ABSTRACT The widespread use of social media has enormous consequences for the society, culture and business with potentially positive and negative effects. As online social networks are increasingly used for dissemination of information, at the same they are also becoming a medium for the spread of fake news for various commercial and political purposes. Technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP) tools offer great promise for researchers to build systems, which could automatically detect fake news. However, detecting fake news is a challenging task to accomplish as it requires models to summarize the news and compare it to the actual news in order to classify it as fake. This project proposes a framework that detects and classifies fake news messages using improved Recurrent Neural Networks and Deep Structured Semantic Model. The proposed approach intuitively identifies important features associated with fake news without previous domain knowledge while achieving accuracy 99%. The performance analysis method used for the proposed system is based on accuracy, specificity and sensitivity.

中文翻译：

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使用 DSSM 和改进的循环神经网络分类器进行假新闻识别和分类

摘要 社交媒体的广泛使用对社会、文化和商业产生了巨大的影响，具有潜在的积极和消极影响。随着在线社交网络越来越多地用于传播信息，同时它们也成为传播虚假新闻的媒介，用于各种商业和政治目的。人工智能 (AI) 和自然语言处理 (NLP) 工具等技术为研究人员构建可以自动检测假新闻的系统提供了广阔的前景。然而，检测假新闻是一项具有挑战性的任务，因为它需要模型来总结新闻并将其与实际新闻进行比较，以便将其归类为假新闻。该项目提出了一个框架，该框架使用改进的循环神经网络和深度结构化语义模型来检测和分类假新闻消息。所提出的方法直观地识别与假新闻相关的重要特征，而无需先前的领域知识，同时达到 99% 的准确率。用于建议系统的性能分析方法基于准确性、特异性和敏感性。