PHASE : Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (2021)

Abstract: Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the build-up 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 state-of-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.

## introduction every 10.9 minutes, a person dies of suicide @xcite . suicide ranks as the second leading cause of death for 14-35 yearolds @xcite in us. extending appropriate clinical and psychological care to suicidal people relies on identifying those at risk. unfortunately, 80% of patients do not undergo clinical treatment, and about 60% of those who died of suicide denied having any suicidal thoughts to mental health practitioners @xcite . in contrast, people exhibiting suicidal ideation often use social media to express their feelings @xcite @xcite @xcite , with eight out guys work hard it is worth it! btw we also reached the football finals #goforvictory 14 may 2012 1:42 pm i have a good feeling about todays match we will definitely win! world, here we come 15 may 2012 08:28 am lost my dad in an horrific accident, dropped out of college i feel hopeless just want to stab my chest 25 aug 2015 12:58 am free me from all this or i will do something myself 25 aug 2015 01:17 am positive emotions negative emotions negative emotions tweet to be assessed tweet to be assessed 2012 2013 2014 2015 i woke and i dont feel good, nothing to look forward to, i feel a whole lot more tired ~ 3 years 7 june 2015 07:13 am i am joining therapy from tomorrow, lets hope i feel better after a few sessions 13 june 2015 04:41 am ~ 2 months phase 1 phase 2 phase 3 a user's tweeting history with timestamp information variation in emotions expressed suicidal intent figure 1 : we study a user's tweeting history and emotional progression. note that while the user's most recent tweet (blue) shows a subtle indication of suicidal intent, it is not sufficient to ascertain suicide risk. grouping the build-up of negative emotions (red) in the user's historical tweets into phase-like emotional progressions, by utilizing the elapsed time between tweets, can contextualize the user's state and provide a more accurate and interpretable risk assessment. all examples in this paper have been anonymized and paraphrased as per a moderate disguise scheme @xcite to protect user privacy @xcite . natural language processing (nlp) presents an encouraging prospect to complement social science to identify risk markers in user behavior @xcite to aid suicide risk assessment @xcite . however, suicide ideation is complex, and often, individual posts may not be sufficient to assess a user's suicide risk, even for humans @xcite . figure 1 illustrates how features such as historical posts @xcite can add context for analyzing a user's online behavior over time @xcite to better ascertain suicide risk. despite the success of usercentric contextual models @xcite for suicide ideation detection, they have two major limitations. first, recurrent neural networks, particularly lstms, that are natural methods to learn patterns from a sequence of a user's historical tweets @xcite @xcite , assume uniform time gaps between successive tweets. however, tweets can be posted at irregular time intervals @xcite , and varying time gaps can influence the assessment of a user's suicidality progression @xcite , as shown in figure 1 . second, these methods implicitly assume that a user's mental and emotional state progression is smooth in time, with an ever-increasing tendency. however, in reality, studies show that emotional @xcite , and suicidality progression can vary significantly @xcite , and show fluctuating phase like patterns @xcite . analyzing such phase-wise emotion progressions and build-up, as illustrated in figure 1 , can be instrumental in contextualizing suicidal risk, and aiding clinical psychologists through increased interpretability in human-in-the-loop systems. 2 building on these limitations, and motivated by psychological studies @xcite of emotional state progression, we propose phase: phase-aware suicidality identification emotion progression model. with phase, we present the first neural framework to identify suicide ideation on social media ( §3.1) that explicitly models the inherent phase-aware progressions in users' emotional spectrums in a contextual time-aware manner. we present the following key contributions: (i) first, building on the success of large scale pretraining in nlp, we utilize plutchik transformer, a transformer to learn linguistic and plutchik-based @xcite emotional cues from tweets ( §3.2). ## ethical and practical considerations emphasizing the sensitive nature of this work, we acknowledge the trade-off between privacy and effectiveness @xcite , and utilize publicly available twitter data in a purely observational @xcite , and non-intrusive manner. we separate user data from all other data on protected servers linked only through anonymous ids, and we perform automatic de-identification of the dataset using named entity recognition @xcite . all examples shown in this work have been paraphrased to protect user privacy @xcite . we ensure that this analysis is shared selectively and subject to irb approval @xcite to avoid misuse such as samaritan's radar @xcite . we acknowledge that suicidality is subjective @xcite and that the interpretation of this analysis may vary across individuals @xcite . we further acknowledge that suicide risk exists on a diverse spectrum @xcite , rather than at a binary level, and that the studied data may be susceptible to demographic, annotator, and medium-specific biases @xcite . finally, our work does not make any diagnostic claims related to suicide. phase should form part of a distributed human-in-the-loop (de andrade et al., 2018) system for finer interpretation of risk. ## conclusion motivated by the rising exhibition of suicide ideation on social media, we present phase. building on psychological studies analyzing the emotional spectrum and mental health of users, phase adaptively learns emotional phase-aware user representations through historical tweeting activity for suicidal ideation detection. we propose multiple modeling innovations in phase components: contextualized historical emotion representations @xcite , time-sensitive emotion lstm (tse-lstm), and a phase-adaptive convolution (pac). we demonstrate that modeling user phases explicitly increases the predictive power in assessing suicidality in tweets. in a qualitative analysis, we show how phase can aid social media moderators and clinical psychologists in subsequent assessment by displaying its predictions together with the learned emotional phases. through phase, we hope to form a future component in a larger human-in-the-loop infrastructure for suicide prevention.