Enhancing Online Discussion Forums with Topic-Driven Content Search and Assisted Posting

  • Damiano DistanteEmail author
  • Alejandro Fernandez
  • Luigi Cerulo
  • Aaron Visaggio
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 553)


Online forums represent nowadays one of the most popular and rich repository of user generated information over the Internet. Searching information of interest in an online forum may be substantially improved by a proper organization of the forum content. With this aim, in this paper we propose an approach that enhances an existing forum by introducing a navigation structure that enables searching and navigating the forum content by topics of discussion. Topics and hierarchical relations between them are semi-automatically extracted from the forum content by applying Information Retrieval techniques, specifically Topic Models and Formal Concept Analysis. Then, forum posts and discussion threads are associated to discussion topics on a similarity score basis. Moreover, to support automatic moderation in websites that host several forums, we propose a strategy to assist a user writing a new post in choosing the most appropriate forum into which it should be added. An implementation of the topic-driven content search and navigation and assisted posting forum enhancement approaches for the Moodle learning management system is also presented in the paper, opening to the application of these approaches to several real distance learning contexts. Finally, we also report on two case studies that we have conducted to validate the two approaches and evaluate their benefits.


Online discussion forums Information search Information extraction Text mining Topic modeling Navigability Searchability Assisted posting E-learning Learning management systems Moodle 


  1. 1.
    Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)Google Scholar
  2. 2.
    Bakalov, A., McCallum, A., Wallach, H.M., Mimno, D.M.: Topic models for taxonomies. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2012, Washington, D.C., USA, 10–14 June 2012, pp. 237–240 (2012)Google Scholar
  3. 3.
    Birkhoff, G. (ed.): Lattice Theory, vol. 25, 3rd edn. American Mathematical Society Colloquium Publications, Providence (1967)zbMATHGoogle Scholar
  4. 4.
    Blei, D.M.: Introduction to probabilistic topic models. Commun. ACM 55, 77–84 (2011). CrossRefGoogle Scholar
  5. 5.
    Castro, F., Nebot, A., Mugica, F.: Extraction of logical rules to describe students’ learning behavior. In: Proceedings of the Sixth Conference on IASTED International Conference Web-Based Education, WBED 2007, vol. 2, pp. 164–169. ACTA Press, Anaheim (2007).
  6. 6.
    Castro, F., Vellido, A., Nebot, A., Mugica, F.: Applying data mining techniques to e-learning problems. In: Jain, L., Tedman, R., Tedman, D. (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, vol. 62, pp. 183–221. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Cerulo, L., Distante, D.: Topic-driven semi-automatic reorganization of online discussion forums: a case study in an e-learning context. In: Proceedings of IEEE Global Engineering Education Conference (EDUCON 2013), pp. 303–310, March 2013Google Scholar
  8. 8.
    Dicheva, D., Dichev, C.: Tm4l: Creating and browsing educational topic maps. Br. J. Educ. Technol. 37(3), 391–404 (2006). CrossRefGoogle Scholar
  9. 9.
    Distante, D., Cerulo, L., Visaggio, C.A., Leone, M.: Enhancing online discussion forums with a topic-driven navigational paradigm: a plugin for the moodle learning management system. In: Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014, pp. 97–106. Scitepress (2014)Google Scholar
  10. 10.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  11. 11.
    Ghenname, M., Ajhoun, R., Gravier, C., Subercaze, J.: Combining the semantic and the social web for intelligent learning systems. In: Proceedings of IEEE Global Engineering Education Conference (EDUCON 2012), pp. 1–6, April 2012Google Scholar
  12. 12.
    Gruen, T.W., Osmonbekov, T., Czaplewski, A.J.: eWOM: the impact of customer-to-customer online know-how exchange on customer value and loyalty. J. Bus. Res. 59, 449–456 (2006)CrossRefGoogle Scholar
  13. 13.
    Hanna, M.: Data mining in the e-learning domain. Campus-Wide Inf. Syst. 21(1), 29–34 (2004)CrossRefGoogle Scholar
  14. 14.
    Hogo, M.A.: Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert Syst. Appl. 37(10), 6891–6903 (2010). CrossRefGoogle Scholar
  15. 15.
    Hrastinski, S.: What is online learner participation? A literature review. Comput. Educ. 51(4), 1755–1765 (2008)Google Scholar
  16. 16.
    Jakobsone, A., Kulmane, V., Cakula, S.: Structurization of information for group work in an online environment. In: Proceedings of IEEE Global Engineering Education Conference (EDUCON 2012), pp. 1–7, April 2012Google Scholar
  17. 17.
    Jalbert, N., Weimer, W.: Automated duplicate detection for bug tracking systems. In: IEEE International Conference on Dependable Systems and Networks with FTCS and DCC, 2008, DSN 2008, pp. 52–61, June 2008Google Scholar
  18. 18.
    Li, Q., Wang, J., Chen, Y.P., Lin, Z.: User comments for news recommendation in forum-based social media. Inf. Sci. 180, 4929–4939 (2010)CrossRefGoogle Scholar
  19. 19.
    Martin, A., Leon, C.: An intelligent e-learning scenario for knowledge retrieval. In: Proceedings of IEEE Global Engineering Education Conference (EDUCON 2012), pp. 1–6, April 2012Google Scholar
  20. 20.
    Meilă, M.: Comparing clusterings by the variation of information. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 173–187. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  21. 21.
    Nguyen, A.T., Nguyen, T.T., Nguyen, T.N., Lo, D., Sun, C.: Duplicate bug report detection with a combination of information retrieval and topic modeling. In: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012, pp. 70–79. ACM, New York (2012).
  22. 22.
    Otterbacher, J.: Searching for product experience attributes in online information sources. In: Proceedings of the International Conference on Information Systems (ICIS 2008). Association for Information Systems, December 2008Google Scholar
  23. 23.
    Romero, C., Ventura, S., Bra, P.D.: Knowledge discovery with genetic programming for providing feedback to courseware authors. User Model. User-Adap. Inter. 14(5), 425–464 (2005). CrossRefGoogle Scholar
  24. 24.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975). CrossRefzbMATHGoogle Scholar
  25. 25.
    Machado, L.D.S., Becker, K.: Distance education: a web usage mining case study for the evaluation of learning sites. In: 2003 IEEE International Conference on Advanced Learning Technologies (ICALT 2003), Athens, Greece, 9–11 July 2003, pp. 360–361. IEEE Computer Society (2003)Google Scholar
  26. 26.
    Stefan, H.: A theory of online learning as online participation. Comput. Educ. 52(1), 78–82 (2009)CrossRefGoogle Scholar
  27. 27.
    Sudau, F., Friede, T., Grabowski, J., Koschack, J., Makedonski, P., Himmel, W.: Sources of information and behavioral patterns in online health forums: qualitative study. J. Med. Internet Res. 16, e10 (2014)CrossRefGoogle Scholar
  28. 28.
    Sung, H.H., Sung, M,B., Sang, C.P.: Web mining for distance education. In: Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology (ICMIT 2000), vol. 2, pp. 715–719. IEEE (2000).
  29. 29.
    Tang, T., McCalla, G.: Smart recommendation for an evolving e-learning system: architecture and experiment. Int. J. e-Learning 4(1), 105–129 (2005)Google Scholar
  30. 30.
    Tsai, C.-J., Tseng, S.S., Lin, C.-Y.: A two-phase fuzzy mining and learning algorithm for adaptive learning environment. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS-ComputSci 2001. LNCS, vol. 2074, pp. 429–438. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  31. 31.
    Wallach, H.M., Mimno, D.M., McCallum, A.: Rethinking LDA: why priors matter. In: 23rd Annual Conference on Neural Information Processing Systems 2009. Advances in Neural Information Processing Systems, vol. 22, pp. 1973–1981 (2009)Google Scholar
  32. 32.
    Yang, Q., Sun, J., Wang, J., Jin, Z.: Semantic web-based personalized recommendation system of courses knowledge research. In: Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics, ICICCI 2010, pp. 214–217. IEEE Computer Society, Washington, D.C. (2010).
  33. 33.
    Zaíane, O.R.: Building a recommender agent for e-learning systems. In: Proceedings of the International Conference on Computers in Education, ICCE 2002, pp. 55–59. IEEE Computer Society, Washington, D.C. (2002).
  34. 34.
    Zhang, K., Peck, K.: The effects of peer-controlled or moderated online collaboration on group problem solving and related attitudes. Can. J. Learn. Technol./La revue canadienne de l’apprentissage et de la technologie 29(3), 93–112 (2003)Google Scholar
  35. 35.
    Zhou, Y., Cong, G., Cui, B., Jensen, C.S., Yao, J.: Routing questions to the right users in online communities. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE 2009, pp. 700–711. , IEEE Computer Society, Washington, D.C. (2009).

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Damiano Distante
    • 1
    Email author
  • Alejandro Fernandez
    • 2
  • Luigi Cerulo
    • 3
  • Aaron Visaggio
    • 3
  1. 1.Unitelma Sapienza UniversityRomeItaly
  2. 2.LIFIA, CIC/F.I.National University of La PlataLa PlataArgentina
  3. 3.University of SannioBeneventoItaly

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