Xi'an Research Institute of High-Tech, Xi'an, 710025, China.
School of Business, East China University of Science and Technology, Shanghai, 200237, China.
Sci Rep. 2021 Aug 9;11(1):16068. doi: 10.1038/s41598-021-95514-5.
With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.
随着互联网的飞速发展，虚假信息的广泛传播严重干扰了信息的搜索和识别。尽管人们对虚假文本检测进行了深入研究，但对充斥互联网的虚假短视频的研究却很少。由于虚假视频传播迅速、覆盖面广，可能会增加误解、影响决策，并导致不可挽回的损失。因此，检测互联网上误导用户的虚假视频非常重要。由于直接检测虚假视频很困难，因此我们在这项研究中探讨了虚假视频上传者的检测，旨在为虚假视频的检测提供依据。具体来说，构建了一个包含 450 个糖尿病和中医药相关视频上传者的数据集，提出了虚假视频上传者的五个特征，并构建了一个朴素贝叶斯模型。通过实验，确定了最佳特征组合，所提出的模型达到了 70.7%的最大准确率。
