A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media (2020)

Abstract: Social media’s ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention. Suicide ideation is often linked to a history of mental depression. The emotional spectrum of a user’s historical activity on social media can be indicative of their mental state over time. In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context. We propose STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media. STATENet outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment. We discuss the empirical, qualitative, practical, and ethical aspects of STATENet for suicide ideation detection.

## introduction globally, close to 800,000 people die by suicide each year, and 20 times more people attempt suicide. suicide is the second leading cause of death in the 15 to 29 year age group @xcite with a rising suicide rate of 35% in the us since 1999 @xcite . extending clinical and psychological care to people showing suicidal ideation relies heavily on identifying those at risk. tragically, 80% of patients do not undergo psychiatric treatment, and about 60% of those who died of suicide denied having suicidal thoughts to mental health practitioners @xcite . recent studies @xcite also show that people exhibiting suicidal ideation make frequent use of social media, e.g., twitter, to share their 1 https://github.com/midas-research/ statenet_time_aware_suicide_assessment josh is so cute hiiii belated merry christmas and advanced happy new year buddy my friends tell me its difficult to trust me since i keep joking all the time i do not want to be alive, i just want to die today. please kill me already. no suicidal or depressive tendencies concerning, clear suicidal intent figure 1 : we study a user whose latest tweet is not indicative of suicidal intent. without seeing the user's recent historic tweet, which shows self-harm tendencies, it is difficult to accurately assess suicidal risk. however, analyzing a user's tweeting history sequentially without factoring in time irregularities between tweets may lead to an inaccurate representation of a user's mental state. time-aware modeling of the temporal dependency between historic tweets reduces the impact of tweets from 3 years ago, providing a more realistic risk assessment. all examples in this paper have been paraphrased for user privacy @xcite . while recent advances in computational social science @xcite have made progress in assessing suicidal risk on social media, analyzing the linguistic traits of tweets is often not sufficient for accurate suicidal intent detection. additional user-level contexts such as tweeting history can be instrumental in identifying a build-up of negative emotions that are often linked to suicide ideation @xcite . such a build-up can occur weeks, months, or even years before the onset of suicidal ideation @xcite and suicidal activity can also be influenced by past ideation or suicide attempts @xcite . analyzing the user history and emotion spectrum, as shown in figure 1 can provide crucial context to estimate suicidal risk in a tweet authored by that user. such an emotional historic context (ehc) of a user over time can be characteristic of their mental health @xcite . modeling temporal user context, either as a bagof-tweets @xcite , or sequentially @xcite helps in identifying suicidal intent. however, in figure 1 , we show that the impact of varying time intervals between tweets is crucial for an accurate assessment. it is critical to model the large gap between the user's recent tweets that are collectively indicative of suicidal intent and those three years apart. such uneven temporal tweeting irregularities (tti) ranging from seconds to years @xcite between successive tweets influence the assessment of a user's tweet differently. sequential models such as long short term memory (lstms) networks assume that posting intervals are uniform, hindering the learning ability of a user's emotion spectrum over varying time intervals. contributions: taking into account a user's emotional historic context and temporal tweeting irregularities, we propose statenet: suicidality assessment time-aware temporal network, a neural framework that evaluates the presence of suicidal intent on social media (sec. 3.1). building on transfer learning's success in natural language processing, statenet uses a dual transformerbased architecture to learn the linguistic and emotional cues in tweets. statenet jointly learns from the language of the tweet (sec. 3.2) to be assessed, and the historic plutchik-based @xcite emotional spectrum of a user in a time-sensitive manner (sec. 3.3). through a series of experiments (sec. 4) on real-world data (sec. 4.1), we show that statenet significantly outperforms competitive methods (sec. 5), with the f1 score of 80%. we demonstrate practical applicability through a qualitative analysis (sec. 5.4), and discuss the ethical implications of this study (sec. 6). at a minimum, we establish validity for timeaware emotional temporal context for identifying suicide ideation on social media. we focus on the intersection of nlp and suicidal risk assessment by taking a step towards improving risk assessment in a non-intrusive manner. our work could be considered as a preliminary screening tool that optimistically forms a component in a larger in-frastructure involving psychologists, health care providers, and social media enterprises. 2 in practice, statenet would flag tweets as "at-risk" for suicidality as part of a human-in-the-loop system to support decisions about potential intervention. ## discussion ethical considerations: the preponderance of the work presented in our discussion presents heightened ethical challenges. as explored in coppersmith et al. (2018) , we address the trade-off between privacy and effectiveness. while data is essential in making models like statenet effective, we must work within the purview of acceptable privacy practices to avoid coercion and intrusive treatment. to that end, we utilize publicly available twitter data in a purely observational @xcite , and non-intrusive manner. although informed consent of each user was not sought as it may be deemed coercive, automated de-identification of the dataset was performed to reduce the risk of including any identifying data in the raw data. all tweets shown as examples in figure 1 and section 5.4 have been paraphrased as per the moderate disguise scheme suggested in @xcite to protect the privacy of individuals @xcite . the annotation of user data has been kept separately from raw user data on protected servers linked only through anonymous ids @xcite . assessments made by statenet are sensitive and should be shared selectively to avoid misuse, such as samaritan's radar @xcite . our work does not make any diagnostic claims related to suicide. we study the social media posts in a purely observational capacity @xcite and do not intervene with the user experience in any way. limitations: we acknowledge that studying suicidality is subjective in nature @xcite and that the interpretation of the analysis presented may vary across individuals. due to the situatedness of language, the studied data may be susceptible to demographic, annotator, and mediumspecific biases @xcite . we recognize that suicide risk exists on a diverse spectrum, and the simplification of binary labels could lead to artificial notions of risk @xcite . practical implications: through statenet, we suggest a neural architecture for preliminary screening of at-risk users on social media to aid the prioritization of clinical resources. our work observes twitter in a non-intrusive manner and does not intervene with the user experience in any way. statenet should form part of a distributed human-in-the-loop (de andrade et al., 2018) system for finer interpretation of risk. focusing on statenet's practical applicability, we work with tweet level annotations rather than the more subjective and difficult to scale user-level annotations. we emphasize on tweet-level prediction; however, statenet can also be applied for user-level suicide risk assessment given its dual text and historic modeling components. ## conclusion motivated by the rising use of social media for exhibiting suicide ideation as opposed to standard clinical practice @xcite , we present statenet. building on psychological studies on analyzing a user's temporal emotional spectrum, statenet models the time aware emotional context of users through historical tweets for more accurate suicide risk estimation on social media. we plan to explore the impact of varying amounts of historical context for a user in our future work. we show statenet's applicability as a preliminary tool in assessing suicidality in tweets. we present a qualitative analysis for a deeper understanding of statenet. through this work, we aim to form a component in a larger human-in-the-loop infrastructure for analyzing potentially concerning suicide-related social media posts. priority-based suicide risk assessment for ranking tweets for suicidal risk, rather than classifying them forms our future direction. additionally, in the future, we would also want to quantify the impact of varying degrees of granularity of learning emotional features from tweets on statenet's performance.