Detecting People Interested in Non-Suicidal Self-Injury on Social Media

We propose a supervised learning approach to detect people interested in Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification problem, and build classifiers based upon features extracted from people self-declared interests. Experimental evaluation on a real-world dataset, th...

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Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Yang, Zaihan, Zinoviev, Dmitry
Format: Article
Language:English
Subjects:
Feature extraction
Online Access:Get full text
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Summary:We propose a supervised learning approach to detect people interested in Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification problem, and build classifiers based upon features extracted from people self-declared interests. Experimental evaluation on a real-world dataset, the LiveJournal social blogging networking platform, demonstrates the effectiveness of our proposed model.
ISSN:2331-8422
DOI:10.48550/arxiv.2207.07014