A Convolutional Neural Network for Clickbait Detection

Click-baits are headlines that exaggerate the facts or hide the partial facts to attract user clicks. Click-baits deter readers from effectively and efficiently obtaining information in the era of information explosion, and will obviously affect user experience in news aggregator sites like Google N...

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Bibliographic Details
Published in2017 4th International Conference on Information Science and Control Engineering (ICISCE) pp. 6 - 10
Main Authors Junfeng Fu, Liang Liang, Xin Zhou, Jinkun Zheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Click-baits Detection
Convolutional Neural Network
Deep Learning
Feature extraction
Measurement
Media
Neural networks
Syntactics
Training
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Summary:Click-baits are headlines that exaggerate the facts or hide the partial facts to attract user clicks. Click-baits deter readers from effectively and efficiently obtaining information in the era of information explosion, and will obviously affect user experience in news aggregator sites like Google News and Yahoo News. Detecting and preventing click-baits become crucial. Previous work achieved remarkable performances on this task with hand-crafted lexical and syntactic features on limited platforms. However, this line of work heavily depend on expertise knowledge and can not be easily applied to languages that do not share such features. To address above issues, we propose a general end-to-end Convolutional Neural Network based approach, which utomatically induces useful features for the end task without relying on any external resources. Empirical experiments on English and Chinese corpus show that our method achieves consistent results, showing the effec- tiveness and robustness of our approach across languages. We will share our annotated corpus collected from Chinese news news sites on publication.
DOI:10.1109/ICISCE.2017.11