Entity-Aware Dual Co-Attention Network for Fake News Detection (2023)

Abstract: Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.

## introduction the development of the web and social media platforms helps us obtain news quickly, but also provides a gateway for spreading false information. the impact of false information is wide, and the spread speed might be even faster than the actual one @xcite . for example, fake news is proven empirically to influence the 2016 u.s. presidential election @xcite @xcite . given the impact of false information, previous studies paid a lot of effort to detect it from different aspects, including (1) news content only @xcite , (2) the combination of news articles and social media replies @xcite , and (3) additional publisher/user information @xcite @xcite . in this work, we focus on using both news contents and social media replies, and further add external knowledge to enhance the model's ability to capture critical entities. named entities play an important role in document understanding and influence text generation 1 code repository: https://github.com/sinhanyang/ dual-can performances @xcite . inspired by this notion, we design a novel model, named dual co-attention network (dual-can), which takes entities' descriptions into consideration to enhance the background knowledge of the model. the proposed dual-can is modified based on one of the representative fake news detection models, defend @xcite . there are three major improvements in the proposed dual-can: (1) inspired by @xcite , we add entities' descriptions for enhancing the performance. ## related works previous works in fake news detection mainly focused on two aspects: news content based and social context based. @xcite focus on the linguistic characteristics of the news content to detect fake news, and find that fake news often contain specific kinds of words. ma et al. (2016) use recurrent neural networks (rnn) to learn the hidden representations from the contextual information of relevant posts over time. monti et al. (2019) analyze social graph and user profile to predict fake news. shu et al. (2019b) find that user profile features are useful in fake news detection. shu et al. (2019a) and lu and li (2020) use co-attention model to leverage news content and social context. their models not only have better performance but also provide interpretability to their models. several works also use external knowledge to improve model's predictions. wang et al. (2020) and hu et al. ( 2021 ) use entity linking method to capture entity descriptions and leverage them in their models. inspired by these works, we use external knowledge for entities to enhance performance, and use both news content and social media context in the proposed model. ## method figure 1 shows the architecture of the proposed dual-can. this section describes the details of the proposed dual-can model, which is composed of five components. 2 the first one is news content encoder, which employs word-level attention network and sentence-level encoder to generate features for the corresponding news contents. the second is entity description encoder. for each entity in news content, entity description encoder grabs its descriptions from the external knowledge base and creates features to represent them. the third is user engagement encoder, which employs the same method as news content encoder to create features to represent user comments. the fourth is dual co-attention component, which captures the relation between (news content, entity description) and (news content, user engagement) pairs. the last is prediction component, which combines all information from the previous components to make the final predictions. ## interpretability we examine attention weights [a s 1 , a d , a s 2 , a sc ] to find those sentences that the proposed model is focusing on when making predictions. figure 2 illustrates the results. we find that our model pays a certain degree of attention to the first sentence in the entity descriptions of both datasets (figure 2a , 2c ). ## conclusion we propose a dual co-attention network for fake news detection, which improves the previous representative model, defend, by (1) adding entity description as external knowledge and (2) redesigning co-attention architecture for using all input information. our results support the usefulness of the proposed dual-can model. the interpretability based on the attention weight is also discussed.