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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Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant No. 61375054), Natural Science Foundation of Guangdong Province Grant No. 2014A030313745, Basic Scientific Research Program of Shenzhen City (Grant No. JCYJ20160331184440545), and Cross fund of Graduate School at Shenzhen, Tsinghua University (Grant No. JC20140001).
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Zheng, HT., Yao, X., Jiang, Y., Xia, ST., Xiao, X. (2017). Boost Clickbait Detection Based on User Behavior Analysis. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_6
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DOI: https://doi.org/10.1007/978-3-319-63564-4_6
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