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Multi-level word features based on CNN for fake news detection in cultural communication

机译：基于CNN的多层次词特征在文化传播中的虚假新闻检测

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摘要

In recent years, due to the booming development of online social networks, fake news has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. However, fake news detection remains to be a challenge, primarily because the content is crafted to resemble the truth in order to deceive readers, and without fact-checking or additional information, it is often hard to determine veracity by text analysis alone. In this paper, we first proposed multi-level convolutional neural network (MCNN), which introduced the local convolutional features as well as the global semantics features, to effectively capture semantic information from article texts which can be used to classify the news as fake or not. We then employed a method of calculating the weight of sensitive words (TFW), which has shown their stronger importance with their fake or true labels. Finally, we develop MCNN-TFW, a multiple-level convolutional neural network-based fake news detection system, which is combined to perform fake news detection in that MCNN extracts article representation and WS calculates the weight of sensitive words for each news. Extensive experiments have been done on fake news detection in cultural communication to compare MCNN-TFW with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.
机译：近年来，由于在线社交网络的蓬勃发展，假新闻已经大量出现并在在线世界中广泛传播。用欺骗性的话语，在线社交网络用户可以很容易地被这些在线虚假新闻所感染，这已经给离线社会带来了巨大的影响。提高在线社交网络中信息的可信赖性的重要目标是及时识别虚假新闻。但是，伪造新闻检测仍然是一个挑战，主要是因为其内容被设计成类似于真相以欺骗读者，并且在没有事实检查或其他信息的情况下，通常很难仅通过文本分析来确定准确性。在本文中，我们首先提出了多级卷积神经网络（MCNN），它引入了局部卷积特征以及全局语义特征，以有效地从文章文本中捕获语义信息，这些信息可用于将新闻分类为假消息或假消息。不。然后，我们采用了一种计算敏感词权重（TFW）的方法，通过伪造或真实标签显示了它们的重要性。最后，我们开发了MCNN-TFW，这是一个基于多级卷积神经网络的假新闻检测系统，该系统组合起来可以执行假新闻检测，其中MCNN提取文章表示形式，WS计算每个新闻的敏感词权重。在文化传播中对假新闻检测进行了大量实验，以将MCNN-TFW与几种最新模型进行比较，实验结果证明了该模型的有效性。

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