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Detecting Fake News on Social Media by CSIBERT

Published: 08 October 2022 Publication History

Abstract

Social media has become a significant news source as the modern world develops. Compared with traditional news media such as newspapers and television, people can consume and share news much faster on social media platforms such as Twitter, Facebook, and Weibo. These platforms are not regulated, which leads to massive amounts of fake news produced online and causes severe negative impacts on politics, economics, and social well-being. Thus, detecting fake news on social media is extremely important but technically challenging. This paper proposes a hybrid fake news detection model called CSIBERT, extracting text features of news events utilizing a Bidirectional Encoder Representations from Transformers (BERT) pre-trained model and introducing other social context features via the Capture, Score, and Integrate (CSI) framework. Our proposed model outperforms existing models with an accuracy of 97.1%. In addition, the CSIBERT model receives decent performance even with a small number of labeled samples on the Weibo fake news detection tasks, demonstrating its ability to solve the label shortage problem in fake news detection challenges.

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  • (2024)Transformer-based models for combating rumours on microblogging platforms: a reviewArtificial Intelligence Review10.1007/s10462-024-10837-957:8Online publication date: 20-Jul-2024

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cover image ACM Other conferences
ICDLT '22: Proceedings of the 2022 6th International Conference on Deep Learning Technologies
July 2022
155 pages
ISBN:9781450396936
DOI:10.1145/3556677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 October 2022

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Author Tags

  1. BERT
  2. CSI
  3. Deep learning
  4. Fake news
  5. Natural Language Processing
  6. Social media

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  • (2024)Transformer-based models for combating rumours on microblogging platforms: a reviewArtificial Intelligence Review10.1007/s10462-024-10837-957:8Online publication date: 20-Jul-2024

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