@inproceedings{8fc30dd6f9b14325bb9fd5ce6a1439d4,
title = "Detecting fake news about Covid-19 using classifiers from Scikit-learn",
abstract = "Weak control of reliability of news circulating on the Internet has provoked a large number of fake news and the need to develop detectors for such news. Unlike researchers that use deep learning networks with raw data, in this paper we consider popular machine learning algorithms from the Scikit-learn library and latent semantic indexes related to text to achieve good results in condition of a small dataset. The experiments use the full (10700) and small (1000) samples from the Constraint-2021 corpus, which includes news about Covid-19. With the best algorithms we achieved a detection quality of 78\% and 71\% micro F1-score, for the full and small datasets respectively. We believe that such a simple technology may be useful for users in their confrontation with fake news, because this approach does not demand a big dataset and can be implemented fast.",
keywords = "Classification, Constraint-2021, Fake news, Scikit-learn",
author = "Elena Shushkevich and Mikhail Alexandrov and John Cardiff",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 16th IEEE International Conference on Computer Science and Information Technologies, CSIT 2021 ; Conference date: 22-09-2021 Through 25-09-2021",
year = "2021",
doi = "10.1109/CSIT52700.2021.9648767",
language = "English",
series = "International Scientific and Technical Conference on Computer Sciences and Information Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "406--409",
booktitle = "IEEE 16th International Conference on Computer Science and Information Technologies, CSIT 2021 - Proceedings",
address = "United States",
}