TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916296553136128 |
|---|---|
| author | Saeed, Mohammad Hammas Papadamou, Kostantinos Blackburn, Jeremy De Cristofaro, Emiliano Stringhini, Gianluca |
| author_facet | Saeed, Mohammad Hammas Papadamou, Kostantinos Blackburn, Jeremy De Cristofaro, Emiliano Stringhini, Gianluca |
| contents | Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05247 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities Saeed, Mohammad Hammas Papadamou, Kostantinos Blackburn, Jeremy De Cristofaro, Emiliano Stringhini, Gianluca Social and Information Networks Cryptography and Security Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness. |
| title | TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities |
| topic | Social and Information Networks Cryptography and Security |
| url | https://arxiv.org/abs/2308.05247 |