Abstract
Social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Identifying and mitigating fake news also presents unique challenges. To tackle these challenges, many existing research efforts exploit various features of the data, including network features. In essence, a news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.
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Acknowledgments
This material is based upon work supported by, or in part by, the ONR grant N00014-16-1-2257, N000141310835, and N00014-17-1-2605. We greatly appreciate Carole Bernard’s careful copy-editing and proofreading.
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Shu, K., Bernard, H.R., Liu, H. (2019). Studying Fake News via Network Analysis: Detection and Mitigation. In: Agarwal, N., Dokoohaki, N., Tokdemir, S. (eds) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-94105-9_3
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