2 Matching Annotations
  1. Oct 2021
    1. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.

      It was said in the conclusion that the TriFN can have a good fake news detection performance in the early stage of information dissemination because of the interactions in social media. User credibility was also mentioned since low credibility users tend to spread fake news.

      This means that users play a big part in detecting and reducing fake news in social media. Let's be responsible to only share credible news articles and report the misleading ones.

  2. Dec 2020