Fake News Detector Using Machine Learning Algorithms - Diaa SALAMA ABD ELMINAAM; Yomna M.I. HASSAN; Radwa MOSTAFA; AbdElrahman TOLBA; Mariam KHALED; John GERGES (Communications of International Proceedings, 2020)
With the Covid-19 (Corona Virus) spread worldwide, people are using this propaganda, and the citizens' desperate need to know the news about this mysterious virus by spreading fake news. Moreover, since Social Media has become a significant news source, it is profoundly needed to detect this fake news. This research aims to develop a web-based model using machine learning algorithms to detect fake news.  The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis. We suggest that Natural Language Processing (NLP) would not be enough alone to make context analysis. Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes, and tweet-length. We also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. To achieve the best accuracy, we combined two of the best algorithms used:  SVM (widely accepted as a baseline classifier, especially with binary classification problems) and Naive Base for comparison.
Keywords: Fake News; SVM; NLP.