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- W2891453035 abstract "Social media has become a popular means for people to consume news. Meanwhile, it also enables the wide dissemination of fake news, i.e., with intentionally false information, which brings significant negative effects to the society. Thus, fake detection is attracting increasing attention. However, fake detection is a non-trivial task, which requires multi-source information such as content, social context, and dynamic information. First, fake is written to fool people, which makes it difficult to detect fake simply based on contents. In addition to contents, we need to explore social contexts such as user engagements and social behaviors. For example, a credible user's comment that is a fake news is a strong signal for detecting fake news. Second, dynamic information such as how fake and true propagate and how users' opinions toward pieces are very important for extracting useful patterns for (early) fake detection and intervention. Thus, comprehensive datasets which contain content, social context, and dynamic information could facilitate fake propagation, detection, and mitigation; while to the best of our knowledge, existing datasets only contains one or two aspects. Therefore, in this paper, to facilitate fake related researches, we provide a fake data repository FakeNewsNet, which contains two comprehensive datasets that includes content, social context, and dynamic information. We present a comprehensive description of datasets collection, demonstrate an exploratory analysis of this data repository from different perspectives, and discuss the benefits of FakeNewsNet for potential applications on fake study on social media." @default.
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- W2891453035 date "2018-09-05" @default.
- W2891453035 modified "2023-09-27" @default.
- W2891453035 title "FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media" @default.
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