文献类型：期刊文献

英文题名：A CNN-based misleading video detection model

作者：Li, Xiaojun[1];Xiao, Xvhao[1];Li, Jia[2];Hu, Changhua[1];Yao, Junping[1];Li, Shaochen[1]

机构：[1]Xian Res Inst High Tech, Xian 710025, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2022

卷号：12

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000782202600033)】；

基金：Funding was provided by Humanity and Social Science Youth Foundation of Ministry of Education of China [Grant number: 18YJC630068].

语种：英文

摘要：Videos, especially short videos, have become an increasingly important source of information in these years. However, many videos spread on video sharing platforms are misleading, which have negative social impacts. Therefore, it is necessary to find methods to automatically identify misleading videos. In this paper, three categories of features (content features, uploader features and environment features) are proposed to construct a convolutional neural network (CNN) for misleading video detection. The experiment showed that all the three proposed categories of features play a vital role in detecting misleading videos. Our proposed approach that combines three categories of features achieved the best performance with the accuracy of 0.90 and the F1 score of 0.89. It also outperformed other baselines such as SVM, k-NN, decision tree and random forest models by more than 22%.