Role detection in online forums based on growth models for trees

  • Alberto Lumbreras
  • Bertrand Jouve
  • Julien Velcin
  • Marie Guégan
Original Article


Some structural characteristics of online discussions have been successfully modeled in the recent years. When parameters of these models are properly estimated, the models are able to generate synthetic discussions that are structurally similar to the real discussions. A common aspect of these models is that they consider that all users behave according to the same model. In this paper, we combine a growth model with an Expectation–Maximization algorithm that finds different parameters for different latent groups of users. We use this method to find the different roles that coexist in the community. Moreover, we analyze whether we can predict users behaviors based on their roles. Indeed, we show that predictions are improved for some of the roles when compared with a simple growth model.


Role detection Clustering Growth models Social networks Discussion forums 


  1. Agarwal N, Liu H, Tang L, Yu PS (2008) Identifying the influential bloggers in a community. In: Proceedings of the international conference on web search and web data mining—WSDM ’08, New York, NY, USA. ACM Press, p 207Google Scholar
  2. Angeletou S, Rowe M, Alani H (2011) Modelling and analysis of user behaviour in online communities. In: Proceedings of the 10th international semantic web conference, pp 35–50Google Scholar
  3. Aragón P, Gómez V, Kaltenbrunner A (2017) To thread or not to thread: the impact of conversation threading on online discussion. In: 11th international AAAI conference on web and social media. The AAAI PressGoogle Scholar
  4. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(October):509–512MathSciNetMATHGoogle Scholar
  5. Bensmail H, Celeux G, Raftery A, Robert C (1997) Inference in model-based cluster analysis. Stat Comput 7:1–10CrossRefGoogle Scholar
  6. Buntain C, Golbeck J (2014) Identifying social roles in reddit using network structure. In: Proceedings of the companion publication of the 23rd international conference on World wide web companion, pp 615–620Google Scholar
  7. Chan J, Hayes, C, Daly E (2010) Decomposing discussion forums using common user roles. In: Proceedings of the WebSci10: extending the frontiers of society on-lineGoogle Scholar
  8. Cheng J, Danescu-Niculescu-Mizil C, Leskovec J (2015) Antisocial behavior in online discussion communities. In: AAAI international conference on weblogs and social media. AAAI Press, pp 61–70Google Scholar
  9. Choobdar S, Ribeiro P, Silva F (2017) Evolutionary role mining in complex networks by ensemble clustering. In: Proceedings of the symposium on applied computing, SAC ’17, New York, NY, USA. ACM, pp 1053–1060Google Scholar
  10. Forestier M, Velcin J, Stavrianou A, Zighed D (2012) Extracting celebrities from online discussions. In: Proceedings of the 2012 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM 2012, pp 322–326Google Scholar
  11. Golder SA (2003) A typology of social roles in usenet. Ph.D. thesis, Harvard UniversityGoogle Scholar
  12. Gómez V, Kappen HJ, Kaltenbrunner A (2010) Modeling the structure and evolution of discussion cascades. In: Proceedings of the 22nd ACM conference on hypertext and hypermedia, pp 181–190Google Scholar
  13. Gómez V, Kappen HJ, Litvak N, Kaltenbrunner A (2012) A likelihood-based framework for the analysis of discussion threads. World Wide Web 16(5–6):645–675Google Scholar
  14. Goyal A, Bonchi F, Lakshmanan LV (2008). Discovering leaders from community actions. In: Proceeding of the 17th ACM conference on information and knowledge mining—CIKM ’08, New York, NY, USA. ACM Press, p 499Google Scholar
  15. Himelboim I, Gleave E, Smith M (2009) Discussion catalysts in online political discussions: content importers and conversation starters. J Comput Med Commun 14(4):771–789CrossRefGoogle Scholar
  16. Kolaczyk ED (2009) Statistical analysis of network data: methods and models. Springer, New YorkCrossRefMATHGoogle Scholar
  17. Kumar R, Mahdian M, McGlohon M (2010) Dynamics of conversations. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 553–562Google Scholar
  18. Kumar S, Spezzano F, Subrahmanian VS (2014) Accurately detecting trolls in Slashdot Zoo via decluttering. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, pp 188–195Google Scholar
  19. Lui M, Baldwin T (2010) Classifying user forum participants: separating the gurus from the hacks, and other tales of the internet. In: Proceedings of Australasian language technology association workshop, pp 49–57Google Scholar
  20. Nelder J, Mead R, Nelder BJ, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313MathSciNetCrossRefMATHGoogle Scholar
  21. Nolker RD, Zhou L (2005) Social computing and weighting to identify member roles in online communities. In: The 2005 IEEE/WIC/ACM international conference on web intelligence (WI’05). IEEE, pp 87–93Google Scholar
  22. Rowe M, Fernandez M, Angeletou S, Alani H (2013) Community analysis through semantic rules and role composition derivation. Web Semant Sci Serv Agents World Wide Web 18(1):31–47CrossRefGoogle Scholar
  23. Wang C, Ye M, Huberman BA (2012) From user comments to on-line conversations. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 244–252Google Scholar
  24. White A, Chan J, Hayes C, Murphy BT (2012) Mixed Membership models for exploring user roles in online fora. In: Proceedings of the 6th annual international conference on weblogs and social media—ICWSM2012, pp 599–602Google Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.CNRSInstitut de Recherche en Informatique de Toulouse - UMR 5505ToulouseFrance
  2. 2.FRAMESPA - UMR 5136, CNRSUniversité de ToulouseToulouseFrance
  3. 3.IMT - UMR 5219, CNRSUniversité de ToulouseToulouseFrance
  4. 4.Laboratoire ERICUniversité de LyonBronFrance
  5. 5.TechnicolorCesson-SévignéFrance

Personalised recommendations