Abstract
Disinformation in the form of fake news, phoney press releases and hoaxes may be misleading, especially when they are not from their original sources and this fake news can cause significant harm to the people. In this paper, we report several machine learning classifiers on the CLEF2021 dataset for the tasks of news claim and topic classification using n-grams. We achieve an F1 score of 38.92% on news claim classification (task 3a) and an F1 score of 78.96% on topic classification (task 3b). In addition, we augmented the dataset for news claim classification and we observed that insertion of alternative words was not beneficial for the fake news classification task.
| Original language | English |
|---|---|
| Pages (from-to) | 446-454 |
| Number of pages | 9 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2936 |
| State | Published - 2021 |
| Event | 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania Duration: 21 Sep 2021 → 24 Sep 2021 |
Keywords
- Fake news claim classification
- Fake news data augmentation
- Fake news detection
- Fake news topic classification