Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

By M. de Choudhury, Emre Kıcıman, Mark Dredze, Glen A. Coppersmith, Mrinal Kumar. 2016

708 citations recorded by the source corpus.

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 societal and ethical implications of this research.

Subfields: Clinical Psychology, Experimental and Cognitive Psychology, Social Psychology, AI Ethics, Fairness & Society, Medical & Health AI

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Citations in the corpus

CausaLM: Causal Model Explanation Through Counterfactual Language Models (2020, methodology): “2019), or using text as confounders (Johansson, Shalit, and Sontag 2016; Choudhury et al. 2016; Roberts, Stewart, and Nielsen 2020).”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, background): “…inﬂuential examples, Coppersmith et al. (2014) infer mental health diagnoses of Twitter users by looking for publicly self-reported diagnoses, and De Choudhury et al. (2016) infer mental health progressions to suicidal ideation by examining when Reddit users shift from mental health subreddits…”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, methodology): “Following most of the previous work in the mental health field [18, 27, 93], we first conduct basic finetuning on a single dataset (the training set).”
Depression and Self-Harm Risk Assessment in Online Forums (2017, background): “In addition to general studies addressing mental health, related work has also speciﬁcally studied suicide and self-harm through social media (Jashinsky et al., 2014; Thompson et al., 2014; Gunn and Lester, 2015; De Choudhury et al., 2016; Coppersmith et al., 2016).”
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression (2017, background): “Recent work [22] suggests that the use of the word “depression” increases one’s chance of posting about suicide in the future.”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “[31] analyzed the posts of Reddit users who wrote about mental health discourse and later shifted to discuss topics about suicidal ideation.”
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2021, background): “Suicide attempters have been reported as suffering from mental disorders, with an investigation on a shift from mental health to suicidal ideation conducted by language and interactional measures (De Choudhury et al., 2016).”
Detecting depression and mental illness on social media: an integrative review (2017, background): “[24] examined posts of a group of reddit users who posted about mental health concerns and then shifted to discuss suicidal ideation in the future.”
Beyond Screen Time: Identity Development in the Digital Age (2020, background): “For instance, public social media text applications (e.g. Facebook, Twitter) could be used to track contamination themes as they emerge naturalistically in young people’s everyday digital discourse (e.g. De Choudhury & De, 2014; De Choudhury et al., 2016).”
Forecasting the onset and course of mental illness with Twitter data (2016, background): “In addition to physical ailments, predictive screening methods have successfully identified markers in social media data for a number of mental health issues, including addiction (6), depression (712), PostTraumatic Stress Disorder (PTSD) (13,14), and suicidal ideation (15).”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Suicidal ideation has also been associated with language used in social media impul-siveness, and loneliness (De Choudhury et al. 2016).”