Özet:
The increase in the amount of false information is a widespread issue in the era of
digital technology, with extensive implications for society such as fostering
skepticism, manipulation, and undermining democratic conversations. In order to
tackle this pressing matter, this study utilizes the use of NLP strategies and employs
an array of ML methods to construct efficient models for detecting false news. The
objective is to identify the most effective approach for addressing this issue.
The chosen methods, namely logistic regression, naive Bayes, support vector
machines, random forests, and k-nearest neighbors, are rigorously evaluated to
ascertain their efficacy in identifying counterfeit news. The key findings indicate that
the ML techniques are highly effective in differentiating between genuine and fake
news stories, achieving accuracy ratings between 85% and 95%. The performance
parameters, including precision, recall, and F1-score, are thoroughly examined to offer
a full comparison.
This research enhances the developing field of false news identification by showcasing
the suitability of Natural Language Processing (NLP) and a variety of ML techniques.
In addition to academic domains, the study aims to explore practical applications,
providing a detailed comprehension of the pros and cons of each algorithm. The study
continues by providing insights into potential future paths, highlighting the necessity
for flexible strategies in identifying false information, considering the ever-changing
nature of disinformation.
In summary, our research significantly contributes to the battle against false
information by creating efficient detection models and providing useful insights into
the capabilities and constraints of various ML methods
