Fun with Frustration? TikTok Influencers' Emotional Expression Predicts User Engagement with COVID-19 Vaccination Messages.

This study examined what kinds of TikTok video and message features are associated with user engagement in the context of COVID-19 vaccination. Content analysis was applied to study a sample of 223 COVID-19 vaccination-related videos from creators with at least 10,000 followers. The content analysis involved coding themes, video formats, the valence of attitude toward vaccination, and emotional expressions from the influencers. A majority of videos showcased personal vaccination experiences, followed by fictitious dramas and instructional information. More fictitious dramas expressed unclear attitudes, neither explicitly supporting nor opposing the COVID-19 vaccine, compared to personal vaccination stories and instructional videos. Some imaginative and dramatic scenes, such as zombie transformation or dramatic spasms after taking the vaccines, were widely imitated across influencers, perhaps humorously, and raised concerns about diminishing positive images of vaccine uptake. Videos with simultaneous expression of humor and frustration significantly predicted engagement when the video content opposed or was uncertain about taking the vaccine, implying the effectiveness of mixed emotional attributes within a message. This study provides insight into how social context and message choices by creators interact to influence audience engagement.

Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum.

BACKGROUND: Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient. OBJECTIVE: The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers' substance use and well-being outcomes. METHODS: Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes. RESULTS: Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app's support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood. CONCLUSIONS: This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals' expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.

BACKGROUND: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. OBJECTIVE: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. METHODS: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. RESULTS: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. CONCLUSIONS: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Effect of an eHealth intervention on older adults' quality of life and health-related outcomes: a randomized clinical trial.

BACKGROUND: By 2030, the number of US adults age ≥65 will exceed 70 million. Their quality of life has been declared a national priority by the US government. OBJECTIVE: Assess effects of an eHealth intervention for older adults on quality of life, independence, and related outcomes. DESIGN: Multi-site, 2-arm (1:1), non-blinded randomized clinical trial. Recruitment November 2013 to May 2015; data collection through November 2016. SETTING: Three Wisconsin communities (urban, suburban, and rural). PARTICIPANTS: Purposive community-based sample, 390 adults age ≥65 with health challenges. EXCLUSIONS: long-term care, inability to get out of bed/chair unassisted. INTERVENTION: Access (vs. no access) to interactive website (ElderTree) designed to improve quality of life, social connection, and independence. MEASURES: Primary outcome: quality of life (PROMIS Global Health). Secondary: independence (Instrumental Activities of Daily Living); social support (MOS Social Support); depression (Patient Health Questionnaire-8); falls prevention (Falls Behavioral Scale). Moderation: healthcare use (Medical Services Utilization). Both groups completed all measures at baseline, 6, and 12 months. RESULTS: Three hundred ten participants (79%) completed the 12-month survey. There were no main effects of ElderTree over time. Moderation analyses indicated that among participants with high primary care use, ElderTree (vs. control) led to better trajectories for mental quality of life (OR=0.32, 95% CI 0.10-0.54, P=0.005), social support received (OR=0.17, 95% CI 0.05-0.29, P=0.007), social support provided (OR=0.29, 95% CI 0.13-0.45, P<0.001), and depression (OR= -0.20, 95% CI -0.39 to -0.01, P=0.034). Supplemental analyses suggested ElderTree may be more effective among people with multiple (vs. 0 or 1) chronic conditions. LIMITATIONS: Once randomized, participants were not blind to the condition; self-reports may be subject to memory bias. CONCLUSION: Interventions like ET may help improve quality of life and socio-emotional outcomes among older adults with more illness burden. Our next study focuses on this population. TRIAL REGISTRATION: ClinicalTrials.gov ; registration ID number: NCT02128789.

Patient-provider communication while using a clinical decision support tool: explaining satisfaction with shared decision making for mammography screening.

BACKGROUND: Clinical decision aids may support shared decision-making for screening mammography. To inform shared decision-making between patients and their providers, this study examines how patterns of using an EHR-integrated decision aid and accompanying verbal patient-provider communication predict decision-making satisfaction. METHODS: For 51 patient visits during which a mammography decision aid was used, linguistic characteristics of patient-provider verbal communication were extracted from transcribed audio recordings and system logs automatically captured uses of the decision aid. Surveys assessed patients' post-visit decisional satisfaction and its subcomponents. Linear mixed effects models assessed how patients' satisfaction with decision making was related to patterns of verbal communication and navigation of the decision aid. RESULTS: The results indicate that providers' use of quantitative language during the encounter was positively associated with patients' overall satisfaction, feeling informed, and values clarity. Patients' question-asking was negatively associated with overall satisfaction, values clarity, and certainty perception. Where system use data indicated the dyad had cycled through the decision-making process more than once ("looping" back through pages of the decision aid), patients reported improved satisfaction with shared decision making and all subcomponents. Overall satisfaction, perceived support, certainty, and perceived effectiveness of decision-making were lowest when a high number of navigating clicks occurred absent "looping." CONCLUSIONS: Linguistic features of patient-provider communication and system use data of a decision aid predict patients' satisfaction with shared decision making. Our findings have implications for the design of decision aid tools and clinician training to support more effective shared decision-making for screening mammography.