Cross-Platform Violence Detection on Social Media: A Dataset and Analysis
This work addresses the problem of identifying violent content for social media platforms and researchers, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of detecting violent threats across social media platforms by introducing a new cross-platform dataset of 30,000 hand-coded posts and evaluating it with machine learning on an existing YouTube dataset. They achieved high classification accuracy in cross-dataset and merged conditions, demonstrating transferability of violence detection models.
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.