Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021)

Abstract: Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.

## introduction every 40 seconds, a person dies by suicide @xcite . despite the success of psychoclinical methods, such as the suicide probability scale @xcite and suicide ideation questionnaire @xcite , the suicide rate in the u.s. has risen by 35% in the last 20 years @xcite . while these methods are professional @xcite , they have limited efficacy and may even impact participants negatively @xcite . their limitations include barriers such as social stigma @xcite , low literacy @xcite , low motivation to seek hel @xcite , and finances @xcite . tragically, 80% of patients do not undergo clinical treatment, and 60% of those who died by suicide denied having suicidal thoughts to practitioners @xcite . studies suggest that suicide can be influenced by social factors @xcite , and is a contagious phenomenon @xcite . if a user is inclined to suicide ideation, a neighbor in the social network also often exhibits suicidal behavior @xcite . further, social media cultivates safe spaces that encourage users to share thoughts with those who appear similar to themselves @xcite @xcite . analyzing such social context along with historical activity, as in figure 1 , can help further ascertain suicidal risk @xcite . according to psychosocial research, there exists an uneven distribution of power and influence on social media @xcite . people exhibiting suicidal ideation form social clusters @xcite and preferentially copy the behavior of popular users, manifesting social learning of suicide-related behavior such as the "copycat suicide" @xcite (figure 1 ). these social networks present a hierarchical structure of ideation propagation, characteristic for scale-free networks @xcite . in a scale-free network, most nodes have very few links, whereas a handful of in-figure 1 : illustration of social influence and context, specifically copycat suicidal ideation, in a scale-free network setting. such social and temporal context can contextualize a user's state for a more accurate suicide risk assessment. we paraphrase all examples in this paper as per the moderate disguise scheme @xcite to protect user privacy @xcite . social networks with scale-free structure are subjects to major distortions when embedded into the euclidean representation space @xcite by ordinary graph neural networks. to overcome this limitation, we propose to model the social relations using graph convolutions over hyperbolic space @xcite . our key contributions are as follows: ## hyper-sos: formulation and design in this section we present the architecture of the hyper-sos framework (hyperbolic graph convolutional network for suicide assessment on social media) shown in figure 3 , designed to identify suicide ideation on social media by explicitly modeling user's social and temporal emotional context. ## broader impact and ethics emphasizing the sensitive nature of this work, we acknowledge the trade-off between privacy and effectiveness @xcite . to avoid coercion and intrusive treatment, we work within the purview of acceptable privacy practices suggested by @xcite and considerations discussed by @xcite . although informed consent of each user was not sought as it may be deemed coercive, we perform automatic de-identification of the dataset using named entity recognition @xcite to reduce the risk of including any identifying data in the raw data. we paraphrase all examples shown in this work to protect user privacy @xcite . all the user data is kept separately on protected servers linked to the raw text and network data only through anonymous ids. we acknowledge that it is almost impossible to prevent abuse of released technology even when developed with good intentions @xcite . hence, we ensure that this analysis is shared only selectively and subject to irb approval @xcite to avoid misuse such as samaritan's radar @xcite . limitations: we acknowledge that suicidality is subjective @xcite , the interpretation of this analysis may vary across individuals on social media @xcite , and we do not know the true intentions of the user behind the post. we further acknowledge that suicide risk exists on a diverse spectrum @xcite , and a binary distinction is a task simplification intended to alert the human in the loop about exceeding a possible intervention threshold. we also recognize that the studied data may be susceptible to demographic, annotator, and medium-specific biases @xcite . ## conclusion motivated by psychological studies, we propose a framework jointly leveraging emotional history from user's past tweets and social information from user's neighborhood in a network to contextualize the interpretation of the latest tweet of a user. to our knowledge, this is the first deep graph neural network study to automatically identify suicide ideation on social media. reflecting upon the scalefree nature of social network relationships, we propose the use of hyperbolic graph convolution networks, and demonstrate that these are more suitable for our twitter task than their euclidean counterparts. inspired by geophysics, we further propose the use of heat mechanism to learn the historic emotional spectrum of a user in a time-sensitive manner. when analyzing the contributions of its individual components to assess suicidal intent, we demonstrate the beneficial impact of both the social and personal context representations.