Major depression constitutes a serious challenge in personal and public health. Tens of millions people each year suffer from only fraction receives adequate treatment. We explore the potential to use social media detect diagnose major depressive disorder individuals. first employ crowdsourcing compile set Twitter users who report being diagnosed with clinical depression, based on standard psychometric instrument. Through their postings over preceding onset we measure behavioral attributes...

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing forecasting this limited due to the paucity information regarding ideation, exacerbated by stigma illness. This paper fills gaps in literature developing statistical methodology infer which individuals could undergo transitions from health discourse suicidal We utilize semi-anonymous support communities on Reddit as unobtrusive data sources likelihood these shifts....

Social media is continually emerging as a platform of information exchange around health challenges. We study mental discourse on the popular social media:reddit. Building findings about seeking and sharing practices in online forums, like Twitter, we address three research First, present characterization self-disclosure inmental illness communities reddit. observe individuals discussing variety concerns ranging from daily grind to specific queries diagnosis treatment. Second, build...

Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as new type of lens in understanding depression populations. Information gleaned from bears to complement traditional survey techniques its ability provide finer grained measurements over time while radically expanding population sample sizes. present work on using crowdsourcing methodology build large corpus Twitter that have been shared by individuals diagnosed with...

We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about forthcoming influence childbirth behavior and mood new mothers. Using posts, we quantify postpartum changes in 376 mothers along dimensions engagement, emotion, network, linguistic style. then construct statistical from training set observations these measures before after reported childbirth, forecast significant The can classify who will change significantly...

The birth of a child is major milestone in the life parents. We leverage Facebook data shared voluntarily by 165 new mothers as streams evidence for characterizing their postnatal experiences. consider multiple measures including activity, social capital, emotion, and linguistic style participants' pre- periods. Our study includes detecting predicting onset post-partum depression (PPD). work complements recent on significant postpartum changes behavior, language, affect from Twitter data. In...

Search engines and social media are two of the most com-monly used online services; in this paper, we examine how users appropriate these platforms for health activi-ties via both large-scale log analysis a survey 210 people. While often turn to search learn about serious or highly stigmatic conditions, surprising amount sensitive information is also sought shared media, our case public plat-form Twitter. We contrast what content people seek vs. share on as well why they choose particular...

Pro-eating disorder (pro-ED) communities on social media encourage the adoption and maintenance of disordered eating habits as acceptable alternative lifestyles rather than threats to health. In particular, networking site Instagram has reacted by banning searches several pro-ED tags issuing content advisories others. We pre-sent first large-scale quantitative study investigating in aftermath moderation -- our dataset contains 2.5M posts between 2011 2014. find that community adopted...

Online social support is known to play a significant role in mental well-being. However, current research limited its ability quantify this link. Challenges exist due the paucity of longitudinal, pre- and post illness risk data, reliable methods that can examine causality between past availability future risk. In paper, we propose method measure how language comments Reddit health communities influences suicidal ideation future. Incorporating human assessments stratified propensity score...

Powered by machine learning techniques, social media provides an unobtrusive lens into individual behaviors, emotions, and psychological states. Recent research has successfully employed data to predict mental health states of individuals, ranging from the presence severity disorders like depression risk suicide. These algorithmic inferences hold great potential in supporting early detection treatment design interventions. At same time, outcomes this can pose risks such as issues incorrect,...

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

From the Arab Spring to Occupy Movement, social media has been instrumental in driving and supporting socio-political movements throughout world. In this paper, we present one of first investigations an activist movement around racial discrimination police violence, known as "Black Lives Matter". Considering Twitter a sensor for broader community's perception events related movement, study participation over time, geographical differences participation, its relationship protests that...

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, then evaluated applicability existing solutions. We compiled human-created COVID-19 abstracted it into narrative prompts for a model output AI-misinfo. found significant linguistic...

Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during course of health crisis. Experts have speculated that consuming misinformation online can potentially worsen mental individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify causal relationship between sharing misinformation, a strong indicator experiencing exacerbated anxiety. We conduct large-scale observational spanning over 80 million...

Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due the enormous rate of production new information: therefore, often forced analyze a judiciously selected “sample” data. Like other media phenomena, information diffusion is process–it affected by user context, and topic, in addition graph topology. This paper studies impact different attribute topology based sampling...

Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected j) from observed events emails j). Often overlooked, however, the impact of tie definition on corresponding network, in turn relevance inferred network research question interest. Here we problem inference for two email sets different size origin. In each case, generate a family networks parameterized by threshold condition...

Social media sites have struggled with the presence of emotional and physical self-injury content. Individuals who share such content are often challenged severe mental illnesses like eating disorders. We present first study quantifying levels illness severity (MIS) in social media. examine a set users on Instagram post pro-eating disorder tags (26M posts from 100K users). Our novel statistical methodology combines topic modeling novice/clinician annotations to infer MIS user's Alarmingly,...

Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts included expert input to evaluate the authenticity diagnostic self-disclosures.This study aims move noisy self-reports on social media more accurate identification diagnoses by exploring a human-machine partnered approach, wherein computational linguistic shared content is combined with...

Cultural and gender norms shape how mental illness therapy are perceived. However, there is a paucity of adequate empirical evidence around cultural dimensions illness. In this paper we situate social media as "lens" to examine these dimensions. We focus on large dataset individuals who self-disclose have an underlying health concern Twitter. Having identified genuine disclosures in data via semi-supervised learning, differences their posts, measured linguistic attributes topic models. Our...

The Werther effect describes the increased rate of completed or attempted suicides following depiction an individual's suicide in media, typically a celebrity. We present findings on prevalence this online platform: r/SuicideWatch Reddit. examine both posting activity and post content after death ten high-profile suicides. Posting increases reports celebrity suicides, exhibits considerable changes that indicate suicidal ideation. Specifically, we observe post-celebrity is more likely to be...

"Human-centered machine learning" (HCML) combines human insights and domain expertise with data-driven predictions to answer societal questions. This area's inherent interdisciplinarity causes tensions in the obligations researchers have humans whose data they use. paper studies how scientific papers represent research subjects HCML. Using mental health status prediction on social media as a case study, we conduct thematic discourse analysis 55 examine these representations. We identify five...

Hateful speech bears negative repercussions and is particularly damaging in college communities. The efforts to regulate hateful on campuses pose vexing socio-political problems, the interventions mitigate effects require evaluating pervasiveness of phenomenon as well impacts students' psychological state.Given growing use social media among students, we target above issues by studying online aspect a dataset 6 million Reddit comments shared 174 To quantify prevelence an community, devise...