Abstract
The increased use of social network generates a huge amount of data. Extracting useful information from this huge data available is the need of today. Study and analysis of this data generated provide insight into the behavior of the customers or users and thus will be beneficial to increase the sales of products or understand customers. To achieve the same, we propose a novel framework which will extract trendy topics, identify communities related to these trendy, topics, and also identify influential or seed nodes in communities. The framework intends to find the list of topics which are popular, second, find trend-driven communities, and from these trend-driven communities find nodes which act as seed nodes and thus dominate the spread of information in the community. Analysis of real-world data is done and results are compared with baseline approaches.
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
References
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘03), pp. 137–146. ACM, New York, NY, USA (2003). http://dx.doi.org/10.1145/956750.956769
Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) Proceedings of the 3rd international conference on Internet and network economics (WINE'07), pp. 306–311. Springer, Berlin, Heidelberg (2007)
Bonchi, F.: Influence propagation in social networks: a data mining perspective. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 2(1) pp. 2–2 (2011)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, pp. 57–66 2001. https://doi.org/10.1145/502512.502525
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (TWEB) 1(1), 1–39 (2007)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining—WSDM ’10, p. 241 (2010). Available at: http://portal.acm.org/citation.cfm?doid=1718487.1718518
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probability ies for independent cascade model. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 67–75 (2008)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘10), 1029–1038. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1835804.1835934
Liu, L., et al.: Modelling of information diffusion on social networks with applications to WeChat. Physica A Stat. Mech. Appl. 496, 318–329 (2018)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: WSDM ’11, pp. 45–54 (2011)
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010)
Kim, Jooho, Hastak, Makarand: Social network analysis: characteristics of online social networks after a disaster. Int. J. Inf. Manag. 38(1), 86–96 (2018)
Tu, H.T., Nguyen, K.P.: Differential information diffusion model in social network. In: Asian Conference on Intelligent Information and Database Systems. Springer, Cham (2018)
Shi, J., et al.: Determinants of users’ information dissemination behavior on social networking sites: an elaboration likelihood model perspective. Internet Res. 28(2), 393–418 (2018)
Hu, W., et al.: Who will share my image?: Predicting the content diffusion path in online social networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM (2018)
Yang, Jaewon, Leskovec, Jure: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)
Tu, H.T., Nguyen, K.P.: Competitive information diffusion model in social network with negative information propagation. In: Asian Conference on Intelligent Information and Database Systems. Springer, Cham (2018)
Liu, X., Liu, C.: Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog. IEEE Access (2018)
Beydoun, G., et al.: Disaster management and information systems: insights to emerging challenges. Inf. Syst. Front. 20, 1–4 (2018)
Liang, Y., Kee, K.F.: Developing and validating the ABC framework of information diffusion on social media. New Media Soc. 20(1), 272–292 (2018)
Jiang, C., Chen, Y., Liu, K.J.R.: Evolutionary dynamics of information diffusion over social networks. IEEE Trans. Signal Process. 62(17), 4573–4586 (2014)
Romero, Galuba, W., Asur, S., Huberman, B.: Influence and passivity in social media. In: ECML/PKDD ’11, pp. 18–33 (2011)
Lim, K.W., Chen, C., Buntine, W.: Twitter-network topic model: a full Bayesian treatment for social network and text modeling (2016). arXiv preprint arXiv:1609.06791
Jain, S., Mohan, G., Sinha, A.: Network diffusion for information propagation in online social communities. In: 2017 Tenth International Conference on Contemporary Computing (IC3). IEEE (2017)
Stai, E., et al.: Temporal dynamics of information diffusion in twitter: modeling and experimentation. IEEE Trans. Comput. Soc. Syst. 5(1), 256–264 (2018)
Ye, X., et al.: Open source social network simulator focusing on spatial meme diffusion. In: Human Dynamics Research in Smart and Connected Communities. pp. 203–222. Springer, Cham (2018)
Jalayer, M., Azheian, M., Kermani, M.A.M.A.: A hybrid algorithm based on community detection and multi attribute decision making for influence maximization.”. Comput. Ind. Eng. 120, 234–250 (2018)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
AlSumait, L., Barbará, D., Domeniconi, C.: On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: 2008 Eighth IEEE International Conference on Data Mining, Pisa, pp. 3–12 (2008). https://doi.org/10.1109/icdm.2008.140
Cordeiro, M.: Twitter event detection: Combining wavelet analysis and topic inference summarization. In: Doctoral Symposium on Informatics Engineering, DSIE (2012)
Liu, G., Xu, X., Zhu, Y., Li, L.: An improved latent dirichlet allocation model for hot topic extraction. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 470–476. Sydney, NSW (2014). https://doi.org/10.1109/bdcloud.2014.55
Wu, C., Wu, B., Wang, B.: Event evolution model based on random walk model with hot topic extraction. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds.) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, vol. 10086. Springer, Cham (2016)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, P10008 (2008)
Acknowledgements
The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards. Informed consent was obtained from all individual participants included in the study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pagare, R., Khare, A., Chaudhary, S. (2020). (T-ToCODE): A Framework for Trendy Topic Detection and Community Detection for Information Diffusion in Social Network. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_43
Download citation
DOI: https://doi.org/10.1007/978-981-32-9949-8_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9948-1
Online ISBN: 978-981-32-9949-8
eBook Packages: EngineeringEngineering (R0)