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A Comparative Study of Users’ Microblogging Behavior on Sina Weibo and Twitter

  • Qi Gao
  • Fabian Abel
  • Geert-Jan Houben
  • Yong Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7379)

Abstract

In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time.

Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research.

Keywords

user modeling microblogging comparative usage analysis 

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References

  1. 1.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW 2010, pp. 851–860. ACM (2010)Google Scholar
  2. 2.
    Long, R., Wang, H., Chen, Y., Jin, O., Yu, Y.: Towards Effective Event Detection, Tracking and Summarization on Microblog Data. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 652–663. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: CHI 2010, pp. 1185–1194. ACM (2010)Google Scholar
  4. 4.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW 2010, pp. 591–600. ACM (2010)Google Scholar
  6. 6.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In: WWW 2011. ACM (2011)Google Scholar
  7. 7.
    Gao, Q., Abel, F., Houben, G.J.: GeniUS: Generic User Modeling Library for the Social Semantic Web. In: JIST 2011. Springer (2011)Google Scholar
  8. 8.
    Mandl, T.: Comparing Chinese and German Blogs. In: HT 2009. ACM (2009)Google Scholar
  9. 9.
    Hofstede, G., Hofstede, G.J.: Cultures and Organizations: Software of the Mind. McGraw-Hill (2005)Google Scholar
  10. 10.
    Chen, L., Tsoi, H.K.: Analysis of user tags in social music sites: Implications for cultural differences. In: CSCW 2011. ACM (2011)Google Scholar
  11. 11.
    Yu, L., Asur, S., Huberman, B.A.: What trends in chinese social media. CoRR abs/1107.3522 (2011)Google Scholar
  12. 12.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford University (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qi Gao
    • 1
  • Fabian Abel
    • 1
  • Geert-Jan Houben
    • 1
  • Yong Yu
    • 2
  1. 1.Web Information SystemsDelft University of TechnologyThe Netherlands
  2. 2.APEX Data & Knowledge Management Lab.Shanghai Jiaotong UniversityChina

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