COVID-19 Infodemic. Understanding content features in detecting fake news using a machine learning approach
The use of content features, particularly textual and linguistic for fake news detection is under-researched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study investigates a selection of content features such as word bigrams, part of speech distribution etc. to improve fake news detection. We performed a series of experiments on a new dataset gathered during the COVID-19 pandemic and using Decision Tree, K-Nearest Neighbor, Logistic Regression, Support Vector Machine and Random Forest. Random Forest yielded the best results, followed closely by Support Vector Machine, across all setups. In general, both the tex...
| Authors | Vimala Balakrishnan, Lee Zing Hii, Eric Laporte |
|---|---|
| Primary category | cs.CL |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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