Jarrahi Ali, Safari Leila
Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran.
Multimed Tools Appl. 2023;82(2):2913-2939. doi: 10.1007/s11042-022-12668-8. Epub 2022 Apr 11.
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 control and verification mechanism has led to the spread of fake news as one of the most critical threats to democracy, economy, journalism, health, and freedom of expression. So, designing and using efficient automated methods to detect fake news on social media has become a significant challenge. One of the most relevant entities in determining the authenticity of a news statement on social media is its publishers. This paper examines the publishers' features in detecting fake news on social media, including , , , , and . In this regard, we propose an algorithm, namely for evaluating publishers' credibility on social networks. We also suggest a high accurate multi-modal framework, namely FR-Detect, for fake news detection using user-related and content-related features. Furthermore, a sentence-level convolutional neural network is provided to properly combine publishers' features with latent textual content features. Experimental results show that the publishers' features can improve the performance of content-based models by up to 16% and 31% in accuracy and F1, respectively. Also, the behavior of publishers in different news domains has been statistically studied and analyzed.
随着互联网的扩展和引人注目的社交媒体基础设施的发展，人们更喜欢通过这些媒体来关注新闻。尽管这些媒体在新闻领域有诸多优势，但缺乏控制和验证机制导致了假新闻的传播，这成为对民主、经济、新闻业、健康和言论自由最严重的威胁之一。因此，设计和使用高效的自动化方法来检测社交媒体上的假新闻已成为一项重大挑战。在确定社交媒体上新闻声明的真实性时，最相关的实体之一是其发布者。本文研究了发布者在检测社交媒体上假新闻时的特征，包括 、 、 、 和 。在这方面，我们提出了一种算法，即 ，用于评估社交网络上发布者的可信度。我们还提出了一个高精度的多模态框架，即FR-Detect，用于使用与用户相关和与内容相关的特征进行假新闻检测。此外，还提供了一个句子级卷积神经网络，以将发布者的特征与潜在的文本内容特征适当结合。实验结果表明，发布者的特征可以分别将基于内容的模型的准确率和F1值提高多达16%和31%。此外，还对发布者在不同新闻领域的行为进行了统计研究和分析。
