer, #Alzheimer, dementia, and #dementia. Five themes were identified: continuing care, finding support, preventing spread of COVID-19, maintaining human rights, and the impact of the pandemic on the daily lives of people living with dementia. People living with dementia and their families faced unique challenges related to caregiving, maintaining social connectedness while trying to follow public health guidelines, and navigating the convergence of COVID-19 and dementia-related stigma. Data from Twitter can be an effective means to understand the impacts of public health emergencies among those living with dementia and how to address their needs moving forward by highlighting gaps in practice, services, and research.

Phubbing among Lebanese young adults: Scale validation and association with mental health (depression, anxiety, and stress)

Mobile phones use has not been without several social and psychological problems, specifically during the fast spread of the COVID-19 infection, which imposed strict restrictions and isolation. This research principal aims were to (1) confirm the validity of the Generic Scale of Phubbing in Arabic (GSP), and (2) evaluate the association between phubbing and mental health (depression, anxiety and stress). A first cross-sectional study enrolled 203 participants to confirm the factor structure of the phubbing scale among Lebanese young adults. A confirmatory factor analysis (CFA) was carried out on the whole sample using SPSS AMOS v.24 to confirm the four-factor structure of the GSP. The root mean square error of approximation (RMSEA) statistic, the comparative fit index (CFI) and the Tucker Lewis Index (TLI) were used to evaluate the goodness-of-fit of the model. RMSEA values ≤0.08 and ≤ 0.10 indicate a good and acceptable fit respectively. CFI and TLI values ≥0.90 indicate good model fit. A second cross-sectional study enrolled 461 respondents (18-29 years old) to conduct the multivariable analysis. The fit indices values were as follows: χ2/df = 181.74/84 = 2.16, TLI = .92, CFI = .94 and RMSEA = .076 [95% CI .061-.091] respectively, indicating an excellent fit of the model. The results of the multiple linear regression using the ENTER model, when taking the phubbing score as the dependent variable, showed that female gender (β = 0.11; t(454) = 2.50; p = .013), more stress (β = 0.27; t(454) = 3.94; p < .001), more anxiety (β = 0.30; t(454) = 4.24; p < .001), and older age (β = 0.28; t(454) = 6.12; p < .001) were positively correlated with higher phubbing, or higher household crowding index (β = -0.15; t(454) = -3.62; p < .001) was significantly correlated with less phubbing. The results of this study were able to confirm the validity of the Arabic version of the GSP scale. This will allow Lebanese clinicians to use this validated tool to screen for the presence of the phubbing phenomenon within this age group. We propose finding possible correlation between phubbing and others factors (such as obsession and loneliness) and validating this scale in other Arabic-speaking countries.