Integrating higher-order relations for enhanced twitter bot detection
Bibliographic Data
| ID | 16858828 |
|---|---|
| Authors | Sebastian Reiche (0000-0002-2555-999X, Hasso Plattner Institute, corresponding author), Sarel Cohen (0000-0003-4578-1245, Academic College of Tel Aviv-Yafo), Kirill Simonov (0000-0001-9436-7310, Hasso Plattner Institute), Tobias Friedrich (0000-0001-7324-4100, Hasso Plattner Institute) |
| Year | 2024 |
| Volume | 14 |
| Issue | 1 |
| Publication date | 2024-10-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-024-01372-0 |
| OpenAlex | W4403613049 |
| Language | EN |
| References cited | 28 |
Bot detection is a critical task in preserving the integrity of social networks and mitigating online disinformation. Despite advances in graph-based methods for detecting bots, these models often rely on follow relations, assuming that users sharing similar characteristics are more likely to connect. However, these methods often fail to address complex behavioral patterns indicative of coordinated bot activities, allowing bot developers to avoid detection more easily. We investigate the potential of integrating behavioral and higher-order relations, focusing on Retweet, Co-Retweet (where two users retweet the same tweet) and Co-Hashtag (where two users frequently use the same hashtag), and compare these against conventional follower and following relations. We further explore the process of relation creation, particularly highlighting the Co-Hashtag relation’s robustness against data collection flaws and its ability to mitigate shortcomings in the collection of datasets. Our experiments not only contribute to the ongoing efforts to address challenges in bot detection, but also open up new avenues for exploring how complex network patterns can be employed for social network analysis
Computer security · Data mining · Data science · Disinformation · Machine learning · Social media · World Wide Web · Complex Network Analysis Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection
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| Highly cited | No |
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