Fake news Detection on Social Media

Abstract

Fake news may be intentionally created to promote economic, political and social interests and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles. In contrast, we focus on content-based detection of fake news articles, while assuming that we have a small amount of labels, made available by manual fact-checkers or automated sources. We argue this is a more realistic setting in the presence of massive amounts of content, most of which cannot be easily fact checked. So, we represent collections of news articles as multi-dimensional tensors, leverage tensor decomposition to derive concise article embeddings that capture spatial/contextual information about each news article. Results on realworld data sets show that our method performs on par or better than existing fully supervised models, in that we achieve better detection accuracy using fewer labels. In our proposed system we perform fake news detection using a ensemble learning. Firstly we perform pre-processing on extract data which is extracted from news. After that the classifier predicted news as a fake or real. In particular, our proposed method achieves 92.84 percent accuracy.