Abstract
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%.
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