The main goal of this research is to support the Fact-checkers to find relevant facts and arguments related to the COVID-19 information.

1. requirements.txt

Open your Linux terminal inside the APIs folder.

1. To find the transformer-based text similarity : uvicorn Transformers:app --reload

2. To find the vectorizer-based text similarity : uvicorn Vectorizer:app --reload

3. Example text:

4. Example Output:

*** add image for the example input and output ****

We utilize the Covid-on-the-Web Dataset (https://github.com/Wimmics/CovidOnTheWeb), especially the CORD-19 Argumentative Knowledge Graph (CORD19-AKG) for COVID-19 related facts and arguments.

To extract argumentative components (claims and evidences) and PICO elements, They used the Argumentative Clinical Trial Analysis platform (ACTA) [2].

Argumentative components and PICO elements were extracted from the articles' abstracts.

*** Need to update ***

• Transformer based COVID-19 facts similarity
• TF-IDF vectorizer and Count vectorizer based COVID-19 facts similarity

This work is financially supported in part by the Countering Creative Information Manipulation with Explainable AI [CIMPLE] (https://www.chistera.eu/projects/cimple) project.

[1] Franck Michel, Fabien Gandon, Valentin Ah-Kane, Anna Bobasheva, Elena Cabrio, Olivier Corby, Raphaël Gazzotti, Alain Giboin, Santiago Marro, Tobias Mayer, Mathieu Simon, Serena Villata, Marco Winckler. Covid-on-the-Web: Knowledge Graph and Services to Advance COVID-19 Research. International Semantic Web Conference (ISWC), Nov 2020, Athens, Greece. PDF