Abstract
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.
Cite
CITATION STYLE
Mathur, P., Sawhney, R., Chopra, S., Leekha, M., & Ratn Shah, R. (2020). Utilizing temporal psycholinguistic cues for suicidal intent estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12036 LNCS, pp. 265–271). Springer. https://doi.org/10.1007/978-3-030-45442-5_33
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
