Language of ADHD in Adults on Social Media

Optimal interval and feature selection in activity data for detecting attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurobehavioral disorder that is common in children and adolescents. Inattention, impulsivity, and hyperactivity are the key symptoms of ADHD patients. Traditional clinical assessments delay ADHD diagnosis and increase undiagnosed cases and costs, as well. The use of deep learning (DL) and machine learning (ML)-based objective techniques for diagnosing ADHD has grown exponentially in recent years as the efficiency of early diagnosis has improved. This research highlights the significance of utilizing feature selection techniques before constructing machine learning models on activity datasets. It also explores the distinctions between specific time-interval activity data and broader interval activity data in identifying ADHD patients from the clinical control group. Five ML models were developed and tested to assess the performance of two sets of features and different categories of activity data in predicting ADHD. The study concludes with the following findings: (i) the model's performance showed a notable improvement of 0.11 in accuracy with the adoption of a precise feature selection process; (ii) activity data recorded in the morning and at night are more significant predictors of ADHD compared to other times; (iii) the utilization of specific time interval data is crucial for ADHD prediction; (iv) the random forest performs better than the other machine learning models used in the study, with 84% accuracy, 79% precision, 85% F1-score, and 92% recall. As we move into an era where early disease prediction is possible, combining artificial intelligence methods with activity data could create a strong framework for helping doctors make decisions that can be used far beyond hospitals.

Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers

We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F1-score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.

Effects of the need fulfillment given by opposite-sex friends on breakup considerations: A moderated mediation model

The purpose of this study was to investigate the impact of need fulfillment given by opposite-sex friends (NFOF) on breakup considerations, the mediating role of love commitment in this relationship, and the moderating role of need fulfillment given by romantic partners (NFRP). A total of 334 unmarried individuals in romantic relationships from Northwest China were invited to participate in the study. The findings revealed the following: (1) NFOF significantly and positively predicted breakup considerations. (2) This relationship is mediated by love commitment (3) The association between NFOF and breakup considerations was moderated by NFRP (in terms of the first mediation path). Specifically, those who hold higher levels of NFRP are appreciably buffered against the negative impact of NFOF on love commitment. These findings emphasize the crucial role of NFOF and NFRP in shaping love commitment and breakup considerations. Moreover, our research has important realistic implications: NFOF, as a trigger, has a negative effects the quality of romantic relationships and leads to breakup considerations. And, the key to maintaining a romantic relationship is to focus on their partners' need fulfillment as much as possible and increasing the level of their love commitment.

Digital Collateral Information Through Electronic and Social Media in Psychotherapy: Comparing Clinician-reported Trends Before and During the COVID-19 Pandemic

BACKGROUND

Patient clinical collateral information is critical for providing psychiatric and psychotherapeutic care. With the shift to primarily virtual care triggered by the COVID-19 pandemic, psychotherapists may have received less clinical information than they did when they were providing in-person care. This study assesses whether the shift to virtual care had an impact on therapists' use of patients' electronic and social media to augment clinical information that may inform psychotherapy.

METHODS

In 2018, we conducted a survey of a cohort of psychotherapists affiliated with McLean Hospital. We then reapproached the same cohort of providers for the current study, gathering survey responses from August 10, 2020, to September 1, 2020, for this analysis. We asked clinicians whether they viewed patients' electronic and social media in the context of their psychotherapeutic relationship, what they viewed, how much they viewed it, and their attitudes about doing so.

RESULTS

Of the 99 respondents, 64 (64.6%) had viewed at least 1 patient's social media and 8 (8.1%) had viewed a patient's electronic media. Of those who reported viewing patients' media, 70 (97.2%) indicated they believed this information helped them provide more effective treatment. Compared with the 2018 prepandemic data, there were significantly more clinicians with>10 years of experience reporting media use in therapy. There was also a significant increase during the pandemic in the viewing of media of adult patients and a trend toward an increase in viewing of media of older adult patients.

CONCLUSIONS

Review of patients' electronic and social media in therapy became more common among clinicians at a large psychiatric teaching hospital during the COVID-19 pandemic. These findings support continuing research about how reviewing patients' media can inform and improve clinical care.

Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data

BACKGROUND

Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors.

OBJECTIVE

The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors.

METHODS

A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted.

RESULTS

A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings.

CONCLUSIONS

Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

"For Me, 'Normality' is Not Normal": Rethinking Medical and Cultural Ideals of Midlife ADHD Diagnosis

According to psychiatry, Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic condition beginning in early life. Psychiatry advocates for early diagnosis to prevent comorbidities that may emerge in untreated cases. "Late"-diagnosis is associated with various hazards that might harm patients' lives and society. Drawing on fieldwork in Israel, we found that 'midlife-ADHDers,' as our informants refer to themselves, express diverse experiences including some advantages of being diagnosed as adults rather than as children. They share what it means to experience "otherness" without an ADHD diagnosis and articulate how being diagnosed "late" detached them from medical and social expectations and allowed some to nurture a unique ill-subjectivity, develop personal knowledge, and invent therapeutic interventions. The timeframe that psychiatry conceives as harmful has been, for some, a springboard to find their own way. This case allows us to rethink 'experiential time'-the meanings of timing and time when psychiatric discourse and subjective narratives intertwine.

ADHD and political participation: An observational study

BACKGROUND AND OBJECTIVE

Over the past decade, researchers have been seeking to understand the consequences of adult attention-deficit/hyperactivity disorder (ADHD) for different types of everyday behaviors. In this study, we investigated the associations between ADHD and political participation and attitudes, as ADHD may impede their active participation in the polity.

METHODS

This observational study used data from an online panel studying the adult Jewish population in Israel, collected prior the national elections of April 2019 (N = 1369). ADHD symptoms were assessed using the 6-item Adult ADHD Self-Report (ASRS-6). Political participation (traditional and digital), news consumption habits, and attitudinal measures were assessed using structured questionnaires. Multivariate linear regression analyses were conducted to analyze the association between ADHD symptoms (ASRS score <17) and reported political participation and attitudes.

RESULTS

200 respondents (14.6%) screened positive for ADHD based on the ASRS-6. Our findings show that individuals with ADHD are more likely to participate in politics than individuals without ADHD symptoms (B = 0.303, SE = 0.10, p = .003). However, participants with ADHD are more likely to be passive consumers of news, waiting for current political news to reach them instead of actively searching for it (B = 0.172, SE = 0.60, p = .004). They are also more prone to support the idea of silencing other opinions (B = 0.226, SE = 0.10, p = .029). The findings hold when controlling for age, sex, level of education, income, political orientation, religiosity, and stimulant therapy for ADHD symptoms.

CONCLUSIONS

Overall, we find evidence that individuals with ADHD display a unique pattern of political activity, including greater participation and less tolerance of others' views, but not necessarily showing greater active interest in politics. Our findings add to a growing body of literature that examines the impact of ADHD on different types of everyday behaviors.

Understanding Problematic Social Media Use in Adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD): A Narrative Review and Clinical Recommendations

Attention-Deficit/Hyperactivity Disorder (ADHD) is consistently associated with a host of social problems, such as victimization and difficulties in maintaining close friendships. These problems are not limited to offline relations but also manifest in the online social world, as previous research shows that ADHD is associated with problematic use of social media. Given the ubiquitous nature of social media, the goal of the current review is to understand why adolescents with ADHD demonstrate more problematic social media use than their typically developing peers. To this end, we provide a narrative review on the evidence for the link between ADHD and social media use, and consequently present an integrative framework, which encompasses neurobiological mechanisms (i.e., imbalance theory of brain development and dual pathway model of ADHD) and social mechanisms, including influences from peers and parents. We conclude that empirical work shows most consistent evidence for the link between problematic social media use and ADHD (symptoms), while intensity of social media use is also associated with several other behaviors and outcomes. Finally, we hypothesize how existing interventions for ADHD may work on the identified mechanisms and provide at-hand clinical recommendations for therapists working with adolescents with ADHD who exhibit problematic social media use.

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.

Understanding the expression of loneliness on Twitter across age groups and genders

Some individuals seek support around loneliness on social media forums. In this work, we aim to determine differences in the use of language by users—in different age groups and genders (female, male), who publish posts on Twitter expressing loneliness. We hypothesize that these differences in the use of language will reflect how these users express themselves and some of their support needs. Interventions may vary depending on the age and gender of an individual, hence, in order to identify high-risk individuals who express loneliness on Twitter and provide appropriate interventions for these users, it is important to understand the variations in language use by users who belong to different age groups and genders and post about loneliness on Twitter. We discuss the findings from this work and how they can help guide the design of online loneliness interventions.

Dysfunctional temporal stages of eye-gaze perception in adults with ADHD: a high-density EEG study

ADHD has been associated with social cognitive impairments across the lifespan, but no studies have specifically addressed the presence of abnormalities in eye-gaze processing in the adult brain. This study investigated the neural basis of eye-gaze perception in adults with ADHD using event-related potentials (ERP). Twenty-three ADHD and 23 controls performed a delayed face-matching task with neutral faces that had either direct or averted gaze. ERPs were classified using microstate analyses. ADHD and controls displayed similar P100 and N170 microstates. ADHD was associated with cluster abnormalities in the attention-sensitive P200 to direct gaze, and in the N250 related to facial recognition. For direct gaze, source localization revealed reduced activity in ADHD for the P200 in the left/midline cerebellum, as well as in a cingulate-occipital network at the N250. These results suggest brain impairments involving eye-gaze decoding in adults with ADHD, suggestive of neural signatures associated with this disorder in adulthood.

Eliciting Expressions of Emotion: An Exploratory Analysis of Alexithymia in Adults with Autism Utilising the APRQ

This study examined alternative methods for detecting alexithymia to the Toronto Alexithymia Scale-20 (TAS-20) by comparing the emotional linguistic performance of ASD and NT samples (n = 32 in each) on the Alexithymia Provoked Responses Questionnaire (APRQ). We utilised both the LIWC and tidytext approaches to linguistic analysis. The results indicate the ASD sample used significantly fewer affective words in response to emotionally stimulating scenarios and had less emotional granularity. Affective word use was correlated with ASD symptomatology but not with TAS-20 scores, suggesting that some elements of alexithymia are not well detected by the TAS-20 alone. The APRQ, in combination with the tidytext package, offers significant potential for sophisticated exploration of emotional expression in ASD.

Experiences of criticism in adults with ADHD: A qualitative study

People with ADHD are at high risk of receiving criticism from others, yet criticism has not been well researched in this population. This study aimed to provide a rich understanding of what experiences adults with ADHD traits have with criticism. As part of a larger study, 162 participants with ADHD and high ADHD traits provided a written response to an open question asking about their experiences of criticism from other people. Thematic analysis was used to identify five common themes in the responses. Behaviours associated with inattention were perceived as the most criticised, whilst impulsive behaviours were mostly criticised in social contexts. Criticism was perceived via numerous conducts and was reported to have negative consequences for self-worth and wellbeing. To cope, some participants avoided criticism or changed how they reacted, including trying to accept themselves as they are. The responses indicated that receiving understanding from others played an important role in whether criticism was perceived. Overall, the findings highlight the need for more knowledge, understanding and acceptance towards neurodiversity from the general population.

Challenges and Strategies to Mitigate Problematic Social Media Use in Psychiatric Disorders

LEARNING OBJECTIVES

After participating in this activity, learners should be better able to:• Outline and discuss strategies to mitigate problematic social media use in psychiatric disorders• Identify solutions to encourage healthy use.

ABSTRACT

Social media has been found to contribute to a variety of different psychiatric disorders, with recent research showing a complex relationship between social media use and mental health outcomes. This article outlines how the strategies that social media sites utilize to increase user engagement can differentially affect individuals with psychiatric disorders, and proposes solutions that may promote more healthy use. With these aims in view, the article (1) delineates the strategies, often unrecognized, that social media sites use to increase user engagement, (2) highlights how these strategies can affect individuals with psychiatric disorders, and (3) proposes novel solutions to encourage healthy use. The first step to creating innovative and universal interventions is to understand the challenges faced by individuals with psychiatric disorders when using social media.

Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population.

Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup

Using archived social media data, the language signatures of people going through breakups were mapped. Text analyses were conducted on 1,027,541 posts from 6,803 Reddit users who had posted about their breakups. The posts include users' Reddit history in the 2 y surrounding their breakups across the various domains of their life, not just posts pertaining to their relationship. Language markers of an impending breakup were evident 3 mo before the event, peaking on the week of the breakup and returning to baseline 6 mo later. Signs included an increase in I-words, we-words, and cognitive processing words (characteristic of depression, collective focus, and the meaning-making process, respectively) and drops in analytic thinking (indicating more personal and informal language). The patterns held even when people were posting to groups unrelated to breakups and other relationship topics. People who posted about their breakup for longer time periods were less well-adjusted a year after their breakup compared to short-term posters. The language patterns seen for breakups replicated for users going through divorce (n = 5,144; 1,109,867 posts) or other types of upheavals (n = 51,357; 11,081,882 posts). The cognitive underpinnings of emotional upheavals are discussed using language as a lens.

How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises

BACKGROUND

Twitter has emerged as a novel way for physicians to share ideas and advocate for policy change. #ThisIsOurLane (firearm injury) and #GetUsPPE (COVID-19) are examples of nationwide health care-led Twitter campaigns that went viral. Health care-initiated Twitter hashtags regarding major public health topics have gained national attention, but their content has not been systematically examined.

OBJECTIVE

We hypothesized that Twitter discourse on two epidemics (firearm injury and COVID-19) would differ between tweets with health care-initiated hashtags (#ThisIsOurLane and #GetUsPPE) versus those with non-health care-initiated hashtags (#GunViolence and #COVID19).

METHODS

Using natural language processing, we compared content, affect, and authorship of a random 1% of tweets using #ThisIsOurLane (Nov 2018-Oct 2019) and #GetUsPPE (March-May 2020), compared to #GunViolence and #COVID19 tweets, respectively. We extracted the relative frequency of single words and phrases and created two sets of features: (1) an open-vocabulary feature set to create 50 data-driven-determined word clusters to evaluate the content of tweets; and (2) a closed-vocabulary feature for psycholinguistic categorization among case and comparator tweets. In accordance with conventional linguistic analysis, we used a P<.001, after adjusting for multiple comparisons using the Bonferroni correction, to identify potentially meaningful correlations between language features and outcomes.

RESULTS

In total, 67% (n=4828) of #ThisIsOurLane tweets and 36.6% (n=7907) of #GetUsPPE tweets were authored by health care professionals, compared to 16% (n=1152) of #GunViolence and 9.8% (n=2117) of #COVID19 tweets. Tweets using #ThisIsOurLane and #GetUsPPE were more likely to contain health care-specific language; more language denoting positive emotions, affiliation, and group identity; and more action-oriented content compared to tweets with #GunViolence or #COVID19, respectively.

CONCLUSIONS

Tweets with health care-led hashtags expressed more positivity and more action-oriented language than the comparison hashtags. As social media is increasingly used for news discourse, public education, and grassroots organizing, the public health community can take advantage of social media's broad reach to amplify truthful, actionable messages around public health issues.

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

Purpose

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

Design/methodology/approach

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

Findings

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

Research limitations

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

Practical implications

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

Originality/value

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

Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media

BACKGROUND

The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.

OBJECTIVE

Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.

METHODS

We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets.

RESULTS

We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis.

CONCLUSIONS

We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Background

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study.

Objective

This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset.

Methods

This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets.

Results

Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19).

Conclusions

To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

Variability in Language used on Social Media prior to Hospital Visits

Forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records. An ensemble machine learning model forecasted ED visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.

Examining the Phenomenon of Quarter-Life Crisis Through Artificial Intelligence and the Language of Twitter

Quarter-life crisis (QLC) is a popular term for developmental crisis episodes that occur during early adulthood (18–30). Our aim was to explore what linguistic themes are associated with this phenomenon as discussed on social media. We analyzed 1.5 million tweets written by over 1,400 users from the United Kingdom and United States that referred to QLC, comparing their posts to those used by a control set of users who were matched by age, gender and period of activity. Logistic regression was used to uncover significant associations between words, topics, and sentiments of users and QLC, controlling for demographics. Users who refer to a QLC were found to post more about feeling mixed emotions, feeling stuck, wanting change, career, illness, school, and family. Their language tended to be focused on the future. Of 20 terms selected according to early adult crisis theory, 16 were mentioned by the QLC group more than the control group. The insights from this study could be used by clinicians and coaches to better understand the developmental challenges faced by young adults and how these are portrayed naturalistically in the language of social media.

Studying expressions of loneliness in individuals using twitter: an observational study

OBJECTIVES

Loneliness is a major public health problem and an estimated 17% of adults aged 18-70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words 'lonely' or 'alone' in their Twitter timeline and correlate their posts with predictors of mental health.

SETTING AND DESIGN

From approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words 'lonely' or 'alone' and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users' posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018-2019.

PRIMARY OUTCOME MEASURES

We evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users' timelines, measured by area under curve (AUC).

RESULTS

Twitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86.

CONCLUSIONS

Users' Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.

Distress, Suicidality, and Affective Disorders at the Time of Social Networks

PURPOSE OF REVIEW

We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series.

RECENT FINDINGS

Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement. Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

Patients' willingness to share digital health and non-health data for research: a cross-sectional study

BACKGROUND

Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients' perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use.

METHODS

Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types' health relatedness. Factor analysis was used to identify the structure in patients' perceptions of willingness to share different digital data, and their health relatedness.

RESULTS

Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know.

CONCLUSION

Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

Incorporating Information From Electronic and Social Media Into Psychiatric and Psychotherapeutic Patient Care: Survey Among Clinicians

BACKGROUND

Obtaining collateral information from a patient is an essential component of providing effective psychiatric and psychotherapeutic care. Research indicates that patients' social and electronic media contains information relevant to their psychotherapy and clinical care. However, it remains unclear to what degree this content is being actively utilized by clinicians as a part of diagnosis or therapy. Moreover, clinicians' attitudes around this practice have not been well characterized.

OBJECTIVE

This survey aimed to establish the current attitudes and behaviors of outpatient clinicians regarding the incorporation of patients' social and electronic media into psychotherapy.

METHODS

A Web-based survey was sent to outpatient psychotherapists associated with McLean Hospital in Belmont, Massachusetts. The survey asked clinicians to indicate to what extent and with which patients they reviewed patients' social and electronic media content as part of their clinical practice, as well as their reasons for or against doing so.

RESULTS

Of the total 115 respondents, 71 (61.7%) indicated that they had viewed at least one patient's social or electronic media as part of psychotherapy, and 65 of those 71 (92%) endorsed being able to provide more effective treatment as a result of this information. The use of either short message service text messages or email was significantly greater than the use of other electronic media platforms (χ²₁=24.1, n=115, P<.001). Moreover, the analysis of survey responses found patterns of use associated with clinicians' years of experience and patient demographics, including age and primary diagnosis.

CONCLUSIONS

The incorporation of patients' social and electronic media into therapy is currently common practice among clinicians at a large psychiatric teaching hospital. The results of this survey have informed further questions about whether reviewing patient's media impacts the quality and efficacy of clinical care.

Evaluating the predictability of medical conditions from social media posts

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.

Exploring how adolescents with ADHD use and interact with technology

INTRODUCTION

The ubiquity of technology is reshaping the way teens express themselves and interact with peers. Considering that teens with attention-deficit/hyperactivity disorder (ADHD) experience a range of social impairments and that risk behaviors have the potential to be more widespread and damaging online, understanding how teens with ADHD use the Internet is important.

METHODS

The current study included 58 teens (72.4% boys; 13-16 years old) from the United States of America with ADHD. Study aims were to examine these teens' Internet use frequency, preferred online activities, Facebook interactions, and online risk behaviors (i.e., cyberbullying and sexting). Associations between online behaviors and offline symptoms and behaviors were explored to identify potential risk and protective factors.

RESULTS

Findings suggested that teens with ADHD use technology in similar ways as do the general population of teens described in previous research but appeared at unique risk of cyberbullying behaviors. Offline risks were associated with online risk behaviors. Using Facebook was associated with online risks (e.g., weak online connections) and offline risks (e.g., poorer social skills and more internalizing symptoms).

CONCLUSIONS

Online social platforms permit the exploration of social behaviors via naturalistic observation. It is imperative researchers gain understanding of the increasingly prevalent online social worlds of teens. Such an understanding may enable researchers to formulate effective social interventions for teens with ADHD.

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets' novelty, utility, theme, and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and nonhealth organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

Cross-platform and cross-interaction study of user personality based on images on Twitter and Flickr

Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others’ posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood. In this study, we analyzed variation in image content with user personality across three interaction types (posts, likes and profile images) and two platforms, using a unique data set of users who are active on both Twitter and Flickr. Usage patterns on these two social media platforms revealed different aspects of users’ personality. Cross-platform data fusion is thus shown to improve personality prediction performance.