Journal article Open Access

Machine Learning Based Detection of Deceptive Tweets on Covid-19

Amisha Sinha; Mohnish Raval; S Sindhu

Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)

Social media plays a vital role in connecting people around world and developing relationships. Social Media has a huge potential audience and the circulation of any information does impact a huge population. With the surge of Covid-19, we can see a lot offake news and tweets circulating about remedies, medicine, and general information related to pandemics. In this paper, we set out machine learning-based detection of deceptive information around Covid-19. With this paper, we have described our project which could detect whether a tweet is fake or real automatically. The labeled dataset is used in the process which is extracted from the arXiv repository. Dataset has tweets, upon which various methods are applied for cleaning, training, and testing. Preprocessing, Classification, tokenization, and stemming/removal of stop words are performed to extract the most relevant information from the dataset and to achieve better accuracy in comparison with the existing system. For classification, we have used two classification techniques- Tf-Idf and Bags of words. To achieve better accuracy, we have used two other methodology-SVM and Random Forest and have achieved an F1-score of 0.94 using SVM.

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