Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, Lucie Flek
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.
BibTeX
@inproceedings{sawhney-etal-2021-suicide,
title = "Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning",
author = "Sawhney, Ramit and
Joshi, Harshit and
Shah, Rajiv Ratn and
Flek, Lucie",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.176/",
doi = "10.18653/v1/2021.naacl-main.176",
pages = "2176--2190"
}