65 citations · 108 across the 17 of their papers we have counts for
6 papers · 1 filter
SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection
Hanning Lu, Yingguang Yang, Jinwei Su +8
LLM-driven social bots can generate fluent, human-like text, reducing the discriminative advantage of content-based detection alone. However, coordinated campaigns still leave rela…
Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes
Qi Wu, Yingguang Yang, hao liu +4
Social bot detection is crucial for mitigating misinformation, online manipulation, and coordinated inauthentic behavior. While existing neural network-based detectors perform well…
SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection
Yingguang Yang, Qi Wu, Buyun He +4
Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains beni…
BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers
Buyun He, Yingguang Yang, Qi Wu +6
Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions.…
BotSCL: Heterophily-aware Social Bot Detection with Supervised Contrastive Learning
Qi Wu, Yingguang Yang, Buyun He +3
Detecting ever-evolving social bots has become increasingly challenging. Advanced bots tend to interact more with humans as a camouflage to evade detection. While graph-based detec…
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search
Yingguang Yang, Renyu Yang, Yangyang Li +5
Social bots are referred to as the automated accounts on social networks that make attempts to behave like human. While Graph Neural Networks (GNNs) has been massively applied to t…