Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

A Scorecard Method for Detecting Depression in Social Media Users

A Scorecard Method for Detecting Depression in Social Media Users Netsanet Tefera Lina Zhou University of Maryland, Baltimore County {netsa2, zhoul}@umbc.edu Abstract Depression is one of the most prevalent mental health disorders today. Depression has become the leading causes of disability and premature mortality partly due to a lack of effective methods for early detection. This research explores how social media can be used as a tool to detect the level of depression in its users by proposing a scorecard method based on their user profiles. In the proposed method, depression is measured by a selected set of key dimensions along with their specific indicators, which are weighted based on their importance for signaling depression in the literature. To evaluate the scorecard method, we compared three types of social media users: users who committed suicide due to depression, users who were likely suffering from depression, and users who were unlikely suffering from depression. The empirical results demonstrate the effectiveness of the scorecard method in detecting depression

Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among Depression Individuals

Depression has emerged as a significant mental health concern due to a variety of factors, reflecting broader societal and individual challenges. Within the digital era, social media has become an important platform for individuals navigating through depression, enabling them to express their emotional and mental states through various mediums, notably music. Specifically, their music preferences, manifested through sharing practices, inadvertently offer a glimpse into their psychological and emotional landscapes. This work seeks to study the differences in music preferences between individuals diagnosed with depression and non-diagnosed individuals, exploring numerous facets of music, including musical features, lyrics, and musical networks. The music preferences of individuals with depression through music sharing on social media, reveal notable differences in musical features and topics and language use of lyrics compared to non-depressed individuals. We find the network information enhances understanding of the link between music listening patterns. The result highlights a potential echo-chamber effect, where depression individual's musical choices may inadvertently perpetuate depressive moods and emotions. In sum, this study underscores the significance of examining music's various aspects to grasp its relationship with mental health, offering insights for personalized music interventions and recommendation algorithms that could benefit individuals with depression.Comment: arXiv admin note: text overlap with arXiv:2007.03137, arXiv:2205.03459 by other author

Content and Social Network Analyses of Depression-related Tweets of African American College Students

The prevalence of depression is higher among African American college students compared to their White counterparts. They are also more likely to disclose feelings of depression on Twitter. The aim of this exploratory study was to answer the following questions: What are the most common themes of depression-related tweets among African American college students? Are there differences in the social network characteristics of college students that have posted a depression-related tweet or retweet and those who have not? Content and social network analyses were conducted. The study results showed the most common themes focused on feelings of depression, casual mentions, and supportive messages. In addition, we observed that the social networks of users posting depression-related tweets have more mutual connections with their friends than the users who did not post a depression-related tweet. These findings may help to inform the design of social media interventions for African American college students

Interpersonal Emotion Regulation And Online Social Support For Depression: A Review

Online social interactions have become commonplace as social media sites like Facebook and Twitter have risen in popularity. People frequently share about life events, emotions, and even mental health concerns, and there is evidence to suggest that in many cases online sharing can be beneficial in decreasing negative emotions and widening peoples’ social support networks. Research is increasingly focused on the relationship between social media and mental health concerns like depression, but findings are often mixed, or even contradictory, and many questions remain open for future research. Understanding how people with depression use social media and interact with others online to regulate their symptoms is important in guiding future research and developing online interventions. This literature review proposes that interpersonal emotion regulation can be applied as a framework by which to better understand how people with depression interact online and seek out social support, and addresses directions for future research