@inproceedings{sawhney-etal-2021-phase,
title = "{PHASE}: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media",
author = "Sawhney, Ramit and
Joshi, Harshit and
Flek, Lucie and
Shah, Rajiv Ratn",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.205",
doi = "10.18653/v1/2021.eacl-main.205",
pages = "2415--2428",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media
%A Sawhney, Ramit
%A Joshi, Harshit
%A Flek, Lucie
%A Shah, Rajiv Ratn
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F sawhney-etal-2021-phase
%X 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.
%R 10.18653/v1/2021.eacl-main.205
%U https://aclanthology.org/2021.eacl-main.205
%U https://doi.org/10.18653/v1/2021.eacl-main.205
%P 2415-2428
Markdown (Informal)
[PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media](https://aclanthology.org/2021.eacl-main.205) (Sawhney et al., EACL 2021)
ACL