Forecasting the onset and course of mental illness with Twitter data

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners\u27 average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset

Analyzing Tweets For Predicting Mental Health States Using Data Mining And Machine Learning Algorithms

Tweets are usually the outcome of peoples’ feelings on various topics. Twitter allows users to post casual and emotional thoughts to share in real-time. Around 20% of U.S. adults use Twitter. Using the word-frequency and singular value decomposition methods, we identified the behavior of individuals through their tweets. We graded depressive and anti-depressive keywords using the tweet time-series, time-window, and time-stamp methods. We have collected around four million tweets since 2018. A parameter (Depressive Index) is computed using the F1 score and Mathews correlation coefficient (MCC) to indicate the depressive level. A framework showing the Depressive Index and the Happiness Index is prepared with the time, location, and keywords and delivers F1 Score, MCC, and CI values. COVID-19 changed the routines of most peoples\u27 lives and affected mental health. We studied the tweets and compared them with the COVID-19 growth. The Happiness Index from our work and World Happiness Report for Georgia, New York, and Sri Lanka is compared. An interactive framework is prepared to analyze the tweets, depict the happiness index, and compare it. Bad words in tweets are analyzed, and a map showing the Happiness Index is computed for all the US states and was compared with WalletHub data. We add tweets continuously and a framework delivering an atlas of maps based on the Happiness Index and make these maps available for further study. We forecasted tweets with real-time data. Our results of tweets and COVID-19 reports (WHO) are in a similar pattern. A new moving average method was presented; this unique process gave perfect results at peaks of the function and improved the error percentage. An interactive GUI portal computes the Happiness Index, depression index, feel-good- factors, prediction of the keywords, and prepares a Happiness Index map. We plan to create a public web portal to facilitate users to get these results. Upon completing the proposed GUI application, the users can get the Happiness Index, Depression Index values, Happiness map, and prediction of keywords of the desired dates and geographical locations instantaneously

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Characteristics of Fame-Seeking Individuals Who Completed or Attempted Mass Murder in the United States

Previous researchers have found mass murderers characterized as loners, victims of bullying, goths, and individuals who had a psychotic break. A gap in the literature that remained concerned the motive and mindset of mass murderers before their attack, particularly those who seek fame, and why they are motivated by such violent intentions. The purpose of this study was to provide a deeper analysis of the characteristics of fame-seeking individuals who have completed or attempted mass murder, as well as insight into their behavior on social media. The conceptual framework consisted of a constructivist model, which guided the exploration the purposeful sample of 12 Americans who completed or attempted mass murder. The research questions aligned with themes provided by Bandura\u27s social learning theory, Sulloway\u27s theory of birth order and family dynamics, Millon and Davis\u27s psychopathy theories, O\u27Toole\u27s findings on the copycat effect, and Lankford\u27s criteria for fame-seeking mass murderers, and guided an analysis of open-source data. Six main themes among fame-seeking individuals in the United States who had completed or attempted mass murder emerged: (a) fame as primary motivation, (b) preoccupation with violence, (c) presence of specific role models/copycat behavior, (d) strong opinions about society/racial groups, (e) symptoms of narcissism/mood disorder/personality disorder, and (f) failed relationships. These findings add to the knowledge about mass murder and fame seeking. Social change may occur through recommended evaluation of and improvements in current mental health approaches, improved threat assessment, expanded education on characteristics of mass murderers, and dissemination of information related to mass murder

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Systems Engineering Approaches to Minimize the Viral Spread of Social Media Challenges

Recently, adolescents’ and young adults’ use of social media has significantly increased. While this new landscape of cyberspace offers young internet users many benefits, it also exposes them to numerous risks. One such phenomenon receiving limited research attention is the advent and propagation of viral social media challenges. Several of these challenges entail self-harming behavior, which combined with their viral nature, poses physical and psychological risks for the participants and the viewers. One example of these viral social media challenges that could potentially be propagated through social media is the Blue Whale Challenge (BWC). In the initial study we investigate how people portray the BWC on social media and the potential harm this may pose to vulnerable populations. We first used a thematic content analysis approach, coding 60 publicly posted YouTube videos, 1,112 comments on those videos, and 150 Twitter posts that explicitly referenced BWC. We then deductively coded the YouTube videos based on the Suicide Prevention Resource Center (SPRC) Messaging guidelines. We found that social media users post about BWC to raise awareness and discourage participating, express sorrow for the participants, criticize the participants, or describe a relevant experience. Moreover, we found most of the videos on YouTube violate at least 50% of the SPRC safe and effective messaging guidelines. These posts might have the problematic effect of normalizing the BWC through repeated exposure, modeling, and reinforcement of self-harming and suicidal behavior, especially among vulnerable populations, such as adolescents. A second study conducted a systematic content analysis of 180 YouTube videos (~813 minutes total length), 3,607 comments on those YouTube videos, and 450 Twitter posts to explore the portrayal and social media users’ perception of three viral social media-based challenges (i.e., BWC, Tide Pod Challenge (TPC), and Amyotrophic Lateral Sclerosis (ALS) Ice Bucket Challenge (IBC)). We identified five common themes across the challenges, including: education and awareness, criticizing the participants and blaming the victims, detailed information about the participants, giving viewers a tutorial on how to participate, and understanding seemingly senseless online behavior. We found that the purpose of posting about an online challenge varies based on the inherent risk involved in the challenge itself. However, analysis of the YouTube comments showed that previous experience and exposure to online challenges appear to affect the perception of other challenges in the future. The third study investigated the beliefs that lead adolescents and young adults to participate in these activities by analyzing the ALS IBC to represent challenges with minimally harmful behaviors intended to support philanthropic endeavors and the Cinnamon Challenge (CC), to represent those involving harmful behaviors that may culminate in injury. We conducted a retrospective quantitative study with a total of 471 participants between the ages of 13 and 35 who either had participated in the ALS IBC or the CC or had never participated in any online challenge. We used binomial logistic regression models to classify those who participated in ALS IBC or CC versus those who didn’t with the beliefs from the Integrated Behavioral Model (IBM) as predictors. Our findings showed that both CC and ALS IBC participants had significantly greater positive emotional responses, value for the outcomes of the challenge, and expectation of the public to participate in the challenge in comparison to individuals who never participated in any challenge. In addition, only CC participants perceived positive public opinion about the challenge and perceived the challenge to be easy with no harmful consequences, in comparison to individuals who never participated in any challenge. The findings from this study were used to develop interventions based on knowledge of how the specific items making up each construct apply specifically to social media challenges. In the last study, we showed how agent-based modeling (ABM) might be used to investigate the effect of educational intervention programs to reduce social media challenges participation at multiple levels- family, school, and community. In addition, we showed how the effect of these educational based interventions can be compared to social media-based policy interventions. Our model takes into account the “word of mouth” effect of these interventions which could either decrease participation in social media challenge further than expected or unintentionally cause others to participate