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-

Triaging Content Severity in Online Mental Health Forums

Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for Information Science and Technology (2017

Lightme: Analysing Language in Internet Support Groups for Mental Health

Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Methods: Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout mental health forum for young people. Results: When comparing with the state-of-the-art, a solution mainly based on features from lexical resources, received the best classification performance for the crisis posts (52%), which is the most severe class. Six salient linguistic characteristics were found when analysing the crisis post; 1) posts expressing hopelessness, 2) short posts expressing concise negative emotional responses, 3) long posts expressing variations of emotions, 4) posts expressing dissatisfaction with available health services, 5) posts utilising storytelling, and 6) posts expressing users seeking advice from peers during a crisis. Conclusion: It is possible to build a competitive triage classifier using features derived only from the textual content of the post. Further research needs to be done in order to translate our quantitative and qualitative findings into features, as it may improve overall performance

Análisis del lenguaje en grupos de apoyo en Internet de salud mental

Assisting moderators to triage critical posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout.com mental health forum. When comparing with the state-of-the-art, our solution achieved the best classification performance for the crisis posts (52%), which is the most severe class.Dar asistencia a los moderadores de Grupos de Ayuda en Internet es importante para asegurar su uso de forma segura. Métodos de clasificación de textos que analizan el lenguaje utilizado en estos forums es una de las posibles soluciones. Esta investigación trata de utilizar tecnologías del procesamiento del lenguaje natural y el aprendizaje automático para construir un sistema de clasificación de triaje usando datos del forum de salud mental Reachout.com. Al comparar con el estado de la cuestión, nuestra propuesta alcanza el mejor rendimiento para la clase crisis (52%), siendo ésta la de mayor importancia

The role of online social networks in the wellbeing of highly skilled migrants: a case-study of an online forum for Russian-speaking migrants in the UK

This study aims to investigate the role of online social networks in highly skilled migrants’ wellbeing. The research focused on Russian-speaking migrants in the UK. It was designed around a case study of a Russian-speaking online forum for migrants in the UK. The literature on migration, wellbeing, integration, social networks and social media were researched to establish a conceptual framework and position the study within a larger field of research. A mixed-methods approach was used, employing literature review and primary research to collect and analyse data from an online forum scrape and an online survey. The forum was scraped for a period of 12 months and analysed using social networks and statistical analysis in R. An online survey was administered via social media and analysed using statistical analysis in SPSS. Ethical issues regarding online social media data research have been considered and addressed. The findings suggest that there is no direct link between online networks and migrants’ life satisfaction. However, there is evidence that online networks play a role in wellbeing through links with integration and social support. Online networks contribute to integration through providing information support to improve migrants’ knowledge of host communities; and emotional/affirmation support to affirm their socio-cultural identities. The findings revealed that migrants with links to the host country reported higher levels of wellbeing, whereas migrants with stronger links to the home country reported lower levels of wellbeing. These results indicate that migrants’ wellbeing and integration is strongly linked to developing bridging social capital in the host country. Online social networks can be instrumental in this. The study will contribute to knowledge on migration, online networks, social support and the ethics of online research. It will inform academics, practitioners and the wider public on the role of migrants’ social networks in their wellbeing