Social Bot-Aware Graph Neural Network for Early Rumor Detection
Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots’ behavior in the early stage has become the main reason for rumors’ wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90% accuracy.
Code (1)
Tasks
Graph Neural NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter
With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The openness and convenience of social media…
Graph AttentionSRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media
The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates social panic and triggers a crisis of s…
Rumor Detection with a novel graph neural network approach
The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remain…
Graph Neural NetworkRumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict rela…
Graph AttentionRepresentation LearningSemantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual information and the propagation structure of…