Abstract
This work concerns the analysis of number, sizes and other characteristics of groups identified in the blogosphere using a set of models identifying social relations. These models differ regarding identification of social relations, influenced by methods of classifying the addressee of the comments (they are either the post author or the author of a comment on which this comment is directly addressing) and by a sentiment calculated for comments considering the statistics of words present and connotation. The state of a selected blog portal was analyzed in sequential, partly overlapping time intervals. Groups in each interval were identified using a version of the CPM algorithm, on the basis of them, stable groups, existing for at least a minimal assumed duration of time, were identified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agarwal, N., Liu, H.: Modeling and Data Mining in Blogosphere. Moegan & Claypool Publishers (2009)
Akritidis, L., Katsaros, D., Bozanis, P.: Identifying influential bloggers: Time does matter. In: Procs. of the 2009 IEEE/WIC/ACM Int. Joint Conf. on Web Intelligence and Intelligent Agent Technology, WI-IAT 2009, vol. 1, pp. 76–83. IEEE Computer Society, Washington, DC (2009)
Bermingham, A., Conway, M., McInerney, L., O’Hare, N., Smeaton, A.F.: Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In: Proc. of the 2009 Int. Conf. on Adv. in Social Network Analysis and Mining, pp. 231–236. IEEE Comp. Soc., Washington, DC, USA (2009)
Chi, Y., Zhu, S., Song, X., Tatemura, J., Tseng, B.L.: Structural and temporal analysis of the blogosphere through community factorization. In: Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 163–172. ACM, New York (2007)
Fortunato, S.: Community detection in graphs. Phys. Rep., ch. 486 (2010)
Gliwa, B., Saganowski, S., Zygmunt, A., Bródka, P., Kazienko, P., Koźlak, J.: Identification of group changes in blogosphere. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, Istanbul, Turkey, August 26-29. IEEE Computer Society (2012)
Gryc, W., Moilanen, K.: Leveraging textual sentiment analysis with social network modelling: Sentiment analysis of political blogs in the 2008 u.s. presidential election. In: Procs. of the ”From Text to Political Positions” Workshop (T2PP 2010). Vrije Universiteit, Amsterdam (2010)
Koźlak, J., Zygmunt, A.: Agent-based modelling of social organisations. In: International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, Korean Bible University, Seoul, Korea, June 30-July 2, pp. 467–472. IEEE Computer Society (2011)
Krauss, J., Nann, S., Simon, D., Fischbach, K., Gloor, P.: Predicting movie success and academy awards through sentiment and social network analysis. In: ECIS 2008 Proceedings (2008)
Nguyen, T., Phung, D.Q., Adams, B., Tran, T., Venkatesh, S.: Hyper-community detection in the blogosphere. In: Proceedings of Second ACM SIGMM Workshop on Social Media, WSM 2010, pp. 21–26. ACM, New York (2010)
Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.S.: Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recogn. 43(1), 113–127 (2010)
Palla, G., Ábel, D., Farkas, I.J., Pollner, P., Derényi, I., Vicsek, T.: k-clique percolation and clustering. In: Bollobás, B., Kozma, R., Miklós, D. (eds.) Handbook of Large-scale Random Networks. Springer (2009)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2) (2008)
Shamma, D.A., Kennedy, L., Churchill, E.F.: Statler: Summarizing media through short-messaging services. In: CSW 2010. ACM, USA (2010)
Tang, L., Liu, H.: Graph mining applications to social network analysis. In: Aggarwal, C., Wang, X. (eds.) Managing and Mining Graph Data. Springer (2010)
Tromp, E., Pechenizkiy, M.: Senticorr: Multilingual sentiment analysis of personal correspondence. In: Proc. of ICDM 2011 Workshops. IEEE Press (2011)
Xu, K., Li, J., Liao, S.S.: Sentiment community detection in social networks. In: Procs. of the 2011 iConference, iConf. 2011, pp. 804–805. ACM, NY (2011)
Zygmunt, A., Bródka, P., Kazienko, P., Koźlak, J.: Different approaches to groups and key person identification in blogosphere. In: International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, Kaohsiung, Taiwan, July 25-27, pp. 593–598. IEEE Computer Society (2011)
Zygmunt, A., Bródka, P., Kazienko, P., Koźlak, J.: Key person analysis in social communities within the blogosphere. J. UCS 18(4), 577–597 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gliwa, B., Koźlak, J., Zygmunt, A., Cetnarowicz, K. (2012). Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds) Social Informatics. SocInfo 2012. Lecture Notes in Computer Science, vol 7710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35386-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-35386-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35385-7
Online ISBN: 978-3-642-35386-4
eBook Packages: Computer ScienceComputer Science (R0)