By Chenyan Jia
In this post, newscoding recommends several fake news or misinformation detection algorithms or datasets (especially misinformation related to COVID-19) that are used by researchers or Internet companies (*the following list is in no particular order of importance).
In this article, Twitter introduces new labels and warning messages that will provide additional context and information on some Tweets containing disputed or misleading information related to COVID-19.
A research paper published in the Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) “Where is your Evidence: Improving Fact-checking by Justification Modeling” extended the LIAR dataset to the LIAR-PLUS dataset. The LIAR dataset was introduced by (Wang, 2017) and consists of 12,836 short statements taken from POLITIFACT and labeled by humans (Alhindi, Petridis, Muresan, 2018).
Metafact is a health fact-checking platform using a community of verified experts. The website has an intuitive interface and contains highly COVID-19 related content.
Neural Covidex applies state-of-the-art neural network models and AI techniques to answer questions using the COVID-19 Open Research Dataset (CORD-19) provided by the Allen Institute for AI (data release of May 26, 2020), which currently contains over 47,000 scholarly articles. In addition, Neural Covidex also supports search on randomized controlled trials related to COVID-19 provided by Trialstreamer.
Facebook works with over 60 fact-checking organizations that review content in more than 50 languages in order to prevent the spread of misinformation during the COVID-19 pandemic.
Researchers from the Center for Artificial Intelligence Research (CAiRE) posted COVID-19 related misinformation test sets newly proposed in their “Misinformation has High Perplexity” paper.
USC Melady Lab identifies unreliable, misleading and clickbait information shared on Twitter regarding COVID-19 from 2020-03-01 – 2020-05-03.
Alhindi, T, Petridis, S, & Muresan, S. (2018). Where is your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), Brussels, Belgium.
Wang, Y. W. (2017). Liar, liar pants on fire: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, BC, Canada.