Abstract:
Nowadays, social media platforms have become central to disseminating information, shaping
public opinion, and influencing behavior in various areas. Sharing information and media with
a large number of users is most effectively done through this method. However, it also has a
major downside of sharing misleading and fake information, intentionally or unintentionally,
that can harm others. The reason behind this is the absence of a proper way to identify whether
a news story is authentic or misleading. Therefore, spreading misleading information on these
platforms poses a significant challenge and requires a comprehensive understanding of
identifying and controlling it.
Researchers have attempted to identify fake news through various methods. The author’s
approach involves distinguishing between disinformation and misinformation, with
disinformation being more dangerous as it targets real individuals or entities and can potentially
harm their reputations. The proposed solution to this problem involves using logistic regression
and random forest classifiers to accurately determine whether the content of news is
disinformation or misinformation.
At the initial implementation phase, the accuracy of reliability detection in news was found to
be 89%. However, identifying disinformation and misinformation has proved to have low
accuracy due to the variability of the data set. Therefore, further development of the project
requires proper optimization of these models.
