Abstract
Traditional information spread and activation models on social networks, fail to take user interests towards specific content (topics) into account. To this, we propose a predictive topical spreading activation model (TopSPA). Following cues from the well-known spreading activation (SPA) model, we design the TopSPA algorithm to include the affinity of users to given topics. TopSPA utilizes the social connection structures of users, along with their topic affinities, to model the information flow. We use topic-based skew in energy seeding and energy propagation resistance in the network to form our overall information diffusion model. We empirically validate our model on multiple social event datasets on Twitter, predicting information diffusion over the social graph with a high accuracy.
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References
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM, pp. 65–74. ACM (2011)
Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: ICDM (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bourigault, S., Lagnier, C., Lamprier, S., Denoyer, L., Gallinari, P.: Learning social network embeddings for predicting information diffusion. In: WSDM, pp. 393–402. ACM (2014)
Bourigault, S., Lamprier, S., Gallinari, P.: Representation learning for information diffusion through social networks: an embedded cascade model. In: WSDM, pp. 573–582. ACM (2016)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. ICWSM 10, 10–17 (2010)
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S.,Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobiletelecom networks. In: EDBT, pp. 668–677. ACM (2008)
Fei, H., Jiang, R., Yang, Y., Luo, B., Huan, J.: Content based social behavior prediction: a multi-task learning approach. In: CIKM, pp. 995–1000 (2011)
Grabowicz, P.A., Ganguly, N., Gummadi, K.P.: Distinguishing between topical and non-topical information diffusion mechanisms in social media. In: ICWSM, pp. 151–160 (2016)
Halberstam, Y., Knight, B.: Homophily, group size, and the diffusion of political information in social networks: Evidence from twitter. J. Public Econ. 143, 73–88 (2016)
Jiang, C., Chen, Y., Liu, K.R.: Evolutionary dynamics of information diffusion over social networks. IEEE Trans. Signal Process. 62(17), 4573–4586 (2014)
Kuo, T.T., Hung, S.C., Lin, W.S., Peng, N., Lin, S.D., Lin, W.F.: Exploiting latent information to predict diffusions of novel topics on social networks. In: ACL (2012)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social media or a news media. In: Proceedings of the WWW (2010)
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM (2010)
McCallum, A.K.: Mallet: a machine learning for language toolkit. http://mallet.cs.umass.edu (2002)
Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: SIGKDD, pp. 33–41 (2012)
Nagar, S., Narang, K., Mehta, S., Subramaniam, L., Dey, K.: Topical discussions on unstructured microblogs: analysis from a geographical perspective. In: WISE (2013)
Nagar, S., Seth, A., Joshi, A.: Characterization of social media response to natural disasters. In: Proceedings of the WWW (2012)
Narang, K., Nagar, S., Mehta, S., Subramaniam, L.V., Dey, K.: Discovery and analysis of evolving topical social discussions on unstructured microblogs. In: ECIR (2013)
Nematzadeh, A., Ferrara, E., Flammini, A., Ahn, Y.Y.: Optimal network modularity for information diffusion. Phys. Rev. Lett. 113(8), 088,701 (2014)
Nguyen, J.H., Hu, B., Gnnemann, S., Ester, M.: Finding contexts of social influence in online social networks. In: The 7th SNA-KDD Workshop—SNA-KDD’13 (2013)
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, pp. 695–704 (2011)
Tang, J., Wu, S., Sun, J.: Confluence: Conformity influence in large social networks. In: KDD (2013)
Wu, H., Bu, J., Chen, C., Wang, C., Qiu, G., Zhang, L., Shen, J.: Modeling dynamic multi-topic discussions in online forums. In: AAAI Conference on Artificial Intelligence (2010)
Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: ICDM, pp. 599–608 (2010)
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Dey, K., Lamba, H., Nagar, S., Gupta, S., Kaushik, S. (2019). Modeling Topical Information Diffusion over Microblog Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_29
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