Journal of Classification

, Volume 36, Issue 2, pp 322–349 | Cite as

Modeling Community Structure and Topics in Dynamic Text Networks

  • Teague R. HenryEmail author
  • David Banks
  • Derek Owens-Oas
  • Christine Chai


The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a bespoke Bayesian model applied to a dataset consisting of the top 467 US political blogs in 2012, their posts over the year, and their links to one another. Our model allows dynamic topic discovery to inform the latent network model and the network structure to facilitate topic identification. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested. We examine the time varying nature of the Sensational Crime topic, as well as the network properties of the Election News topic, as notable and easily interpretable empirical examples.


Networks Natural language processing Topic modeling Political blogs Community detection 



  1. Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P. (2008). Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research, 9(2008), 1981–2014.zbMATHGoogle Scholar
  2. Arun, R., Suresh, V., Madhavan, C.V., Murthy, M.N. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations. In Advances in knowledge discovery and data mining (pp. 391–402). Springer.Google Scholar
  3. Blei, D., Ng, A., Jordan, M. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  4. Blei, D.M., & Lafferty, J.D. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on machine learning (pp. 113–120). ACM.Google Scholar
  5. Brown, P.F., Desouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C. (1992). Class-based n-gram models of natural language. Computational linguistics, 18 (4), 467–479.Google Scholar
  6. Chang, J., & Blei, D.M. (2009). Relational topic models for document networks. In International conference on artificial intelligence and statistics (pp. 81–88).Google Scholar
  7. Faust, K., & Wasserman, S. (1992). Blockmodels: interpretation and evaluation. Social networks, 14(1), 5–61.CrossRefGoogle Scholar
  8. Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81(395), 832–842.MathSciNetCrossRefGoogle Scholar
  9. Gilks, W.R., Best, N., Tan, K. (1995). Adaptive rejection metropolis sampling within gibbs sampling. Applied Statistics, 44, 455–472.CrossRefGoogle Scholar
  10. Ho, Q., Eisenstein, J., Xing, E.P. (2012). Document hierarchies from text and links. In Proceedings of the 21st international conference on World Wide Web (pp. 739–748). ACM.Google Scholar
  11. Hoff, P.D., Raftery, A.E., Handcock, M.S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.MathSciNetCrossRefGoogle Scholar
  12. Hoffman, M., Bach, F.R., Blei, D.M. (2010). Online learning for latent dirichlet allocation. In Advances in neural information processing systems (pp. 856–864).Google Scholar
  13. Holland, P.W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs. Journal of the american Statistical association, 76 (373), 33–50.MathSciNetCrossRefGoogle Scholar
  14. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.CrossRefGoogle Scholar
  15. Hunter, D.R., Goodreau, S.M., Handcock, M.S. (2008). Goodness of Fit of Social Network Models. Journal of the American Statistical Association, 103(481), 248–258.MathSciNetCrossRefGoogle Scholar
  16. Krivitsky, P.N., & Handcock, M.S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 29–46.MathSciNetCrossRefGoogle Scholar
  17. Latouche, P., Birmelé, E., Ambroise, C. (2011). Overlapping stochastic block models with application to the French political blogosphere. Annals of Applied Statistics, 5(1), 309–336.MathSciNetCrossRefGoogle Scholar
  18. Lawrence, E., Sides, J., Farrell, H. (2010). Self-segregation or deliberation? blog readership, participation, and polarization in american politics. Perspectives on Politics, 8(01), 141.CrossRefGoogle Scholar
  19. McNamee, P., & Mayfield, J. (2003). Jhu/apl experiments in tokenization and non-word translation. In Comparative evaluation of multilingual information access systems (pp. 85–97). Springer.Google Scholar
  20. Moody, J. (2004). The structure of a social science collaboration network: disciplinary cohesion from 1963 to 1999. American Sociological Review, 69(2), 213–238.CrossRefGoogle Scholar
  21. Newman, M.E.J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 69 (22), 026113.CrossRefGoogle Scholar
  22. Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. J. Graph Algorithms Appl., 10(2), 191.MathSciNetCrossRefGoogle Scholar
  23. Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning.Google Scholar
  24. Robins, G., Elliott, P., Pattison, P. (2001). Network models for social selection processes. Social Networks, 23(1), 1–30.CrossRefGoogle Scholar
  25. Snijders, T.A., & Nowicki, K. (1997). Estimation and prediction for stochastic blockmodels for graphs with latent block structure. Journal of Classification, 14(1), 75–100.MathSciNetCrossRefGoogle Scholar
  26. Steinley, D. (2004). Properties of the hubert-arable adjusted rand index. Psychological Methods, 9(3), 386.CrossRefGoogle Scholar
  27. Wang, E., Silva, J., Willett, R., Carin, L. (2011). Dynamic relational topic model for social network analysis with noisy links. In 2011 IEEE, statistical signal processing workshop (SSP) (pp. 497–500). IEEE.Google Scholar
  28. Wasserman, S., & Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p . Psychometrika, 61(3), 401–425.MathSciNetCrossRefGoogle Scholar
  29. Yin, J., & Wang, J. (2014). A dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 233–242). ACM.Google Scholar

Copyright information

© Classification Society of North America 2019

Authors and Affiliations

  • Teague R. Henry
    • 1
    Email author
  • David Banks
    • 2
  • Derek Owens-Oas
    • 2
  • Christine Chai
    • 2
  1. 1.University of North CarolinaChapel HillUSA
  2. 2.Duke UniversityDurhamUSA

Personalised recommendations