text: 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.
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  @cite_29: History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societ al and ethical implications of this research.
  @cite_32: Background: Suicide is a leading cause of death in the United States. Social media such as Twitter is an emerging surveillance tool that may assist researchers in tracking suicide risk factors in real time. Aims: To identify suicide-related risk factors through Twitter conversations by matching on geographic suicide rates from vital statistics data. Method: At-risk tweets were filtered from the Twitter stream using keywords and phrases created from suicide risk factors. Tweets were grouped by state and departures from expectation were calculated. The values for suicide tweeters were compared against national data of actual suicide rates from the Centers for Disease Control and Prevention. Results: A total of 1,659,274 tweets were analyzed over a 3-month period with 37,717 identified as at-risk for suicide. Midwestern and western states had a higher proportion of suicide-related tweeters than expected, while the reverse was true for southern and eastern states. A strong correlation was observed between sta...
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  @cite_17: Online mental health forums provide users with an anonymous support platform that is facilitated by moderators responsible for finding and addressing critical posts, especially those related to self-harm. Given the seriousness of these posts, it is important that the moderators are able to locate these critical posts quickly in order to respond with timely support. We approached the task of automatically triaging forum posts as a multiclass classification problem. Our model uses a supervised classifier with various features including lexical, psycholinguistic, and topic modeling features. On a dataset of mental forum posts from ReachOut.com1, our approach identified critical cases with a F-score of over 80 , showing the effectiveness of the model. Among 16 participating teams and 60 total runs, our best run achieved macro-average F1-score of 41 for the critical categories (The best score among all the runs was 42 ).
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  @cite_23: This paper describes the three phases of the Durkheim Project. For this project we developed a clinician's dashboard that displays output of models predicting suicide risk of veterans and active duty military personnel. During phase one, we built the clinician’s dashboard and completed a Veterans Affairs (VA) predictive risk medical records study, based on an analysis of the narrative, or free text, portions of VA medical records, In phase two, we will predict suicide risk based on opt-in social media postings by patients using social media websites, e.g., Facebook. We describe the software infrastructure that we have completed for this phase two system. During phase three we will provide a three layer intervention strategy. We discuss our methodology for the three phases, including IRBapproved protocols for the first two phases and a soon-to-be approved IRB protocol for phase three.
  @cite_2: This paper describes the Data61-CSIRO text classification systems submitted as part of the CLPsych 2016 shared task. The aim of the shared task is to develop automated systems that can help mental health professionals with the process of triaging posts with ideations of depression and or self-harm. We structured our participation in the CLPsych 2016 shared task in order to focus on different facets of modelling online forum discussions: (i) vector space representations; (ii) different text granularities; and (iii) fine- versus coarse-grained labels indicating concern. We achieved an F1score of 0.42 using an ensemble classification approach that predicts fine-grained labels of concern. This was the best score obtained by any submitted system in the 2016 shared task.
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  @cite_11: In the 24 hours prior to her death by suicide, a young girl made 145 posts on Twitter. In the posts, she outlines her history of incestuous sexual abuse, rape, and risky sexual behavior. These posts were analyzed using the Linguistic Inquiry and Word Count (LIWC). Trends were found indicating an increase in positive emotions over the 24 hours and a change in focus from the self to others. This first study of posts on Twitter by a person about to commit suicide provides hypotheses for future studies of this medium for communication.
summary: In addition to general studies addressing mental health, related work has also specifically studied suicide and self-harm through social media @cite_32 @cite_23 @cite_11 @cite_29 @cite_34 . Recently, CLPsych 2016 @cite_14 investigated approaches for detecting the self-harm risk of mental health forum posts @cite_40 . Most related work in this area uses variations of linear classifiers with some sort of feature engineering; successful methods have employed: a combination of sparse (bag-of-words) and dense (doc2vec) representation of the target forum posts @cite_2 , a stack of feature-rich Random Forest and linear Support Vector Machine (SVM) @cite_6 , an RBF SVM classifier utilizing similar sets of features @cite_16 , and various contextual and psycholinguistic features @cite_17 @cite_25 . In contrast to the above works, our model does not use any general or domain specific feature engineering; it learns appropriate representations of documents by considering only their textual content.