Social robot detection based on user behavioral representation

Abstract

Social robots aim to facilitate our lives by mimicking our behaviors to deliver information and interact with us. Unfortunately, some social robots are bred for sinister intention, which often spread fake news, even threatening personal and national security. To effectively detect these robots, some existing works utilize basic user attributes for clustering, such as creation time and other information, but ignore the behavioral features of social robots, which are important a priori information for detection, and their absence leads to poor performance. To address this problem, we construct the Social Robot Behavior Detection model, which employs social robots' behavioral habits in disseminating content. Specifically, our method consists of three parts. First, to accurately characterize news, we analyze news features from multiple views, which facilitates representing users' habits of posting news. Second, a novel aggregation strategy is designed for aggregating user features to obtain accurate user representation. Finally, a classifier is used to detect whether user is social robots or not. We conduct analytical experiments on two public datasets, and the results show that our model has better detection performance, which demonstrates that our approach outperforms existing works on social robot detection. Our source code is available at https://github.com/NPURG/SRDB.