Abstract
The propagation of fake information on social networks is now a societal problem. Design of mitigation and intervention strategies for fake information has received less attention in social media research, mainly due to the challenge of designing relevant user behavior models. In this paper we lay the groundwork towards such models and present a novel, data-driven approach for user behavior analysis and characterization. We leverage unsupervised learning to define user behavioral categories over key behavior dimensions. We then relate these categories to content-based, user-based, and network-based features that can be extracted in near-real time and identify the most discriminative features. Finally, we build predictive models via supervised learning that leverage these features to determine a user’s behavior category. Rigorous evaluation indicates that the constructed models can be valuable in predicting user behavior from recent activity. These models can be employed to rapidly identify users for intervention in mitigation strategies, crisis communication, and brand management.
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Rajabi, Z., Shehu, A., Purohit, H. (2019). User Behavior Modelling for Fake Information Mitigation on Social Web. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_24
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DOI: https://doi.org/10.1007/978-3-030-21741-9_24
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