FAKE NEWS DETECTION IN URDU LANGUAGE FROM SOCIAL MEDIA USING DEEP LEARNING MODEL

Abstract

This thesis presents a framework for fake news detection from the social media platform.The data is extracted from Twitter because it is one of the largest social platforms that promote news rapidly. Mostly the news promoters in the field of media use Twitter for spreading of news. There is a lot of work that has been done by different researches in the field of fake news detection in which they used different approaches, however, most of such approaches are validated on English. We are presenting a method that uses a hybrid approach for the detection of fake news in Urdu. The first method in our framework is content based approach in which embedding feature is used that converts text data into vector. Word2vec technique is applied for this purpose. The second approach that has been used for this research is Social Base approach, in which we used engagement feature as our data set comprises of extra information such as re-tweet count, likes, Favourite Count. The third approach is Network Based Approach, in which network feature is used considering Friends count and follower count. The presented work uses Deep Learning Models for better performance. Two types of deep learning models such as CNN and LSTM are implemented. The results from LSTM Model are more accurate as 72% accuracy obtained from this model when Content Base approach used in this model. When we use hybrid approach in LSTM the accuracy increased to 73%. In CNN model when we used Content Base Approach the 67% accuracy obtained, but in hybrid approach we got 64% accuracy. The LSTM results are higher than CNN in fake news detection.