Abstract
High speed information dissemination in the online environment is useful, but could also lead to fast spread deceptive content. Among the areas affected by online misinformation is the medical field, hence the need of tools able to help the users filter inaccurate information. This paper is focused on the detection of false medical information distributed online. The proposed model is a sequential neural network with three layers which employs word embeddings. When tested on a dataset concerned with vaccine hesitancy, its accuracy reached 83%. We show that effective detection of potentially deceptive articles on vaccination can be achieved by using this neural network.
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This work was supported by the Computer Science Department of the Technical University of Cluj-Napoca, Romania.
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Slavescu, R.R., Pop, FI., Slavescu, K.C. (2022). Towards Detecting Fake Medical Content on the Web with Machine Learning. In: Vlad, S., Roman, N.M. (eds) 7th International Conference on Advancements of Medicine and Health Care through Technology. MEDITECH 2020. IFMBE Proceedings, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-93564-1_29
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DOI: https://doi.org/10.1007/978-3-030-93564-1_29
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