NIT: Searching for rumors in social network through neighborhood information transmission

Rumor detection has been a hot issue in current online public opinion governance. Most of the recent rumor detection methods tend to be supervised learning, while research about unsupervised detection methods is relatively rare, which focuses on deep learning models. For both interpretability and high performance, an unsupervised rumor detection method based on neighborhood information transmission (NIT) is proposed. Firstly, the initial importance regarded as the initial information of the object is evaluated from the perspective of local and global heterogeneous distance. Then the neighborhood information graph is constructed based on the neighborhood relationship on mixed data. On the neighborhood graph, NIT performs information transmission between neighbors and constantly updates the importance score by paying attention to the correlation between objects. Finally, the rumor object can be sought out by importance score ranking. In addition, to enhance the rumor identification effect, a propagation-based enhancement strategy is proposed to improve the performance of the unsupervised rumor detection algorithm by the propagation index of each object. Experiments were conducted on two UCI datasets and Weibo real-world rumor dataset, and the results proved the effectiveness of the NIT algorithm and the feasibility of the enhancement strategy.