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

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 timesensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.

Detecting Suicidality with a Contextual Graph Neural Network

“…Among the recent deep learning technologies, graph neural networks (GNNs) have received growing attention in the suicidality assessment task. In particular, GNNs were adopted to extract social information from a user's neighborhood in a social network formed between different users posting about suicidality (Sinha et al, 2019;Sawhney et al, 2021b). Furthermore, Cao et al (2020) built personal knowledge graphs on Sina Weibo to utilize rich social interaction data in suicidal ideation detection.…”

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

“…With the increase in popularity of social media websites (e.g. Facebook, Reddit, Twitter), NLP researchers started using the textual data collected from these platforms for detecting emotions [4,5,6], offensive content [7,8,9], hate speech [10,11], humour [12,13,14], sarcasm [15,16,17], pejorative language [18], inspirational content [19], optimism [20,21] and the manifestations of mental health problems such as depression [22,23], suicide ideation [24,25] and anxiety [26]. Researchers explored the online content from social media even further and began focusing on the multi-modal data [27,28], including internet memes.…”

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling

“…Detecting intentions of self-harm over the course of human-bot conversations. Existing work has looked into detecting suicidal ideation from users on social media, such as in Sawhney et al (2021). However, expressions of intent to self-harm may appear different in a conversational form and in particular, in conversation with a bot.…”

“…Finally, "context" can also be understood as userspecific context over time. For example, Sawhney et al (2021) show that personally contextualizing the buildup of suicide ideation is critical for accurate identification of users at risk.…”