• ANIRUDDHA SHRIKRUSHNA MORE
• GAYATRI BHAGWAN GOND
• RUSHIKESH MADHUKAR VARADE
• PUNAM RAMESH BHUTE
• Prof.P. A. KHARAT

The paper introduces WELFake, a novel approach for fake news detection that leverages word embeddings over linguistic features. WELFake combines the power of word embeddings with linguistic features to enhance the accuracy of fake news identification. The proposed methodology demonstrates the importance of linguistic nuances and context in distinguishing between genuine and deceptive information. By integrating word embeddings with linguistic features, WELFake aims to improve the robustness of fake news detection models. The experimental results showcase the effectiveness of WELFake in achieving higher accuracy and reliability compared to traditional methods

Word Embedding, Linguistic Features, Fake News Detection, Natural Language Processing, Machine Learning.

"WELFake: Word Embedding Over Linguistic Features for Fake News Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g460-g463, April-2024, Available :http://www.jetir.org/papers/JETIR2404661.pdf

"WELFake: Word Embedding Over Linguistic Features for Fake News Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg460-g463, April-2024, Available at : http://www.jetir.org/papers/JETIR2404661.pdf