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.
Type
Publication
Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II 42
Shivang Chopra
CS Ph.D. Student
Ph.D. candidate in Computer Science at Georgia Institute of Technology