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Combining Node Identifier Features and Community Priors for Within-Network Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Boost Clickbait Detection Based on User Behavior Analysis

Authors : Hai-Tao Zheng, Xin Yao, Yong Jiang, Shu-Tao Xia, Xi Xiao

Published in: Web and Big Data

Publisher: Springer International Publishing

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Abstract

Article in the web is usually titled with a misleading title to attract the users click for gaining click-through rate (CTR). A clickbait title may increase click-through rate, but decrease user experience. Thus, it is important to identify the articles with a misleading title and block them for specific users. Existing methods just consider text features, which hardly produce a satisfactory result. User behavior is useful in clickbait detection. Users have different tendencies for the articles with a clickbait title. User actions in an article usually indicate whether an article is with a clickbait title. In this paper, we design an algorithm to model user behavior in order to improve the impact of clickbait detection. Specifically, we use a classifier to produce an initial clickbait-score for articles. Then, we define a loss function on the user behavior and tune the clickbait score toward decreasing the loss function. Experiment shows that we improve precision and recall after using user behavior.

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Metadata
Title
Boost Clickbait Detection Based on User Behavior Analysis
Authors
Hai-Tao Zheng
Xin Yao
Yong Jiang
Shu-Tao Xia
Xi Xiao
Copyright Year
2017
Book
Web and Big Data

Print ISBN: 978-3-319-63563-7

Electronic ISBN: 978-3-319-63564-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63564-4_6

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