Cross-Platform Violence Detection on Social Media: A Dataset and Analysis

• URL: http://arxiv.org/abs/2506.03312v1
• Date: Tue, 03 Jun 2025 18:54:07 GMT
• Title: Cross-Platform Violence Detection on Social Media: A Dataset and Analysis
• Authors: Celia Chen, Scotty Beland, Ingo Burghardt, Jill Byczek, William J. Conway, Eric Cotugno, Sadaf Davre, Megan Fletcher, Rajesh Kumar Gnanasekaran, Kristin Hamilton, Marilyn Harbert, Jordan Heustis, Tanaya Jha, Emily Klein, Hayden Kramer, Alex Leitch, Jessica Perkins, Casi Sherman, Celia Sterrn, Logan Stevens, Rebecca Zarrella, Jennifer Golbeck,
• Abstract summary: We introduce a cross-platform dataset of 30,000 posts hand-coded for violent threats and sub-types of violence.<n>To evaluate the signal present in this dataset, we perform a machine learning analysis with an existing dataset of violent comments from YouTube.
• Score: 1.3007535106461825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Violent threats remain a significant problem across social media platforms. Useful, high-quality data facilitates research into the understanding and detection of malicious content, including violence. In this paper, we introduce a cross-platform dataset of 30,000 posts hand-coded for violent threats and sub-types of violence, including political and sexual violence. To evaluate the signal present in this dataset, we perform a machine learning analysis with an existing dataset of violent comments from YouTube. We find that, despite originating from different platforms and using different coding criteria, we achieve high classification accuracy both by training on one dataset and testing on the other, and in a merged dataset condition. These results have implications for content-classification strategies and for understanding violent content across social media.

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