Abstract
Fighting medical disinformation in the era of the global pandemic is an increasingly important problem. As of today, automatic systems for assessing the credibility of medical information do not offer sufficient precision to be used without human supervision, and the involvement of medical expert annotators is required. Thus, our work aims to optimize the utilization of medical experts’ time. We use the dataset of sentences taken from online lay medical articles. We propose a general framework for filtering medical statements that do not need to be manually verified by medical experts. The results show the gain in fact-checking performance of expert annotators on capturing misinformation by the factor of 2.2 on average. In other words, our framework allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow.
Citations
-
1
CrossRef
-
0
Web of Science
-
1
Scopus
Authors (4)
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Web Information Systems Engineering – WISE 2021 strony 420 - 434
- Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Nabożny A., Balcerzak B., Morzy M., Wierzbicki A.: Focus on Misinformation: Improving Medical Experts’ Efficiency of Misinformation Detection// Web Information Systems Engineering – WISE 2021/ : , 2021, s.420-434
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-030-91560-5_31
- Verified by:
- Gdańsk University of Technology
seen 113 times
Recommended for you
Improving medical experts’ efficiency of misinformation detection: an exploratory study
- A. Nabożny,
- B. Balcerzak,
- M. Morzy
- + 3 authors
Enriching the Context: Methods of Improving the Non-contextual Assessment of Sentence Credibility
- A. Nabożny,
- B. Balcerzak,
- D. Korzinek
Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
- A. Nabożny,
- B. Balcerzak,
- A. Wierzbicki
- + 2 authors