Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.

中文翻译：

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DeepFakE：使用基于张量分解的深度神经网络改进假新闻检测

与传统方式相比，社交媒体平台简化了信息共享，其中也包括新闻。随着移动技术的革命，访问和共享数据的便利性导致了假新闻的泛滥。假新闻有可能操纵公众舆论，因此可能危害社会。因此，有必要检查在社交媒体上共享的新闻文章的可信度和真实性。如今，假新闻问题受到了研究界的广泛关注，需要一种高效低效的最优解决方案。现有的检测方法基于新闻内容或社交上下文，使用基于用户的特征作为个体。在本文中，新闻文章的内容和社交网络中回声室（基于社交媒体的用户共享相同意见的社区）的存在被考虑到假新闻检测中。通过结合新闻、用户和社区信息，形成一个表示社交上下文（社交媒体上的用户配置文件和新闻文章之间的相关性）的张量。新闻内容与张量融合，并采用耦合矩阵张量分解来获得新闻内容和社会背景的表示。所提出的方法已在真实世界的数据集 BuzzFeed 上进行了测试。分解后得到的因子被用作新闻分类的特征。集成机器学习分类器 (XGBoost) 和深度神经网络模型 (DeepFakE) 用于分类任务。