Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation

Temporal psycholinguistics can play a crucial role in studying expressions of suicidal intent on social media. Current methods are limited in their approach in leveraging contextual psychological cues from online user communities. This work embarks in a novel direction to explore historical activiti...

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Autores principales: Mathur, Puneet, Sawhney, Ramit, Chopra, Shivang, Leekha, Maitree, Ratn Shah, Rajiv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148016/
http://dx.doi.org/10.1007/978-3-030-45442-5_33
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author Mathur, Puneet
Sawhney, Ramit
Chopra, Shivang
Leekha, Maitree
Ratn Shah, Rajiv
author_facet Mathur, Puneet
Sawhney, Ramit
Chopra, Shivang
Leekha, Maitree
Ratn Shah, Rajiv
author_sort Mathur, Puneet
collection PubMed
description Temporal psycholinguistics can play a crucial role in studying expressions of suicidal intent on social media. Current methods are limited in their approach in leveraging contextual psychological cues from online user communities. This work embarks in a novel direction to explore historical activities of users and homophily networks formed between Twitter users for extracting suicidality trends. Empirical evidence proves the advantages of incorporating historical user profiling and temporal graph convolutional modeling for automated detection of suicidal connotations on Twitter.
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spelling pubmed-71480162020-04-13 Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation Mathur, Puneet Sawhney, Ramit Chopra, Shivang Leekha, Maitree Ratn Shah, Rajiv Advances in Information Retrieval Article Temporal psycholinguistics can play a crucial role in studying expressions of suicidal intent on social media. Current methods are limited in their approach in leveraging contextual psychological cues from online user communities. This work embarks in a novel direction to explore historical activities of users and homophily networks formed between Twitter users for extracting suicidality trends. Empirical evidence proves the advantages of incorporating historical user profiling and temporal graph convolutional modeling for automated detection of suicidal connotations on Twitter. 2020-03-24 /pmc/articles/PMC7148016/ http://dx.doi.org/10.1007/978-3-030-45442-5_33 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Mathur, Puneet
Sawhney, Ramit
Chopra, Shivang
Leekha, Maitree
Ratn Shah, Rajiv
Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title_full Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title_fullStr Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title_full_unstemmed Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title_short Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation
title_sort utilizing temporal psycholinguistic cues for suicidal intent estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148016/
http://dx.doi.org/10.1007/978-3-030-45442-5_33
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