SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media

Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah


Abstract
Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research. While it has been established that these users share a common set of properties, the current state-of-the-art approaches utilize only text-based (stylistic and semantic) cues. We contend that the use of information from networks in the form of condensed social graph embeddings and author profiling using features from historical data can be combined with an existing set of features to improve the performance. To that end, we experiment on a manually annotated dataset of tweets created using a three-phase strategy and propose SNAP-BATNET, a deep learning based model to extract text-based features and a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings that outperform the current state-of-the-art. We conduct a comprehensive quantitative analysis with baselines, both generic and specific, that presents the case for SNAP-BATNET, along with an error analysis that highlights the limitations and challenges faced paving the way to the future of AI-based suicide ideation detection.
Anthology ID:
N19-3019
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
147–156
URL:
https://www.aclweb.org/anthology/N19-3019
DOI:
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