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Detection and veracity analysis of fake news via scrapping and authenticating the web search
Affiliation:1. Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran;2. Departmernt of Psychology and Educational Sciences, University of Tehran, Tehran, Iran;1. IT Research Institute, Chosun University, Gwangju, South Korea;2. Hankuk University of Foreign Studies, Seoul, South Korea;3. Department of History and Historical Content, Sangmyung University, Seoul, South Korea;4. Jan Evangelista Purkyne University, Moskevska 54, 400 96 Usti nad Labem, Czech Republic;5. Software Convergence Education Institute, Chosun University, Gwangju, South Korea;1. Beijing Key Laboratory of Intelligence Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, PR China;2. School of Artificial Intelligence, Henan University, Zhengzhou 450046, PR China;3. North China Institute of Science and Technology, Hebei 065201, PR China
Abstract:Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach.
Keywords:Detection of fake news  Fake news analysis  Fake news in images  Social media  Veracity
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