Abstract
Misinformation prospers on online social networks and impacts society in various aspects. They spread rapidly online; therefore, it is crucial to keep track of any information that could potentially be false as early as possible. Many efforts have focused on detecting and eliminating misinformation using machine learning methods. Our proposed framework aims to leverage the strength of human roles engaging with a machine learning tool, providing a monitoring tool to identify the risk of misinformation on Twitter at an early stage. Specifically, this work is interested in a visualisation tool that prioritises popular Twitter topics and analyses the responses of the higher-risk topics through stance classification. Besides tackling the challenging task of stance classification, this work also aims to explore features within the information from Twitter that could provide further aspects of a response to a topic using sentiment analysis. The main objective is to provide an engaging tool for people who are also working towards the issue of online misinformation, i.e., fact-checkers in identifying and managing the risk of a specific topic at an early stage by taking appropriate actions towards it before the consequences worsen.
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The author is supported by the Engineering and Physical Sciences Research Council [grant number EP/V00784X/1].
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Liew, X.Y. (2023). Monitoring Online Discussions and Responses to Support the Identification of Misinformation. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_51
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DOI: https://doi.org/10.1007/978-3-031-28241-6_51
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