Abstract
This paper presents the design concept of a credibility evaluation tool for medical web-documents and describes the implementation of its part. There have been numerous attempts to create such tool but most of them were strictly subject-specific. In this study, we aim to create a universal classifier for non-credible articles from the medical domain. Unlike most of the latest fact-checking solutions, it evaluates overall the credibility of the document instead of assessing separate claims. We collected a database of articles and sentences evaluated by experts, conducted the study of sentence’s context in the task of credibility assessment, then performed statistical analysis in order to verify and fine-tune the design. The proposed scheme is constructed in such a way that it should be easy to update and has an easily interpretable output for Internet users with no expert knowledge about medicine.
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References
Hassan, N., et al.: Claimbuster: the first-ever end-to-end fact-checking system. Proc. VLDB Endow. 10(12), 1945–1948 (2017)
Lim, W.Y., Lee, M.L., Hsu, W.: iFACT: an interactive framework to assess claims from tweets. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 787–796. ACM (2017)
Popat, K., Mukherjee, S., Strötgen, J., Weikum, G.: Credibility assessment of textual claims on the web. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2173–2178. ACM (2016)
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., Krause, A.: Fake news detection in social networks via crowd signals. In: Companion of the The Web Conference 2018 on The Web Conference 2018, pp. 517–524. International World Wide Web Conferences Steering Committee (2018)
Zhi, S., Sun, Y., Liu, J., Zhang, C., Han, J.: ClaimVerif: a real-time claim verification system using the web and fact databases. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2555–2558. ACM (2017)
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A Quantitative Study Instruction
A Quantitative Study Instruction
Phrases can be evaluated by using a three-degree scale:
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CREDIBLE
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Is credible, does not raise any objections
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Contains information in the medical domain
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NEUTRAL
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Contains worthless information or no information
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Is vague
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Is highly persuasive and remains consistent with the current medical knowledge
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NOT CREDIBLE
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Is not credible
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Contains false or untested information
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Is highly persuasive, but is inconsistent with the current medical knowledge
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Articles can be evaluated by using a two-degree scale:
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CREDIBLE
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Does not raise any serious objections
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Does not call for actions inconsistent with the current medical knowledge
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Contains a fraction of specific medical knowledge
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NOT CREDIBLE
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Does raise serious objections
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Calls for actions inconsistent with the current medical knowledge
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Does not contain any specific medical knowledge
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Contains false of untested information
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Nabożny, A., Balcerzak, B., Wierzbicki, A. (2018). Automatic Credibility Assessment of Popular Medical Articles Available Online. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_20
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DOI: https://doi.org/10.1007/978-3-030-01159-8_20
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Online ISBN: 978-3-030-01159-8
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