#Sad: Twitter Content Predicts Changes in Cognitive Vulnerability and Depressive Symptoms

Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models

Text content analysis for depression detection using machine learning techniques has become a prominent area of research. However, previous studies focused mainly on analyzing the textual content, neglecting the fundamental factors driving text generation. Consequently, existing models face the challenge of poor generalization to out-of-domain data as they struggle to capture the crucial features of depression. To address this, we propose a novel computational perspective of "stimulus-response patterns" that brings us closer to the essence of clinical diagnosis of depression. Adopting this computational perspective allows us to conceptually unify diverse datasets and generalize this perspective to common datasets in the field. We introduce the Stimulus-Response Patterns-aware Network (SRP-Net) as an exemplary approach within this computational perspective. To assess the performance of the SRP-Net, we constructed a multi-stimulus dataset and conducted experimental evaluations, demonstrating its exceptional cross-stimulus generalizability. Furthermore, we demonstrated the promising performance of SPR-Net in real medical scenarios and conducted an interpretability analysis of the stimulus-response patterns. Our research investigates the critical role of stimulus-response patterns in enhancing the generalizability of text-based depression detection models, which can potentially facilitate data-driven depression detection to approach the diagnostic accuracy of psychiatrists.

Social media use and academic, social, and career development among college students with disabilities

OBJECTIVE

This study explores the use of social media and its impact on the college life of students with disabilities.

PARTICIPANTS

341 college students who identified as having disabilities were recruited from two universities in the Northeastern United.

METHODS

Multivariate multiple regression models examined the relationship between social media use and academic achievement, social connectedness, and work preparedness.

RESULTS

The findings indicate that students' GPA was not associated with any social media use variables, but the time spent on using social media was negatively associated with the work hope and social connectedness. Social media learning was positively associated with work preparedness and social connectedness.

CONCLUSIONS

The study findings suggest that social media used for learning purposes can have a positive impact on career and social development among college students with disabilities. However, the time spent for non-learning purposes did not show positive benefits.

Artificial intelligence language predictors of two-year trauma-related outcomes

BACKGROUND

Recent research on artificial intelligence has demonstrated that natural language can be used to provide valid indicators of psychopathology. The present study examined artificial intelligence-based language predictors (ALPs) of seven trauma-related mental and physical health outcomes in responders to the World Trade Center disaster.

METHODS

The responders (N = 174, Mage = 55.4 years) provided daily voicemail updates over 14 days. Algorithms developed using machine learning in large social media discovery samples were applied to the voicemail transcriptions to derive ALP scores for several risk factors (depressivity, anxiousness, anger proneness, stress, and personality). Responders also completed self-report assessments of these risk factors at baseline and trauma-related mental and physical health outcomes at two-year follow-up (including symptoms of depression, posttraumatic stress disorder, sleep disturbance, respiratory problems, and GERD).

RESULTS

Voicemail ALPs were significantly associated with a majority of the trauma-related outcomes at two-year follow-up, over and above corresponding baseline self-reports. ALPs showed significant convergence with corresponding self-report scales, but also considerable uniqueness from each other and from self-report scales.

LIMITATIONS

The study has a relatively short follow-up period relative to trauma occurrence and a limited sample size.

CONCLUSIONS

This study shows evidence that ALPs may provide a novel, objective, and clinically useful approach to forecasting, and may in the future help to identify individuals at risk for negative health outcomes.

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

A Systematic review of the validity of screening depression through Facebook, Twitter, Instagram, and Snapchat

BACKGROUND

The aim of this study was to determine the validity of using social media for depression screening.

METHOD

Article searches on PubMed and PsycINFO from database inception to August 20, 2019 were completed with a search string and filters.

RESULTS

15 articles made the inclusion criteria. Facebook, Twitter, and Instagram profiles of depressed people were distinguishable from nondepressed people shown by social media markers. Facebook studies showed that having fewer Facebook friends and mutual friends, posting frequently, and using fewer location tags positively correlated with depressive symptoms. Also, Facebook posts with explicit expression of depressive symptoms, use of personal pronouns, and words related to pain, depressive symptoms, aggressive emotions, and rumination predicted depression. Twitter studies showed that the use of "past focus" words, negative emotions and anger words, and fewer words per Tweet positively correlated with depression. Finally, Instagram studies showed that differences in follower patterns, photo posting and editing, and linguistic features between depressed people and nondepressed people could serve as a marker.

LIMITATIONS

The primary articles analyzed had different methods, which constricts the amount of comparisons that can be made. Further, only four social media platforms were explored.

CONCLUSION

Social media markers like number and content of Facebook messages, linguistic variability in tweets and tweet word count on Twitter, and number of followers, frequency of Instagram use and the content of messages on Instagram differed between depressed people and nondepressed people. Therefore, screening social media profiles on these platforms could be a valid way to detect depression.