Conversation Analysis on Social Networking Sites

Conference object English OPEN
Belkaroui , Rami ; Faiz , Rim ; Elkhlifi , Aymen (2014)
  • Publisher: HAL CCSD
  • Related identifiers: doi: 10.1109/SITIS.2014.80
  • Subject: Social Network | Twitter | Conversation retrieval | [ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR] | [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] | user interactions | [ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI] | social media

International audience; With the explosion of Web 2.0, people are becoming more communicative through expansion of services and multi-platform applications such as microblogs, forums and social networks which establishes social and collabora-tive backgrounds. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works focused only to retrieve separate tweets or those sharing same hashtags, but, it is not powerful enough if the goal of the search is to retrieve relevant tweets based on content. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on microblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and google. We have evaluated our method on collected social network corpus related to specific subjects, and we obtained good results.
  • References (24)
    24 references, page 1 of 3

    [1] L. B. Jabeur, L. Tamine, and M. Boughanem, “Uprising microblogs: A bayesian network retrieval model for tweet search,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing. New York, NY, USA: ACM, 2012, pp. 943-948. [Online]. Available: http://doi.acm.org/10.1145/2245276.2245459

    [2] D. Boyd, S. Golder, and G. Lotan, “Tweet, tweet, retweet: Conversational aspects of retweeting on twitter,” in Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, ser. HICSS '10. Washington, DC, USA: IEEE Computer Society, 2010, pp. 1-10. [Online]. Available: http://dx.doi.org/10.1109/HICSS.2010.412

    [3] Y.-C. Wang, M. Joshi, W. W. Cohen, and C. P. Ros, “Recovering implicit thread structure in newsgroup style conversations.” in ICWSM, E. Adar, M. Hurst, T. Finin, N. S. Glance, N. Nicolov, and B. L. Tseng, Eds. The AAAI Press, 2008. [Online]. Available: http://dblp.uni-trier.de/db/conf/icwsm/icwsm2008.html

    [4] W. Xi, J. Lind, and E. Brill, “Learning effective ranking functions for newsgroup search,” in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '04. New York, NY, USA: ACM, 2004, pp. 394-401. [Online]. Available: http://doi.acm.org/10.1145/1008992.1009060

    [5] D. Feng, E. Shaw, J. Kim, and E. Hovy, “Learning to detect conversation focus of threaded discussions,” in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, ser. HLTNAACL '06. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006, pp. 208-215. [Online]. Available: http://dx.doi.org/10.3115/1220835.1220862

    [6] L. Hong and B. D. Davison, “A classification-based approach to question answering in discussion boards,” in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '09. New York, NY, USA: ACM, 2009, pp. 171-178. [Online]. Available: http: //doi.acm.org/10.1145/1571941.1571973

    [7] J. Seo, W. B. Croft, and D. A. Smith, “Online community search using thread structure,” in Proceedings of the 18th ACM conference on Information and knowledge management, ser. CIKM '09. New York, NY, USA: ACM, 2009, pp. 1907-1910. [Online]. Available: http://doi.acm.org/10.1145/1645953.1646262

    [8] S. Ding, G. Cong, C. yew Lin, and X. Zhu, “Using conditional random fields to extract contexts and answers of questions from online forums,” in In Proceedings of Association for Computational Linguistics ACL-08: HLT, 2008, pp. 710-718.

    [9] B. Kerr, “Thread arcs: an email thread visualization,” in Proceedings of the Ninth annual IEEE conference on Information visualization, ser. INFOVIS'03. Washington, DC, USA: IEEE Computer Society, 2003, pp. 211- 218. [Online]. Available: http://dl.acm.org/citation.cfm?id= 1947368.1947407

    [10] D. Lam, S. L. Rohall, C. Schmandt, and M. K. Stern, “Exploiting E-mail Structure to Improve Summarization,” 2002. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.12.7056

  • Metrics
    No metrics available
Share - Bookmark