Fake News Detection

• Author(s): Nagavelli Yogender Nath; Gattu Ramya; R. Prasanth Reddy; K. Mani Raju
• Paper ID: 1712453
• Page: 780-787
• Published Date: 31-07-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 1 July-2024

Abstract

In today?s world, social media platforms are important means of information diffusion, and people trust them without questioning their authenticity. Social media is a major factor in propagating fake news. Thus, to mitigate the consequences of fake news, we create an NLP model to differentiate fake and real news. Here machine learning algorithms has been used for enhancing fake news detection performance with NLP. Models trained using max entropy classifier, where news content is scanned for sentences that could indicate the news is fake based on existing NLP libraries. TF-IDF weighting is used to score certain pieces of text, so that detection is fast on any updates or incoming messages due to its fast computation time and high recall rate (low mistake rate). Here the proposed project?s purpose is to detect fake and misleading news from social media networks.

Keywords

Fake news, Recurrent Neural Network, TF-IDF, NLP.

Citations

IRE Journals:
Nagavelli Yogender Nath, Gattu Ramya, R. Prasanth Reddy, K. Mani Raju "Fake News Detection" Iconic Research And Engineering Journals Volume 8 Issue 1 2024 Page 780-787 https://doi.org/10.64388/IREV8I1-1712453

IEEE:
Nagavelli Yogender Nath, Gattu Ramya, R. Prasanth Reddy, K. Mani Raju "Fake News Detection" Iconic Research And Engineering Journals, 8(1) https://doi.org/10.64388/IREV8I1-1712453