In recent years, with the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of any control and verification mechanism has led to the spread of fake news, as one of the most important threats to democracy, economy, journalism and freedom of expression. Designing and using automatic methods to detect fake news on social media has become a significant challenge. In this paper, we examine the publishers' role in detecting fake news on social media. We also suggest a high accurate multi-modal framework, namely FR-Detect, using user-related and content-related features with early detection capability. For this purpose, two new user-related features, namely Activity Credibility and Influence, have been introduced for publishers. Furthermore, a sentence-level convolutional neural network is provided to combine these features with latent textual content features properly. Experimental results have shown that the publishers' features can improve the performance of content-based models by up to 13% and 29% in accuracy and F1-score, respectively.
翻译:近年来,随着互联网的扩大和具有吸引力的社会媒体基础设施的扩大,人们倾向于通过这些媒体跟踪新闻。尽管这些媒体在新闻领域有许多优势,但缺乏任何控制和核查机制导致虚假新闻的传播,这是对民主、经济、新闻和言论自由的最重要威胁之一。设计和使用自动方法在社交媒体上探测假新闻已成为一项重大挑战。在本文中,我们审视了出版商在发现社交媒体上的假新闻方面的作用。我们还提出一个高度准确的多模式框架,即FR-探测器,利用与用户相关和内容相关的功能以及早期检测能力。为此,为出版商引入了两个与用户有关的新特征,即活动信誉和影响力。此外,还提供了一个判决级的动态神经网络,将这些特征与潜在的文本内容特征适当地结合起来。实验结果表明,出版商的特征可以提高内容模型的性能,精确率和F1核心的性能分别达到13%和29 %。