Fighting an Infodemic: COVID-19 Fake News Dataset
By Parth Patwa, Shivam Sharma, Srinivas Pykl, V. Guptha, G. Kumari, Md. Shad Akhtar, Asif Ekbal, A. Das, Tanmoy Chakraborty. CONSTRAINT@AAAI, 2020
368 citations recorded by the source corpus.
Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression , Gradient Boost , and Support Vector Machine (SVM). We obtain the best performance of 93.46\% F1-score with SVM. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection
Subfields: Epidemiology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/fccf4cc76a6e996742bf1399c8a3af6bf385a126
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Citations in the corpus
- Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022, background): “In research by [82], they describe and release a fake news detection dataset containing 10,700 fake and real news related to COVID-19.”
- Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023, methodology): “We found all currently published algorithms were from the AAAI 2021 shared task challenge – “COVID-19 Fake News Detection in English” using the CONSTRAINT dataset [83].”
- Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English (2021, methodology): “1 Data Source We use the officially provided dataset [9] and external dataset we collect from the Internet as our training data.”