Abstract
The ongoing COVID-19 pandemic is bringing an “infodemic” on social media. Simultaneously, the huge volume of misinformation (such as rumors, fake news, spam posts, etc.) is scattered in every corner of people’s social life. Traditional misinformation detection methods typically focus on centralized offline processing, that is, they process pandemic-related social data by deploying the model in a single local server. However, such processing incurs extremely long latency when detecting social misinformation related to COVID-19, and cannot handle large-scale social misinformation. In this paper, we propose COS2, a distributed and scalable system that supports large-scale COVID-19-related social misinformation detection. COS2 is able to automatically deploy many groups to distribute deep learning models in scalable cloud servers, process large-scale COVID-19-related social data in various groups, and efficiently detect COVID-19-related tweets with low latency.
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Xu, H., Curci, M., Ek, S., Liu, P., Li, Z., Xu, S. (2022). COS2: Detecting Large-Scale COVID-19 Misinformation in Social Networks. In: Ye, K., Zhang, LJ. (eds) Cloud Computing – CLOUD 2021. CLOUD 2021. Lecture Notes in Computer Science(), vol 12989. Springer, Cham. https://doi.org/10.1007/978-3-030-96326-2_7
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DOI: https://doi.org/10.1007/978-3-030-96326-2_7
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