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Fake News Detection Using Multimodal Framework

Savani Shrivastava, Dr. Preeti Rai



The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. One can easily say in today’s world, information aka news to few, is more precious than money itself. The news needs to be in authentic form which is usually found in adulterated version. News, being a form of information can be subjective to the proofs and source for its authenticity. As a human, one can easily identify real news from fake news with the help of one’s innate capability to deduce logic and outlandish source of the information piece. Just that one needs few trusted sources to check for the facts and myths. But on a real time basis, there is a dire need for some software which can nip such ‘false news’ in its bud. Leading it to be one of the most researched areas nowadays, these research focus on Multi-Modal-Framework (MMF) for fake news detection, which is based on “Text-Feature” as-well-as “Image-Features” to predict whether the News-article is “Fake” or “Real”.


Fake-news, image-pipeline, meta-data, word-vector, accuracy, multi-modal

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