- Title
- Towards Fake News Detection on Social Media
- Creator
- Alghamdi, Jawaher; Lin, Yuqing; Luo, Suhuai
- Relation
- 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (Nassau, Bahamas 12-14 December, 2022) p. 148-153
- Publisher Link
- http://dx.doi.org/10.1109/ICMLA55696.2022.00028
- Publisher
- Institute of Electrical and Electronic Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2022
- Description
- The dissemination of fake news on the Internet has resulted in worrying negative implications for individuals and society. This paper begins by discussing the definitions of fake news and the related terms that have often co-occurred with the term fake news. Then, we summarised several social science theories characterising fake news spreading. Next, we discussed the state-of-the-art techniques for detecting fake news using news content and user context information. Finally, we conducted a case study that demonstrates that the interplay between news content and context-based features helps uncover useful patterns to discriminate fake from real news. Our study suggests that content and context-based features are necessary for better performance of fake news detection.
- Subject
- fake news; social media; misinformation; context information
- Identifier
- http://hdl.handle.net/1959.13/1490462
- Identifier
- uon:52917
- Identifier
- ISBN:9781665462839
- Language
- eng
- Reviewed
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