Open-Topic False Information Detection on Social Networks with Contrastive Adversarial Learning (2022)

Abstract: Current works about false information detection based on conversation graphs on social networks focus primarily on two research streams from the standpoint of topic distribution: in-topic and cross-topic techniques, which assume that the data topic distribution is identical or cross, respectively. This signifies that all test data topics are seen or unseen by the model.However, these assumptions are too harsh for actual social networks that contain both seen and unseen topics simultaneously, hence restricting their practical application.In light of this, this paper develops a novel open-topic scenario that is better suited to actual social networks. In this open-topic scenario, we empirically find that the existing models suffer from impairment in the detection performance for seen or unseen topic data, resulting in poor overall model performance. To address this issue, we propose a novel Contrastive Adversarial Learning Network, CALN, that employs an unsupervised topic clustering method to capture topic-specific features to enhance the model’s performance for seen topics and an unsupervised adversarial learning method to align data representation distributions to enhance the model’s generalisation to unseen topics.Experiments on two benchmark datasets and a variety of graph neural networks demonstrate the effectiveness of our approach.

## introduction the convenience and openness of social networks allow people to quickly engage in discussions on a wide range of topics (e.g. the covid-19 epidemic and the war in ukraine), and at the same time they also cause people to suffer from false information, the massive spread of which can affect the political and social order of the real world @xcite . therefore, the detection of false information on social networks is increasingly attracting the attention of the research community @xcite @xcite @xcite . currently, there are two main types of detection models from the perspective of topic distribution: in-topic models and cross-topic models @xcite . (1) in-topic models focus on scenarios where topics of the training and test data are same @xcite , that is, the topics in the test set are seen. (2) cross-topic models focus on scenarios where topics of the training and test data are different @xcite , which means the topics in the test data are unseen. however, we argue that the real social networks contain both seen and unseen topics simultaneously, and we refer to this phenomenon as the open-topic scenario. as shown in figure 1 (a), there is a significant difference among the three topic distributions. to the best of our knowledge, there is no work on false information detection in open-topic scenarios. the intuitive idea is that we could resort to the existing models to handle detection tasks in this scenario. to implement the above conception, we conduct a preliminary validation experiment. specifically, we train an in-topic model and a crosstopic model with the same training data, respectively. then, we leverage an open-topic dataset, mixed seen and unseen topics data, as test dataset to evaluate the performance of the above two models (more details can be found in appendix a. ). the experimental results are shown in figure 1(b) . from the results, we find that the accuracy of the intopic model on unseen topics is significantly lower than that of the cross-topic model. although the cross-topic model improves the accuracy on unseen topics, the accuracy on seen topics is lower than that of the in-topic model. we argue that the reasons for the above phenomenon are as follows. (1) the performance of the in-topic model benefits from prior knowledge of the data @xcite , also known as topic-specific features, such as specific topic words. this prior knowledge cannot be transferred to new topics due to differences between topic features, resulting in poor generalisation of in-topic models to unseen topics. (2) the cross-topic model mainly learns topic-invariant features, such as writing style, from the topics of the train data and transfers these features to the test data @xcite . this learning mechanism causes the cross-topic model to discard prior topic knowledge of the data, reducing the performance of the cross-topic model for seen topics. apparently, the deficiencies of existing methods in open topic scenarios can be alleviated if both topic-specific and topic-invariant features can be preserved. to retain these two types of features, we face two severe challenges. (1) how to learn topic-specific features? some works adopt user features @xcite or pre-trained topic models @xcite to obtain topic-specific features, which are sub-optimal since they fail to address the diversity of topics on social networks. (2) how to learn topic-invariant features? while existing work has achieved good results using adversarial learning @xcite , it relies on the topic labels , which is challenging for information on social networks because topics are constantly emerging. to tackle the above problem and challenges, we propose a novel contrastive adversarial learning network (caln) to obtain and fuse both topicspecific features and topic-invariant features. concretely, we use the drop edge technique @xcite to generate two augmented graphs from an original conversation graph and leverage a graph neural network (gnn) to obtain their graphlevel representations. since different topic conversation graphs have different topic-specific words and topic-specific propagation patterns @xcite , we design an unsupervised topic feature (tf) learner based on contrastive learning to obtain topic-specific knowledge. this tf learner can learn the intrinsic invariance of the data by maximizing the mutual information between the augmented graphs, ultimately achieving topic clustering. then, we resort to classical adversarial learning @xcite to design an unsupervised representation alignment (ra) learner to obtain topic-invariant features, which is achieved by reversing the gradient signal of contrastive learning and aligning the representation distribution of the data. finally, we fuse the obtained topic-invariant features, topicspecific features, and graph-level representations to predict the authenticity of the conversation graph of false information. our contributions are summarized as follows: (1) we study a new issue of false information detection in open-topic scenarios and discover the shortcomings of existing methods in such scenarios. (2) we propose a contrastive adversarial learning network to obtain and fuse topic-specific and topic-invariant feature learning to improve the false information detection in open-topic scenarios. (3) we demonstrate the effectiveness of our method through comparison, ablation and visualization experiments on two real datasets and various gnns. ## conclusion this paper proposes a novel open-topic scenario containing the seen and unseen topic simultaneously for false information detection to complement existing work. we explore the shortcomings of existing models in the open-topic scenario and propose a contrastive adversarial learning network caln, containing a topic feature learner and a representation alignment learner. the topic feature learner is an unsupervised topic-based clustering method to learn topic-specific features, improving the model's performance for seen topics. the representation alignment learner is an unsupervised adversarial learning method to learn topic-invariant features, enhancing the model's generalize for unseen topics. experiments on various gnn encoders and two real datasets demonstrate the effectiveness of our model.