Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media (2023)

Abstract: As the impact of social media gradually escalates, people are more likely to be exposed to indistinguishable fake news. Therefore, numerous studies have attempted to detect rumors on social media by analyzing the textual content and propagation paths. However, fewer works on rumor detection tasks consider the malicious attacks commonly observed at response level. Moreover, existing detection models have poor interpretability. To address these issues, we propose a novel framework named **D**efend-**A**nd-**S**ummarize (DAS) based on the concept that responses sharing similar opinions should exhibit similar features. Specifically, DAS filters out the attack responses and summarizes the responsive posts of each conversation thread in both extractive and abstractive ways to provide multi-perspective prediction explanations. Furthermore, we enhance our detection architecture with the transformer and Bi-directional Graph Convolutional Networks. Experiments on three public datasets, *i.e.*, RumorEval2019, Twitter15, and Twitter16, demonstrate that our DAS defends against malicious attacks and provides prediction explanations, and the proposed detection model achieves state-of-the-art.

## introduction due to the low cost and easy access to information, social media has become a popular platform for information dissemination. however, it increases the spread of misinformation as well @xcite . the spread of rumors could cause panic and further damage public mental health or lead to severe economic loss @xcite . therefore, debunking unverified rumors on the internet has become an indispensable issue @xcite . numerous researchers have been dedicated to detecting rumors automatically. early works mostly rely on the textual content of each post and the corresponding responses @xcite . in addition, several studies show the importance of considering the propagation path between the responses within the same conversation thread @xcite @xcite ). to better extract information from the propagation, graph convolutional networks (gcns) are widely adopted and achieve remarkable performance for the rumor detection problem @xcite @xcite . for instance, @xcite pioneer the integration of transformer and gcn to better detect the rumors. however, two main challenges remain unaddressed. firstly, the detectors could be sensitive to critical responses toward an event, i.e., responses that significantly impact the detectors. fig. 1 demonstrates that roughly 18.9% of posts in the twitter15 dataset contain critical responses. the influence of such responses may be formulated as an attacking manner by adversaries. as prior studies mainly focus on determining the veracity of a given claim by the source post and responsive posts, the potential threat from the attack responses could lead to vulnerability in detection models @xcite . hence, some works have developed gan-style frameworks to build more robust detectors @xcite @xcite . however, retraining the entire model to defend against attacks could be time-consuming and limited to recognizing only adversarial examples, disregarding various forms of real-world malicious attacks. on the other hand, recent works mainly leverage neural networks for predictions, making the interpretability behind those predictions unattainable due to the black-box property of such models @xcite . to better interpret the detectors' behavior, some works utilize attention mechanism to highlight the important parts of the inputs @xcite , which demonstrates the feasibility of probing the detection models by identifying influential responses in a conversation thread. however, such an approach lacks comprehensive and human-understandable clues, which brings the second challenge of providing organized explanations that cover different viewpoints. we posit that the consideration of multiple perspectives within the discussion threads serves to enhance readers' cognizance of a multitude of viewpoints, thereby discouraging the uncritical acceptance of an excessively confident verdict. in this paper, we propose a novel framework called defend-and-summarize (das) to reduce detector vulnerability and provide prediction explanations. the design of das follows the idea that responses with similar stances or viewpoints should lie closer in the embedding space. this concept is substantiated by prior studies @xcite , which showcase that various standpoints of political opinions on twitter can be well partitioned into distinct clusters based on the embedding representations. this characteristic could enhance summarization with more structured and comprehensive information. as such, das includes a response extractor and a response abstractor. the extractor filters and organizes the responses, while the abstractor condenses the information according to the organized responses. to improve robustness, we preemptively mitigate malicious attacks with the response extractor. by exploiting the idea of anomaly detection, we filter the responses by considering the genuine ones as normal data and the attack ones as anomalies. in addition to removing the potentially risky responses, we further organize the remaining responses to find representative ones. we apply clustering to automatically explore the underlying aspects of data to interpret model predictions from different perspectives. representative responses are then extracted from the medoid of each cluster. afterward, the response abstractor aims to produce more comprehensive and human-understandable explanations by summarizing the responses from each cluster. we exploit the pre-trained abstractive summarizers and transfer the models to the rumor detection corpora via self-supervised learning. in particular, the abstractor is finetuned by cluster-summary pairs where the medoid of each response cluster serves as a pseudo summary. combining extractive and abstractive summaries from das provides detectors and users with more reliable and comprehensive information on different viewpoints. moreover, we introduce a bi-directional transformer-graph network (bitgn) to improve rumor detection by integrating the robust textual representations of the transformer and the structural information of bidirectional gcn (bigcn). the contributions of this paper are summarized as follows: ## related work model vulnerability adversarial attack has been used to simulate the impact of critical responses @xcite @xcite . for example, @xcite develop gan-style frameworks to generate conflicting utterances that complicate the original conversation threads and force the discriminator to capture more robust text features. moreover, le et al. (2020) model such influential responses as a novel attack scenario and propose an adversarial comment generator to mislead the detector. to reduce the model vulnerability to such attacks, song et al. (2021) perform adversarial training on the detector with adversarial responses. however, this approach requires retraining the entire model. in contrast, our work presents a novel framework that resists response attacks without retraining the model. interpretability one class of studies typically explains model predictions by analyzing the attention given to different parts of inputs, which is usually accomplished by visualizing the word importance scores @xcite or using heatmap @xcite . for instance, @xcite visualize the attention weights between source tweets and the propagation structures to highlight evidential words and suspicious users in predicting fake news. similarly, khoo et al. (2020) provide token-level and post-level explanations by examining the attention weights of transformer layers. apart from attention-based approaches, pugoy and kao (2021) explain recommender system predictions by producing extractive summaries from user and item reviews, which capture crucial sentences for models and provide more comprehensive information than word-level and review-level explanations. consequently, in this paper, we attempt to provide realistic explanations for rumor detection models by summarizing different opinions in each conversation thread.