Abstract
Measuring micro-blogging user influence is very important both in economic and social fields. In this paper, we propose a user-tweet interaction model to describe the relationships among users and tweets. Considering the time affect, TAC(time-effectiveness attenuation coefficient) is proposed when calculating tweet influence which consists of retweet influence and comment influence. Then we make a detail analysis on the generation of user influence which consists of post influence and follow influence based on the results of tweet influences. We also discuss the correlation between post influence and follow influence by use of Spearman’s rank correlation coefficient. At last, we rank users by calculating the bias spatial distances. Taking Sina micro-blogging as background, after a series of experiments, we believe that our method is accurate and comprehensive when measuring the influences of micro-blogging users.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
The reduction of micro-blogging users’ activity and stability, http://whb.news365.com.cn/jkw/201112/t20111221_3209369.htm
Yu, L., et al.: What Trends in Chinese Social Media. In: 5th SNA-KDD Workshop 2011, San Diego, pp. 2–4 (2011)
Lawrence, P., Sergey, B., et al.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project (1998)
Tunkelang, D.: A twitter analog to pagerank, http://thenoisychannel.com/2009/01/13/
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Liu, Y.: Research on social network structure. ZheJiang University (2008)
Kong, S., Feng, L.: Tweet-Centric Approach for Topic-Specific Author Ranking in Micro-Blog. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part I. LNCS, vol. 7120, pp. 138–151. Springer, Heidelberg (2011)
Yamaguchi, Y., Takahashi, T., Amagasa, T., Kitagawa, H.: TURank: Twitter User Ranking Based on User-Tweet Graph Analysis. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 240–253. Springer, Heidelberg (2010)
Balmin, A., Hristidis, V., Papakonstantinou, Y.: Objectrank: Authority-based keyword search in databases. In: 30th International Conference on Very Large Data Bases, vol. 30, pp. 564–575. Morgan Kaufmann (2004)
Cha, M., et al.: Measuring User Influence in Twitter: The Million Follower Fallacy. In: 4th international AAAI Conference on Weblogs and Social, Washington, DC, pp. 11–13 (2010)
Zar, J.H.: Significance Testing of the Spearman Rank Correlation Coefficient. Journal of the American Statistical 30, 578–581 (1972)
OpenAPI of Sina micro-blog, http://open.weibo.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, D., Wu, Q., Han, W. (2013). Measuring Micro-blogging User Influence Based on User-Tweet Interaction Model. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)