Fake News Detector in Social Media Using AI

K. E. Eswari, Harshini S., Kaaviyadharshini M., Kavinnagul S.

2025

Abstract

Natural language processing (NLP), blockchain technology, and reinforcement learning (RL) are all employed within this new method for detecting fake news. It begins by gathering a large set of news articles and accompanying data, which is then cleaned and tokenized through the use of natural language processing (NLP) methods. A key feature set that is extracted, including word counts and readability scores, is used to train an RL agent. The agent is provided with the skills to distinguish authentic and false news through a reward- punishment framework. In the post-training process, the RL agent makes decisions based on these features in determining whether novel articles are valid or fake ones. While blockchain technology's operation is described, more details must be provided. This approach works to prevent false and misleading content dissemination in digitalnews.The explosive expansion of online social networks within the last few years has promoted the spread offake news for political and commercial purposes. The consumers of these sites are easily influenced by fake news using deceitful language, which impacts offline societygreatly. Detection offake news quicklyis one of the key objectives in enhancing the accuracy of information on online social networks. The algorithms, methods, and rules for detection of false news stories, authors, and entities in web-scale social networks are investigated in this work, in addition to measuring their efficacy. The sheer magnitude of web-scale data complicates detection, estimation, and correction offtake news, particularly considering the increasing importance of correct information, particularly social media. In this paper, we introduce a method of identifying false information andtalkabout howtoapplyit on Facebook, one of the most used social networking websites.Rather than relying on typical news websites that typically involve source verification, most smartphone users prefer to read news on social networking sites. Authenticating news and articles posted on social networking platforms such as Facebook, Twitter, WhatsApp, and other microblogs is challenging, though. If rumors are used as factual news, it is not good for society. Emphasis on verifying and sharing authentic, verified news is the need of the time in countries like India where false news may spread easily. In this study, a machine learning andnatural language processing-based model and methodology for detecting fake news are introduced. The technique employs a Support Vector Machine (SVM) tocompilenews reports and evaluate their validity. Compared to other models, the performance of this proposed model with 93.6% accuracy demonstrates how efficiently it can identify false news.

Paper Citation

in Harvard Style

Eswari K., S. H., M. K. and S. K. (2025). Fake News Detector in Social Media Using AI. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 788-793. DOI: 10.5220/0013905800004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Eswari and Harshini S. and Kaaviyadharshini M. and Kavinnagul S.},
title={Fake News Detector in Social Media Using AI},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={788-793},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013905800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Fake News Detector in Social Media Using AI
SN - 978-989-758-777-1
AU - Eswari K.
AU - S. H.
AU - M. K.
AU - S. K.
PY - 2025
SP - 788
EP - 793
DO - 10.5220/0013905800004919
PB - SciTePress