论文标题
使用通过点过程提取的时间功能的假新闻检测
Fake News Detection using Temporal Features Extracted via Point Process
论文作者
论文摘要
许多人使用社交网络服务（SNS）轻松访问各种新闻。有很多方法可以获取和分享``虚假新闻''，这是带有虚假信息的新闻。为了解决虚假新闻，已经进行了几项研究，用于使用SNS提取功能来检测假新闻。在这项研究中，我们尝试使用SNS帖子产生的时间功能，通过使用点过程算法来识别Real News的假新闻。假新闻检测中的时间功能比现有功能具有鲁棒性的优势，因为它对假新闻传播器的依赖最少。此外，我们提出了一种新型的基于多模式注意的方法，其中包括语言和用户功能以及时间功能，用于检测SNS帖子的假新闻。从三个公共数据集获得的结果表明，与现有方法相比，所提出的模型的性能更好，并证明了时间特征在伪造新闻检测中的有效性。
Many people use social networking services (SNSs) to easily access various news. There are numerous ways to obtain and share ``fake news,'' which are news carrying false information. To address fake news, several studies have been conducted for detecting fake news by using SNS-extracted features. In this study, we attempt to use temporal features generated from SNS posts by using a point process algorithm to identify fake news from real news. Temporal features in fake news detection have the advantage of robustness over existing features because it has minimal dependence on fake news propagators. Further, we propose a novel multi-modal attention-based method, which includes linguistic and user features alongside temporal features, for detecting fake news from SNS posts. Results obtained from three public datasets indicate that the proposed model achieves better performance compared to existing methods and demonstrate the effectiveness of temporal features for fake news detection.
