Understanding factors that affect web traffic via twitter

Chunjing Xiao*, Zhiguang Qin, Xucheng Luo, Aleksandar Kuzmanovic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationWeb Information Systems Engineering – WISE 2016 - 17th International Conference, Proceedings
EditorsJianmin Wang, Mohamed F. Mokbel, Hua Wang, Rui Zhou, Yanchun Zhang, Wojciech Cellary
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319487427
StatePublished - 2016
Event17th International Conference on Web Information Systems Engineering, WISE 2016 - Shanghai, China
Duration: Nov 8 2016Nov 10 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Web Information Systems Engineering, WISE 2016


  • Popularity
  • Prediction
  • Twitter
  • Web traffic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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