  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.
