Abstract
To understand how the influencers of event information dissemination on social media can be identified, we propose three perspectives for investigating this topic: the number of related messages posted by the influencers, the number of related messages in which the influencers are mentioned by other users, and the number of influencers’ messages that are reposted. The findings regarding social influencers can help companies identify the key people or organizations with whom they must engage. In addition, we used a social network diagram to depict how event information is disseminated from the influencers. This diagram shows the top influencers at different stages of information dissemination. Effectively modeling relationships among top users and accordingly using them to filter or recommend information are fundamental for mining social networking services. To illustrate our approach, we used the tweets from the Windows 8.1 launch event as a case study.
Chapter PDF
Similar content being viewed by others
References
Thevenot, G.: Blogging as a Social Media. Tourism and Hospitality Research 7, 287–289 (2007)
Smith, C.: By the Numbers: 130 Amazong Facebook User & Demographic Statistics, http://expandedramblings.com/index.php/by-the-numbers-17-amazing-facebook-stats/
Cha, M., Mislove, A., Gummadi, K.P.: A Measurement-Driven Analysis of Information Propagation in the Flickr Social Network. In: Proceedings of the 18th International Conference on World Wide Web, pp. 721–730. ACM, Madrid (2009)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a Social Network or a News Media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM, Raleigh (2010)
Ediger, D., Jiang, K., Riedy, J., Bader, D.A., Corley, C., Farber, R., Reynolds, W.N.: Massive Social Network Analysis: Mining Twitter for Social Good. In: 2010 39th International Conference on Parallel Processing, pp. 583–593 (2010)
Aral, S., Walker, D.: Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks. Management Science 57, 1623–1639 (2011)
Yoganarasimhan, H.: Impact of Social Network Structure on Content Propagation: A Study Using YouTube Data. Quantitative Marketing and Economics 10, 111–150 (2012)
Lin, Y.-R., Lazer, D., Cao, N.: Watching How Ideas Spread Over Social Media. Leonardo 46, 277–277 (2013)
Booth, N., Matic, J.A.: Mapping and Leveraging Influencers in Social Media to Shape Corporate Brand Perceptions. Corporate Communications 16, 184–191 (2011)
Efron, M.: Information Search and Retrieval in Microblogs. Journal of the American Society for Information Science and Technology 62, 996–1008 (2011)
Brenner, J., Smith, A.: 72% of Online Adults are Social Networking Site Users. Pew Research Center’s Internet & American Life Project, Pew Research Center (2013)
Jin, F., Dougherty, E., Saraf, P., Cao, Y., Ramakrishnan, N.: Epidemiological Modeling of News and Rumors on Twitter. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, pp. 1–9 (2013)
Borondo, J., Morales, A., Losada, J., Benito, R.: Characterizing and Modeling an Electoral Campaign in the Context of Twitter: 2011 Spanish Presidential Election as a Case Study. Chaos: An Interdisciplinary Journal of Nonlinear Science 22, 23138 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Shen, CW., Kuo, CJ. (2014). Analysis of Social Influence and Information Dissemination in Social Media: The Case of Twitter. In: Saeed, K., Snášel, V. (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science, vol 8838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45237-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-662-45237-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45236-3
Online ISBN: 978-3-662-45237-0
eBook Packages: Computer ScienceComputer Science (R0)