Abstract: To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., this http URL and this http URL). However, fake news dissemination has been greatly promoted via social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms eight state-of-the-art recommendation models, achieving at least 3~5.3% improvement.

Introduction: While social media sites provide users with the revolutionized communication medium by bringing the communication efficiency to a new level, they can be easily misused for widely spreading misinformation and fake news. Fake news and misinformation have been a long-standing issue for various purposes such as political propaganda BIBREF0 and financial propaganda BIBREF1. To fight against fake news, traditional publishers employed human editors to manually and carefully check the content of news articles to maintain their reputation. However, social media provided a new way to spread news, which lead to broader information sources and expanded audience (i.e., anyone can be a media and create news). In particular, users share news articles with their own opinion or read articles shared by their friends from whatever the source of news is with mostly blind trust BIBREF2 or with their own ideologies BIBREF3, BIBREF4. Although social media posts usually have a very short life cycle, the unprecedented amount of fake news may lead to a catastrophic impact on both individuals and society. Besides from misleading users with false information BIBREF4, widely propagated fake news could even cause trust crisis of entire news ecosystem BIBREF5, even further affecting both the cyberspace and physical space. In literature, researchers focused on four topics regarding fake news: characterization (i.e., types of fake news), motivation, circulation, and countermeasures BIBREF6, BIBREF7. A large body of work has been done on fake news identification BIBREF5, BIBREF8, BIBREF9, BIBREF10 by exploiting multiple content-related and social-related components. However, we notice that the fake news still has been widely spread even after early detection BIBREF11. Therefore, we propose to study a complementary approach to mitigate the spread and impact of fake news. Recently, community and journalists started building and maintaining fact-checking websites (e.g., Snopes.com). Social media users called fact-checkers also started using these fact-checking pages as factual evidences to debunk fake news by replying to fake news posters. Figure FIGREF1 demonstrates a real-world example of a fact-checker's fact-checking behavior on Twitter by debunking another user's false claim with a Snopes page URL as an evidence to support the factual correction. In BIBREF12, researchers found that these fact-checkers actively debunked fake news mostly within one day, and their replies were exposed to hundreds of millions users. To motivate these fact-checkers further quickly engage with fake news posters and intelligently consume increased volume of fact-checking articles, in this paper we propose a novel personalized fact-checking URL recommender system. According to BIBREF13, co-occurrence matrix within the given context provides information of semantic similarity between two objects. Therefore, in our proposed deep-learning based recommender system, we employ two extended matrices: user-user co-occurrence matrix, and URL-URL co-occurrence matrix to facilitate our recommendation. In addition, users tend to form relationships with like-minded people BIBREF14. Therefore, we incorporate each user's social context to capture the semantic relation to enhance the recommendation performance. Our main contributions are summarized as follows: We propose a new framework for personalized fact-checking URL recommendation, which relies on multi-relational context neighbors. We propose two attention mechanisms which allow for learning deep semantic representation of both a target user and a target URL at different granularity. Experimental results show that our proposed model outperforms eight state-of-the-art baselines, covering various types of recommendation approaches. Ablation study confirm the effectiveness of each component in our proposed framework.

Question: What was their perplexity score?
Answer: Perplexity score 142.84 on dev and 138.91 on test