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#suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter

Published: 03 November 2019 Publication History

Abstract

Technological advancements have led to the creation of social media platforms like Twitter, where people have started voicing their views over rarely discussed and socially stigmatizing issues. Twitter, is increasingly being used for studying psycho-linguistic phenomenon spanning from expressions of adverse drug reactions, depressions, to suicidality. In this work we focus on identifying suicidal posts from Twitter. Towards this objective we take a multipronged approach and implement different neural network models such assequential models andgraph convolutional networks, that are trained on textual content shared in Twitter, the historical tweeting activity of the users and social network formed between different users posting about suicidality. We train a stacked ensemble of classifiers representing different aspects of suicidal tweeting activity, and achieve state-of-the-art results on a new manually annotated dataset developed by us, that contains textual as well as network information of suicidal tweets. We further investigate into the trained models and perform qualitative analysis showing how historical tweeting activity and rich information embedded in the homophily networks amongst users in Twitter, aids in accurately identifying tweets expressing suicidal intent.

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    cover image ACM Conferences
    CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
    November 2019
    3373 pages
    ISBN:9781450369763
    DOI:10.1145/3357384
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 November 2019

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    Author Tags

    1. health informatics
    2. social media mining
    3. suicidal ideation

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    • (2024)JMS-QA: A Joint Hierarchical Architecture for Mental Health Question AnsweringIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.332929532(352-363)Online publication date: 1-Jan-2024
    • (2024)Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA InterviewIEEE Journal of Translational Engineering in Health and Medicine10.1109/JTEHM.2024.336976412(359-370)Online publication date: 2024
    • (2024)Mental health prediction from social media connectionsNew Review of Hypermedia and Multimedia10.1080/13614568.2024.234622729:3-4(225-244)Online publication date: 29-Apr-2024
    • (2024)Artificial Intelligence-based Suicide Prevention and Prediction: A Systematic Review (2019-2023)Information Fusion10.1016/j.inffus.2024.102673(102673)Online publication date: Sep-2024
    • (2024)Mental disorder and suicidal ideation detection from social media using deep neural networksJournal of Computational Social Science10.1007/s42001-024-00307-17:3(2277-2307)Online publication date: 6-Jul-2024
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    • (2024)A computational model for assisting individuals with suicidal ideation based on context historiesUniversal Access in the Information Society10.1007/s10209-023-00991-223:3(1447-1466)Online publication date: 1-Aug-2024
    • (2024)Deciphering Emotional and Linguistic Patterns in Reddit Suicidal DiscourseSocial, Cultural, and Behavioral Modeling10.1007/978-3-031-72241-7_13(133-143)Online publication date: 14-Sep-2024
    • (2024)Suicide Ideation Prediction Through Deep Learning: An Integration of CNN and Bidirectional LSTM with Word EmbeddingsIntelligent Computing10.1007/978-3-031-62277-9_16(271-283)Online publication date: 13-Jun-2024
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