This paper is in the following e-collection/theme issue:

Information Seeking, Information Needs (426) Quality/Credibility of eHealth Information and Trust Issues (492) Machine Learning (1400) Natural Language Processing (650) Information Quality in Digital Media (318) Information Retrieval (271) Information Demand (31) Infodemic Management (44)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26065, first published .
Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Authors of this article:

Aleksandra Nabożny1 Author Orcid Image ;   Bartłomiej Balcerzak2 Author Orcid Image ;   Adam Wierzbicki2 Author Orcid Image ;   Mikołaj Morzy3 Author Orcid Image ;   Małgorzata Chlabicz4 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Nabożny A, Balcerzak B, Morzy M, Wierzbicki A, Savov P, Warpechowski K. Improving medical experts’ efficiency of misinformation detection: an exploratory study. World Wide Web 2023;26(2):773 View