Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)

Puneet Mathur, Ramit Sawhney, Rajiv Ratn Shah

AAAI 2020 pp. 13873-13874

doi:10.1609/AAAI.V34I10.7209 /aaai/2020/mathur2020aaai-suicide/

Abstract

Social media platforms are increasingly being used for studying psycho-linguistic phenomenon to model expressions of suicidal intent in tweets. Most recent work in suicidal ideation detection doesn't leverage contextual psychological cues. In this work, we hypothesize that the contextual information embedded in the form of historical activities of users and homophily networks formed between like-minded individuals in Twitter can substantially improve existing techniques for automated identification of suicidal tweets. This premise is extensively tested to yield state of the art results as compared to linguistic only models, and the state-of-the-art model.

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Text

Mathur et al. "Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7209

Markdown

[Mathur et al. "Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/mathur2020aaai-suicide/) doi:10.1609/AAAI.V34I10.7209

BibTeX

@inproceedings{mathur2020aaai-suicide,
  title     = {{Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)}},
  author    = {Mathur, Puneet and Sawhney, Ramit and Shah, Rajiv Ratn},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13873-13874},
  doi       = {10.1609/AAAI.V34I10.7209},
  url       = {https://mlanthology.org/aaai/2020/mathur2020aaai-suicide/}
}