Propaganda Identification on Twitter Platform During COVID-19 Pandemic Using LSTM

Online social media has been evolved as a universal platform for sharing information. Termination being shared on these platforms can be dubious or filthy. Propaganda is one of the systematic methods by which behavior of user can be manipulated. In this work, various machine learning methods are used for detecting such types of information on online social media. Data is collected d from Twitter using its API with the help of various ambiguous hashtags. The results showed that proposed Long Short Term Memory (LSTM) based propaganda identification showed better results than other machine learning techniques. An accuracy of 77.15% is achieved using the proposed approach. In the future BERT model can be used for achieving better Accuracy.