SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions

Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.Comment: COLING 201

SMHD : a large-scale resource for exploring online language usage for multiple mental health conditions

Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users’ language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language

Ethical Issues in Text Mining for Mental Health

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2

Deep into that Darkness Peering:A Computational Analysis of the Role of Depression in Edgar Allan Poe's Life and Death

Background: To help shed light on the peculiar circumstances surrounding the death of the famed macabre and mystery writer, poet, editor, and literary critic, we explored the potential role of depression in the life and death of Edgar Allan Poe via his written language. Method: Using computerized language analysis, we analyzed works from Poe’s corpora of personal letters (N = 309), poems (N = 49), and short stories (N = 63), and investigated whether a pattern of linguistic cues consistent with depression and suicidal cognition were discernible throughout the writer’s life, particularly in his final years. Building on past work, language scores were collapsed into a composite depression metric for each text. Data from each work type was subsequently compiled and graphed into a single plot by year, with scores exceeding the 95th percentile (p <.05) considered statistically significant and treated as potential depressive episodes. Results: Significant, consistent patterns of depression were not found and do not support suicide as a cause of death. However, linguistic evidence was found suggesting the presence of several potential depressive episodes over the course of Poe’s life – these episodes were the most pronounced during years of Poe’s greatest success, as well as those following the death of his late wife. Limitations: Given the sampling method, it is not possible to establish direct causality; results should be considered informed but tentative. Conclusion: This investigation demonstrates the utility of language analysis for capturing disruptive/maladaptive emotional responses to life events

Is there an app for that?: Ethical issues in the digital mental health response to COVID-19

As COVID-19 spread, clinicians warned of mental illness epidemics within the coronavirus pandemic. Funding for digital mental health is surging and researchers are calling for widespread adoption to address the mental health sequalae of COVID-19. We consider whether these technologies improve mental health outcomes and whether they exacerbate existing health inequalities laid bare by the pandemic. We argue the evidence for efficacy is weak and the likelihood of increasing inequalities is high. First, we review recent trends in digital mental health. Next, we turn to the clinical literature to show that many technologies proposed as a response to COVID-19 are unlikely to improve outcomes. Then, we argue that even evidence-based technologies run the risk of increasing health disparities. We conclude by suggesting that policymakers should not allocate limited resources to the development of many digital mental health tools and should focus instead on evidence-based solutions to address mental health inequalities