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Combating Misinformation Dissemination through Verification and Content Driven Recommendation

机译:通过验证和内容驱动推荐对抗错误信息传播

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The COVID 19 pandemic is a humanitarian emergency that poses an enormous threat to society and has impacted various social media platforms and journalism. News and social media has become an immensely popular platform for consumption of information. However, these platforms are also the bearer of fake news and information which causes negative effects and creates panic. Thus, this research work aim to tackle this problem by creating a unique hybrid model using Machine learning algorithms with Natural Language Processing (NLP) techniques to verify news. In order to make the proposed system foolproof, a superior content based recommendation system is developed which will encourage users to consume authenticated news and content from verified sources. Thus, such a system will provide a holistic approach as it not only verifies but also provides genuine and true recommendations for the same.
机译:Covid 19 Pandemic是一个人道主义紧急情况,对社会构成了巨大威胁,并影响了各种社交媒体平台和新闻。新闻和社交媒体已成为一种非常受欢迎的信息平台,用于消费信息。然而,这些平台也是假新闻和信息的承载,导致负面影响并创造恐慌。因此,这项研究工作旨在通过使用具有自然语言处理(NLP)技术的机器学习算法创建独特的混合模型来解决这个问题来验证新闻。为了使建议的系统万无一失,开发了一种卓越的内容推荐系统,这将鼓励用户从验证的来源中消耗经过身份验证的新闻和内容。因此,这样的系统将提供整体方法,因为它不仅验证,而且还提供了相同的真实和真正的建议。

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