https://scholars.lib.ntu.edu.tw/handle/123456789/413076
標題: | Detection of false online advertisements with DCNN | 作者: | Huang H.-H. Wen Y.-W. HSIN-HSI CHEN |
關鍵字: | Convolutional neural network;Opinion spam detection;Overstated advertisement identification | 公開日期: | 2019 | 起(迄)頁: | 795-796 | 來源出版物: | 26th International World Wide Web Conference 2017 | 摘要: | In addition to opinion spam, the overstated or unproven information in false advertisements could also mislead customers while making purchasing decisions. A false-advertisement judgement system aims at recognizing and explaining the illegal false advertisements. In this paper, we incorporate the convolutional neural network (CNN) with word embeddings and syntactic features in the system. The recognition experiments show that Dependency-based CNN (DCNN) achieves F-scores of 86.77%, 93.18%, and 87.46% in the cosmetics, food, and drug datasets, respectively. Moreover, the explanation of illegality experiments shows the F-scores of 56.19%, 50.36%, and 62.06% in the three datasets. Our judgement system can contribute to different roles in the online advertising. ? 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413076 | ISBN: | 9781450349147 | DOI: | 10.1145/3041021.3054233 |
顯示於: | 資訊工程學系 |
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