Clickbait Convolutional Neural Network

Hai-Tao Zheng,Yong Jiang,Cong-Zhi Zhao,Xin Yao,Jin-Yuan Chen,Arun Sangaiah

doi:10.3390/sym10050138

Abstract

With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.

Highlights

With the development of web advertisements these years, publishers want more clicks on their web pages to increase revenue from advertisements
We propose a clickbait convolutional neural network (CBCNN) model in this work
The results show that the CBCNN model outperforms all the five baseline methods in terms of accuracy, precision and recall

Summary

Introduction

With the development of web advertisements these years, publishers want more clicks on their web pages to increase revenue from advertisements. Under this circumstance, clickbait appears on the net, and tries to attract users’ attention and encourage them to click the link. Clickbait utilizes erotic words, misleading contents, unverified news and exaggerated tones to achieve their goals. While increasing the click-through rates (CTR) of the articles, clickbait decreases users’ satisfaction substantially because users feel a gap between what they want to know (the headline) and what they really read (the content). Clickbait causes fake news to spread on the Internet since many users forward them without further reading the contents.

Methods

Results

Discussion

Conclusion