Juan Antonio Lossio-Ventura
Mathieu Roche
ABSTRACT
We propose in this paper to handle the problem of overload in social interactions by grouping messages according to three important dimensions: (i) content (textual and hashtags), (ii) users, and (iii) time difference. We evaluated our approach on a Twitter data set and we compared it to other existing approaches and the results are promising and encouraging.
- C. C. Aggarwal and C. Zhai. A survey of text clustering algorithms. In Mining Text Data, pages 77--128. Springer, 2012.Google Scholar
Digital Library
- R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999. Google ScholarDigital Library
- A. E. Cano, A. Varga, M. Rowe, F. Ciravegna, and Y. He. Harnessing linked knowledge sources for topic classification in social media. In Proc. of the 24th, HT'13, pages 41--50. ACM, 2013. Google ScholarDigital Library
- M. Chen, X. Jin, and D. Shen. Short text classification improved by learning multi-granularity topics. In Proc. of the 22nd IJCAI, IJCAI'11, 2011. Google ScholarDigital Library
- Z. Dai, A. Sun, and X.-Y. Liu. Crest: Cluster-based representation enrichment for short text classification. In Advances in KDD, pages 256--267. Springer, 2013.Google ScholarCross Ref
- L. Derczynski, A. Ritter, S. Clark, and K. Bontcheva. Twitter part-of-speech tagging for all: Overcoming sparse and noisy data. In Proc. of the RANLP. ACL, 2013.Google Scholar
- S. Erera and D. Carmel. Conversation detection in email systems. In Proc. of the 31st, ECIR'09, pages 498--505, 2008. Google ScholarDigital Library
- M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Cluster validity methods: part i. ACM Sigmod Record, 31(2):40--45, 2002. Google ScholarDigital Library
- X. Hu, N. Sun, C. Zhang, and T.-S. Chua. Exploiting internal and external semantics for the clustering of short texts using world knowledge. In Proc. of the 18th, CIKM'09, pages 919--928. ACM, 2009. Google ScholarDigital Library
- W. Hua, Z. Wang, H. Wang, K. Zheng, and X. Zhou. Short text understanding through lexical-semantic analysis. ICDE, April 2015.Google ScholarCross Ref
- J. A. Lossio-Ventura, H. Hacid, A. Ansiaux, and M. L. Maag. Conversations reconstruction in the social web. In Proc. of the 21st, WWW'12, pages 573--574. ACM, 2012. Google ScholarDigital Library
- J. A. Lossio-Ventura, C. Jonquet, M. Roche, and M. Teisseire. Biomedical term extraction: overview and a new methodology. Information Retrieval Journal, pages 1--41, 2015. Google ScholarDigital Library
- X.-H. Phan, L.-M. Nguyen, and S. Horiguchi. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In Proc. of the 17th, WWWW'08, pages 91--100. ACM, 2008. Google ScholarDigital Library
- A. Rangrej, S. Kulkarni, and A. V. Tendulkar. Comparative study of clustering techniques for short text documents. In Proc. of the 20th, WWW '11, pages 111--112. ACM, 2011. Google ScholarDigital Library
- K. D. Rosa, R. Shah, B. Lin, A. Gershman, and R. Frederking. Topical clustering of tweets. 2011.Google Scholar
- L. Shou, Z. Wang, K. Chen, and G. Chen. Sumblr: Continuous summarization of evolving tweet streams. In Proc. of the 36th, SIGIR '13, pages 533--542. ACM, 2013. Google ScholarDigital Library
- B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas. Short text classification in twitter to improve information filtering. In Proc. of the 33rd, SIGIR'10, pages 841--842, 2010. Google ScholarDigital Library
- A. Sun. Short text classification using very few words. In Proc. of the 35th, SIGIR'12, pages 1145--1146. ACM, 2012. Google ScholarDigital Library
- G. Tang, Y. Xia, W. Wang, R. Lau, and F. Zheng. Clustering tweets using wikipedia concepts. In Proc. of, LREC'14, 2014.Google Scholar
- F. Wang, Z. Wang, Z. Li, and J.-R. Wen. Concept-based short text classification and ranking. In Proc. of the 23rd, CIKM'14, pages 1069--1078. ACM, 2014. Google ScholarDigital Library
- T. Xu and D. W. Oard. Wikipedia-based topic clustering for microblogs. Proc. of the American Society for Information Science and Technology, 48(1):1--10, 2011.Google ScholarCross Ref
- J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In IEEE International Conference on Data Mining. Stanford InfoLab, 2010. Google ScholarDigital Library
- J. Yin and J. Wang. A dirichlet multinomial mixture model-based approach for short text clustering. In Proc. of the 20th, KDD '14, pages 233--242. ACM, 2014. Google ScholarDigital Library
- Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. In Proc. of the 36th, SIGIR '13, pages 363--372. ACM, 2013. Google ScholarDigital Library
Index Terms
- Communication overload management through social interactions clustering
Recommendations
Structuring computer-mediated communication systems to avoid information overload
Unless computer-mediated communication systems are structured, users will be overloaded with information. But structure should be imposed by individuals and user groups according to their needs and abilities, rather than through general software ...
Comments