Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022)

Abstract: 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.

## introduction rumor is defined as unverified information at the time of posting @xcite @xcite . malicious rumors that are spread massively on social media have become a threat to mislead the public and cause social panic. there is a need to debunk rumors in the early stage so as to prevent rumors from the wide spread. sociological research @xcite shows that there exist social bots during the rumor spread, and these bots are particularly active in the early stage of rumors, which will affect the real users through replies and mentions and accelerate the spread @xcite @xcite . current models @xcite @xcite @xcite mainly focus on post content or propagation sequence. these methods model posts as a chronological sequence, and extract the textual feature through gru, lstm, and cnn for rumor detection. other methods @xcite ) account for early rumor detection by modeling user characteristics or credibility with user propagation structures. however, they do not explicitly distinguish genuine users from social bots, so the participation of social bots will lead to the failure of the features captured from both contents and propagation structures. to this end, this paper presents a model named social bot-aware graph neural network (sbag) for early rumor detection. this model consists of two parts: social bot detection (sd) and bot-aware graph rumor detection (bag). the former one is pre-trained based on a large sample of bot users and genuine users, to extract the features to compute the bot possibility for each user. the latter one transfers the sd to the bot-aware graph neural network, which consists of gnnbased user publishing (gup), gat-based user interaction (gui), and textual encoder components. for gup, the bot possibility computed in sd is involved in the aggregation process. for gui, the bot possibility is also integrated into the calculation of the attention weight of user-user. the textual encoder utilizes a convolutional neural network (cnn) to capture textual features. in this way, we take user publishing features, user interaction features, and textual features into consideration for early rumor detection. the codes will be open sourced foot_0 . our main contributions are summarized as follows: ## problem definition assume a set of posts @xmath0, r 2 , ..., r |r| } and a set of users @xmath1, u 2 , ..., u |u| }. each post r corresponds to one publisher and multiple users to repost it. a user publishing graph g @xmath2, e @xmath3, where v p is the set of all publishers and source posts, e p is the set of edges and a edge ## sbag model the framework of the sbag model is shown in fig. 1 . the model consists of two main parts, social bot detection (sd) mentioned in §3.1 and bot-aware graph rumor detection (bag) mentioned in §3.2. we will introduce each module in detail. ## related work conventional rumor detection methods adopted machine learning to classify rumors based on the features of content, user, and propagation pattern, such as decision tree @xcite , support vector machine @xcite @xcite , random forest @xcite , etc. however, these methods involved feature engineering, which is hard to obtain highorder features. recent studies exploited deep learning methods for rumor detection. most of the existing rumor detection methods mainly modeled a source post and its reposts together as a sequence or a graph, using rnn @xcite @xcite , cnn @xcite @xcite , gcn @xcite @xcite and gat @xcite to learn the textual content features. however, these models do not consider the participant of social bots on social media to publish fraudulent content, which may lead to training noise by these fraudulent contents. several studies @xcite @xcite integrated features of user feature or user credibility to learn the propagation patterns of source posts. however, they do not explicitly explore the unique user propagation pattern in the early stage, which limits the early-detection ability of the model. ## conclusion in this work, according to the observation of sociological research, we propose a social bot-aware graph neural network for early rumor detection. first, we pre-train a bot possibility scorer called sd on a large dataset containing bot users and genuine users, then sd is transferred to the bag module. the bag module takes the user's bot possibility information into the calculation of the features from different views, which enables the module to have a priori knowledge of the user in early detection. the experimental results on three public datasets show that sbag effectively captures the early propagation of rumors, and further improves performance of early rumor detection.