Product information diffusion in a social network
There is a need to understand how to: spread product information to maximum range, identifying influential users, and analyze how they are intrinsically connected in a social network. In this paper, we collected tweets of Huawei Mate 9 to analyze users’ information behavior such as tweeting, forwarding, and commenting on tweets. We applied independent cascade model to this empirical Twitter diffusion network, and found it is proper to fit to the product information diffusion process. Using its network structure and PageRank measurement, we can identify influential nodes, and interpret the intrinsic connection between these influential nodes. Further, it is significant to consider the node’s background, such as interest, occupation, and country when identifying influential nodes. And it is discussed that the tweet content related to novel technology may attract more participation in ordinary users.
KeywordsSocial network Information diffusion Independent cascade model Social influence
Research was sponsored in part by the National Social Science Fund Project “Study on dynamic optimization mechanism of information diffusion in social networks”, Agreement Number 15CTQ029.
- 2.Richardson, M., & Domingos, P. (2002). Mining knowledge-sharing sites for viral marketing. In The eighth ACM SIGKDD international conference (pp. 61–70). New York, New York, USA: ACM.Google Scholar
- 6.Leskovec, J., McGlohon, M., Faloutsos, C., & Glance, N. (2007). Patterns of cascading behavior in large blog graphs. In Presented at the 2007 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2007.Google Scholar
- 7.Yang, J., & Leskovec, J. (2010). Modeling information diffusion in implicit networks. In Presented at the 2010 IEEE 10th international conference on data mining (ICDM) (pp. 599–608). IEEE.Google Scholar
- 13.Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media?. In Presented at the 19th international conference on World wide web (p. 591). New York, USA: ACM Press.Google Scholar
- 18.Li, Q., Kailkhura, B., Thiagarajan, J. J., Zhang, Z., & Varshney, P. K. (2017). Influential node detection in implicit social networks using multi-task Gaussian copula models. In Presented at the NIPS 2016 time series workshop (pp. 27–37), Barcelona.Google Scholar
- 19.Kimura, M., & Saito, K. (2006). Tractable models for information diffusion in social networks. In Presented at the European conference on principles of data mining and knowledge discovery. Berlin, Heidelberg: Springer.Google Scholar
- 21.Mei, Y., Zhao, W., & Yang, J. (2017). Influence maximization on twitter—A mechanism for effective marketing campaign. In Presented at the IEEE international conference on communication (pp. 1–6). IEEE.Google Scholar
- 22.Sela, A., Goldenberg, D., Ben-Gal, I., & Shmueli, E. (2017). Latent viral marketing, concepts and control methods. CoRR, cs.SI. arXiv:1704.01775.
- 25.Smith, M., Ceni, A., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J., & Dunne, C. (2010). NodeXL: A free and open network overview, discovery and exploration add-in for Excel 2007/2010/2013/2016, from the Social Media Research Foundation. https://www.smrfoundation.org. Accessed 14 Mar 2018.
- 28.Kimura, M., Saito, K., Nakano, R., & Motoda, H. (2009). Finding influential nodes in a social network from information diffusion data. In Social computing and behavioral modeling (pp. 1–8). Boston, MA: Springer US.Google Scholar
- 31.Muller, E., & Peres, R. (2017). The effect of social networks structure on innovation performance: A review and directions for research. http://www.hitechmarkets.net/files/MullerPeres2017.pdf. Accessed 12 Mar 2018.
- 32.Van den Bulte, C., & Wuyts, S. (2007). Social networks and marketing. MSI relevant knowledge series. Cambridge, MA: Marketing Science Institute.Google Scholar
- 33.Sheth, J. N. (1974). A theory of family buying decisions. In J. N. Sheth (Ed.), Models of buyer behavior: Conceptual, quantitative, and empirical (pp. 17–33). New York: Harper & Row.Google Scholar
- 35.Scholz, M., Dorner, V., Landherr, A., & Probst, F. (2013). Awareness, interest, and final decision: The effects of user-and marketer-generated content on consumers’ purchase decisions. In Presented at the thirty fourth international conference on information systems (pp. 1–17). Milan: Nomos.Google Scholar