An Improved Model for Detection of Fake News On Social Media Using Deep Learning Algorithms (A Case Study of Twitter Networking Site)
Social media for news consumption is a double-edged sword. On one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Traditional machine learning algorithms such as SVM and Decision Tree were used to automatically classify fake news from genuine news. The challenging part of these algorithms is their inability to extract features automatically and also they handle small datasets. In this research, we intend to use a Deep Learning method to improve the detection fake news. FactChecking tool would be used to detect the source of news on social media while CNN algorithms would be applied to detect bot accounts and fake contents.