A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

Abstract

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the buildup of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users' historical context. We propose PHASE, a time-and phase-aware framework that adaptively learns features from a user's historical emotional spectrum on Twitter for preliminary screening of suicidal risk. Building on clinical studies, PHASE learns phase-like progressions in users' historical Plutchik-wheel-based emotions to contextualize suicidal intent. While outperforming stateof-the-art methods, we show the utility of temporal and phase-based emotional contextual cues for suicide ideation detection. We further discuss practical and ethical considerations. 1

FAST: Financial News and Tweet Based Time Aware Network for Stock Trading

“…Time-aware Methods: Recently, time-aware modeling of time series data has shown improvements over conventional sequential models like RNNs and LSTMs on various tasks such as patient subtyping (Baytas et al, 2017), suicide ideation and buildup detection using Twitter history (Sawhney et al, 2020b), disease progression (Gao et al, 2020), and much more. However, modeling the temporal dynamics inherent in social media and online news is complex as it involves noisy and diversely influential data across irregular time intervals.…”

Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods

“…Flow diagram for a systematic reviews which included searches of databases and personality type detection [7]. Many studies show that the linguistic and semantic features of social media users' posts could help indicate and clarify the mental state of the poster [8]. Mapping words often obtain psycholinguistic features words into pre-defined psychological and affective categories.…”

“…Authors have examined the mental state of social media users in many languages.The majority of papers in the field are written in English [11,8,12,13,14,15,16,17,18,19,20,21,22,23]. The Chinese language was the second most used language in the published studies [24,25,26,27].…”

Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

“…The best performing models (Matero et al, 2019;Naderi et al, 2019) at the CLPsych (Zirikly et al, 2019) and CLEF e-Risk (Losada et al, 2019) exemplify the promising yet underexplored direction of user context modeling (Flek, 2020) for suicide ideation detection. Although recent studies (Shing et al, 2020;Sawhney et al, 2020) explore the personal historical context of users, community-based social context has rarely been explored for this task. One of the few attempts includes SNAPBAT-NET (Sinha et al, 2019), a shallow embedding model to extract network structural features.…”

“…We build on previous studies which show that the linguistic styles (De Choudhury et al, 2013 and emotions expressed in suicidal tweets play an important role in assessing suicidal behavior (Sueki, 2015;Spates et al, 2018). Thus, building on this correlation between emotions and suicidal ideation, we finetune BERT on EmoNet (Abdul-Mageed and Ungar, 2017) for capturing fine-grained (Plutchik-based) emotions (Plutchik, 1980;Sawhney et al, 2020).…”