Detecting Inactive Cyberwarriors from Online Forums
Not on the map. Why?
Abstract (arXiv)
The proliferation of misinformation has emerged as a new form of warfare in the information age. This type of warfare involves cyberwarriors, who deliberately propagate messages aimed at defaming opponents or fostering unity among allies. In this study, we investigate the level of activity exhibited by cyberwarriors within a large online forum, and remarkably, we discover that only a minute fraction of cyberwarriors are active users. Surprisingly, despite their expected role of actively disseminating misinformation, cyberwarriors remain predominantly silent during peacetime and only spring into action when necessary. Moreover, we analyze the challenges associated with identifying cyberwarriors and provide evidence that detecting inactive cyberwarriors is considerably more challenging than identifying their active counterparts. Finally, we discuss potential methodologies to more effectively identify cyberwarriors during their inactive phases, offering insights into better capturing their presence and actions. The experimental code is released for reproducibility: urlhttps://github.com/Ryaninthegame/Detect-Inactive-Spammers-on-PTT.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 29 references that are in the map. First posted on arXiv 28 Aug 2023.
Builds on
6 of 29 references.
References · 6
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2019Graph Neural Networks for Social Recommendationcited by 714
- 2017Topology Adaptive Graph Convolutional Networkscited by 113
- 2022RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Searchcited by 17
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.