Abstract
Currently, millions of companies, organizations and individuals take advantage of the social media function of Twitter to promote themselves. One of the most important goals is to attract web traffic. In this paper, we study the problem of obtaining web traffic via Twitter. We approach this problem in two stages. First, we analyze the correlation between important factors and the click number of URLs in tweets. Through measurements, we find that the commonly accepted method, increasing followers by reciprocal exchanges of links, has limited effects on improving the number of clicks. And characteristics of tweets (such as the presence of hashtags and tweet length) exert different impacts on users with different influence levels for obtaining the click number. In our second stage, based on the analyses, we introduce the Multi-Task Learning (MTL) to build a model for predicting the number of clicks. This model takes into account the specific characters of users with different influence levels to improve the predictive accuracy. The experiments, based on Twitter data, show the predictive performance is significantly higher than the baseline.
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References
How to use twitter to increase web traffic. http://www.wikihow.com/Use-Twitter-to-Increase-Web-Traffic
Twitter announces 100 million active users. http://mashable.com/2011/09/08/twitter-has-100-million-active-users
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61402151 and 61272527), Science and Technology Foundation of Henan Province of China (No. 162102410010), and Open Research Fund of Network and Data Security Key Laboratory of Sichuan Province of China (No. NDS2015-02).
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Xiao, C., Qin, Z., Luo, X., Kuzmanovic, A. (2016). Understanding Factors That Affect Web Traffic via Twitter. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_9
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