• GORLA HAMSIKA
• LAKSHMI SWARNA PRIYA
• BAGARI NANDINI

Most of the people now-a-days prefer to read the news via social media over internet. Many websites are publishing the news and provide the source of authentication.The question is how toauthenticatethenewsand articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. Itis harmfulfor thesociety tobelieveon the rumours and pretend to be a news. The need of an houristostoptherumours and focus on the correct, authenticatednewsarticles. Aim of this project is to develop two models for detecting fake news by using Machine Learning algorithms (usingSVM) and Deep Learning algorithms (usingLSTM) respectively. Withthe help of Machine learningand Deep Learning, it is tried to aggregate the news and later determine whether thenews isreal or fake using Support VectorMachine and Long Short-Term model. For the model, the dataset is cleaned and the data is pre-processed. Thenon the pre-processed data, feature extraction techniques are used and the model is trained using both the algorithms separately to get two different models using SVM algorithm and LSTM algorithm respectively. Confusion Matrix, Classification reports and accuracies of both the models are calculated and compared to find the best model for Fake News Detection. SVM Classifier gave99.29 %accuracy and LSTM classifier gave 99.54% accuracy. Both the algorithms gave good accuracies but LSTM gave better results compared to SVM in classifying the news articles. The major contribution of this project is to find the better fit algorithm and techniques for Fake News Detection between SVM and LSTM algorithms by comparing their accuracies. .

SVM, LSTM, Machine Learning, Deep Learning

"Comparative study of Fake News Detection between Machine Learning & Deep Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.a22-a28, June-2022, Available :http://www.jetir.org/papers/JETIR2206003.pdf

"Comparative study of Fake News Detection between Machine Learning & Deep Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppa22-a28, June-2022, Available at : http://www.jetir.org/papers/JETIR2206003.pdf