Towards automatic fake news classification. Issue 1 (1st February 2019)

• Record Type: Journal Article
• Authors: Ghosh, Souvick; Shah, Chirag
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 55:Issue 1(2018)
• Page Start: 805
• Page End: 807
• Publication Date: 2019-02-01
• Subjects: fake news -- deep neural network -- fake news detection -- machine learning -- classification -- social media; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.2018.14505501125
• Languages: English
• ISSNs: 2373-9231

Abstract

ABSTRACT: The interaction of technology with humans has many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Many research studies have been conducted to tackle the detection and spreading of fake news, which is misinformation that looks genuine. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We propose a generalized method based on Deep Neural Networks to detect if a given claim is fake or genuine. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main submodules. The first submodule uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word‐level features for prediction. The second submodule uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives.