Detection of false online advertisements with DCNN
Journal
26th International World Wide Web Conference 2017
Pages
795-796
ISBN
9781450349147
Date Issued
2019
Author(s)
Abstract
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.
Subjects
Convolutional neural network
Opinion spam detection
Overstated advertisement identification
SDGs
Type
conference paper