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

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-

Variables Predicting the Severity of a Mass Shooting: the connection to white supremacy

Since mass shootings have become increasingly relevant in today’s society, the subject of what makes a mass shooting deadly has become more and more popular. This project focuses on how selected variables correlate with the severity of a mass shooting, and especially focuses on the impact of white supremacy ideology. Theoretically, a shooter imbued with this ideology will likely be more violent, thus causing a higher victim count (injuries + deaths). The other variables included in the model are: the use of a long gun, the use of multiple guns, the use of semi-automatic guns, mental illness, and shooter suicide. This project seeks to assess the relationships of these variables to the victim count, and the statistical significance of each of these relationships. By drawing from two prominent mass-shooting databases and associated media sources, a dataset was constructed, then analyzed with correlation, regression, and ANOVA. These analyses confirmed all of the hypotheses, with predictor variable correlating positively and significantly to victim count. Most importantly, the findings confirmed the significance of the white supremacy ideology variable in predicting the violence of a mass shooting, and the effect withstood the introduction of a variety of important control variables; in short, shooters with a white supremacy background tend to inflict a higher victim count during a mass shooting. Based on these findings, suggestions for further research include separating active-shooter mass shootings from other types of mass shootings; standardizing the operational definition of a mass shooting; and increasing the number of possible predictor variables in current mass shooting databases

Assessing the Severity of Health States Based on Social Media Posts

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request healthrelated information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients’ social media posts can help health professionals (HP) in prioritizing the user’s post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user’s health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user’s health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user’s health

Assessing the Severity of Health States based on Social Media Posts

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user's health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health

The Effect of Stigma on Intimate Partner Violence Reporting Among Men Who Have Sex with Men

This study examined the relation between stigma and reporting of intimate partner violence (IPV) in a sample of men who have sex with men (MSM). It was hypothesized that enacted stigma would result in lower reporting of IPV and that the type of IPV would moderate the relationship between enacted stigma and reporting. Using an online survey, we measured IPV (physical, psychological, and sexual violence) and stigma (perceived, enacted, and internalized). Participants (N = 46) were asked if they had ever experienced any of those forms of violence, as well as if they had ever reported the violence through an online survey. They were then asked how likely they would be to report the violence if it happened again in the future. Responses were analyzed using logistical regression with moderation to determine if a) enacted stigma was associated with lower reporting of intimate partner violence and if b) type of violence moderated stigma and reporting, such that physical violence would have the strongest relation between stigma and reporting of IPV. Results showed that enacted stigma was associated with more IPV reporting across all types of violence: physical (coefficient: 1.539, p\u3c.0005), sexual (coefficient: .999, p\u3c.05), and psychological (coefficient: 1.203, p\u3c.005). Results of testing the moderating role of violence type on the relationship between enacted stigma and IPV were non-significant for all types of violence. In conclusion, the more enacted stigma that was experienced, the more reporting occurred. In addition, type of violence did not moderate the relation between enacted stigma and reporting of intimate partner violence

Improving moderator responsiveness in online peer support through automated triage

© 2019 Journal of Medical Internet Research. All rights reserved. Background: Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators' attention where it is most needed. Objective: This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior. Methods: A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training. Results: The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not for crisis messages. Response latency was significantly reduced (P<.001), between the same periods, by factors of 80%, 80%, 77%, and 12% for crisis, red, amber, and green messages, respectively. Regression analysis indicated that the triage system made a significant and unique contribution to reducing the time taken to respond to green, amber, and red messages, but not to crisis messages, after accounting for moderator and community activity. Conclusions: The triage system was generally accurate, and moderators were largely in agreement with how messages were prioritized. It had a modest effect on response ratios, primarily because moderators were already more likely to respond to high priority content before the introduction of triage. However, it significantly and substantially reduced the time taken for moderators to respond to prioritized content. Further evaluations are needed to assess the impact of mistakes made by the triage algorithm and how changes to moderator responsiveness impact the well-being of forum members