LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts

Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts. Individuals seek social connectedness on social media to boost and alleviate their loneliness. Social media platforms allow people to express their thoughts, experiences, beliefs, and emotions. Prior studies on mental health from social media have focused on symptoms, causes, and disorders. Whereas an initial screening of social media content for interpersonal risk factors and low self-esteem may raise early alerts and assign therapists to at-risk users of mental disturbance. Standardized scales measure self-esteem and interpersonal needs from questions created using psychological theories. In the current research, we introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit. Through an annotation approach involving checks on coherence, correctness, consistency, and reliability, we ensure gold-standard for supervised learning. We present results from different deep language models tested using two data augmentation techniques. Our findings suggest developing a class of language models that infuses psychological and clinical knowledge

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research

Deteksi Komentar Cyberbullying pada Media Sosial Instagram Menggunakan Algoritma Random Forest

Cyberbullying is a phenomenon on social media where technological devices are used to insult, demean, and disrespect others. This can cause mental disorders such as loss of self-confidence, stress, depression, and even suicidal tendencies. The Ditch The Label survey, conducted by a British research institute, identified Instagram as the social media platform with the highest incidence of cyberbullying. The aim of this research is to determine the best accuracy based on the classification results of the cyberbullying comment dataset and to detect new comments as either bullying or non-bullying. One method that can be used is the random forest algorithm, which combines several similar or different methods, such as decision trees, in the classification process. The results of the classification of the testing data using the random forest algorithm show the highest accuracy of 84% in the last hyperparameter tuning combination. The built model can also detect new comments with fairly good predictive results. Suggestions for further research include classifying cyberbullying comments into more specific categories, such as racist or sexist comments

The longitudinal effect of social media use on adolescent mental health in the UK: findings from the UK Longitudinal Household Study

BACKGROUND: Cross-sectional studies have suggested an association between the use of social media and depression and anxiety in young people. We examined the longitudinal relationship between social media use and young people's mental health, and the role of self-esteem and social connectedness as potential mediators. METHODS: Adolescents (aged 10-15 years) from the UK Longitudinal Household Study (2009-19) were included. Mental health was measured by the Strengths and Difficulties Questionnaire Total Difficulties score. The number of hours spent on social media was measured on a 5-point scale, from zero to ≥7 h. Self-esteem and social connectedness were measured at ages 13-14 years. Covariates included demographic and household variables. Unadjusted and adjusted multilevel linear regression models explored whether social media use at ages 12-13 years predicted mental health at ages 14-15 years (expressed as beta values and 95% CIs). Path analysis with structural equation modelling was used to investigate the mediation pathways. FINDINGS: We included 3228 adolescents (1659 [51·4%] girls and 1569 [48·6%] boys) for whom social media and mental health data at ages 12-13 years and 14-15 years were available. In adjusted analysis, no association between time spent on social media and poorer mental health was identified (n=2603; b=0·21 [95% CI -0·43 to 0·84]; p=0·52). In adjusted path analysis, there was no mediation of self-esteem (indirect effect; n=2316; b=0·24 [95% CI -0·12 to 0·66]; p=0·22) or social connectedness (indirect effect; -0·03 [-0·20 to 0·12]; p=0·74), but in unadjusted analysis, 68% of the effect of social media use on mental health was mediated by self-esteem (indirect effect; n=2569; 0·70 [0·15 to 1·30]; p=0.016) but not by social connectedness. Similar results were found when the analysis was re-run on a multiply imputed dataset that filled in missing values using multiple imputation. INTERPRETATION: Our data show the importance of longitudinal evidence. We found there was little evidence to suggest a causal relationship between the use of social media and mental health issues 2 years later. Interventions that address social media use alone might not improve young people's mental health, and considering factors such as self-esteem might be more effective. FUNDING: UK National Institute for Health Research School for Public Health Research (grant reference PD-SPH-2015). The views expressed are those of the authors and not necessarily those of the National Institute for Health Research or the UK Department of Health and Social Care

Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4, FastText baseline, and CNN-

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