With regard to the issue of access to health-related content circulating online, especially by laypersons, this article aims at illustrating the effectiveness of using features of a different nature in combination with machine learning and deep learning classifiers for the task of health misinformation detection. To this end, and for evaluation purposes, publicly available datasets consisting of health-related information in the form of both Web pages and social media content are considered.

Di Sotto, S., & Viviani, M. (2022). Assessing health misinformation in online content. In Proceedings of the ACM Symposium on Applied Computing (pp.717-720). Association for Computing Machinery [10.1145/3477314.3507238].

Assessing health misinformation in online content

Viviani M.
2022

Abstract

With regard to the issue of access to health-related content circulating online, especially by laypersons, this article aims at illustrating the effectiveness of using features of a different nature in combination with machine learning and deep learning classifiers for the task of health misinformation detection. To this end, and for evaluation purposes, publicly available datasets consisting of health-related information in the form of both Web pages and social media content are considered.
No
slide + paper
Scientifica
consumer health; deep learning; health literacy; health misinformation; machine learning; social web;
English
37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 - 25 April 2022 through 29 April 2022
978-145038713-2
Di Sotto, S., & Viviani, M. (2022). Assessing health misinformation in online content. In Proceedings of the ACM Symposium on Applied Computing (pp.717-720). Association for Computing Machinery [10.1145/3477314.3507238].
Di Sotto, S; Viviani, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/386184
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