论文标题
归因于事实检查URL建议的多关系注意网络
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
论文作者
论文摘要
为了打击虚假新闻，研究人员主要致力于检测假新闻，并建立和维护事实检查网站（例如，snopes.com和politifact.com）。但是，假新闻传播通过社交媒体网站得到了极大的推广，这些事实检查网站尚未得到充分利用。为了克服这些问题并补充了针对虚假新闻的现有方法，在本文中，我们提出了一个基于深入学习的事实检查URL推荐系统，以减轻虚假新闻在Twitter和Facebook等社交媒体网站中的影响。特别是，我们提出的框架包括一个多个关联的专注模块和一个异质的图形注意力网络，以学习用户 - 净值对，用户用户对和url-url-url对之间的复杂/语义关系。在现实世界中的数据集上进行的广泛实验表明，我们提出的框架的表现优于八个最先进的推荐模型，至少提高了3〜5.3％。
To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). 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.
