More Web Proxy on the site http://driver.im/

research-article

Group Profiling for Understanding Social Structures

Authors:

Huan LiuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 1

Article No.: 15, Pages 1 - 25

https://doi.org/10.1145/2036264.2036279

Published: 01 October 2011 Publication History

Abstract

The prolific use of participatory Web and social networking sites is reshaping the ways in which people interact with one another. It has become a vital part of human social life in both the developed and developing world. People sharing certain similarities or affiliates tend to form communities within social media. At the same time, they participate in various online activities: content sharing, tagging, posting status updates, etc. These diverse activities leave behind traces of their social life, providing clues to understand changing social structures. A large body of existing work focuses on extracting cohesive groups based on network topology. But little attention is paid to understanding the changing social structures. In order to help explain the formation of a group, we explore different group-profiling strategies to construct descriptions of a group. This research can assist network navigation, visualization, and analysis, as well as monitoring and tracking the ebbs and tides of different groups in evolving networks. By exploiting information collected from real-world social media sites, extensive experiments are conducted to evaluate group-profiling results. The pros and cons of different group-profiling strategies are analyzed with concrete examples. We also show some potential applications based on group profiling. Interesting findings with discussions are reported.

References

[1]

Agarwal, N., Liu, H., Tang, L., and Yu, P. S. 2008. Identifying the influential bloggers in a community. In Proceedings of the International Conference on Web Search and Web Data Mining (WSDM’08). ACM, New York, 207--218.

Digital Library

[2]

Airodi, E., Blei, D., Fienberg, S., and Xing, E. P. 2008. Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981--2014.

Digital Library

[3]

Almack, J. 1922. The influence of intelligence on the selection of associates. School Soc. 16, 529--530.

[4]

Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X. 2006. Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06). ACM, New York, 44--54.

Digital Library

[5]

Baumes, J., Goldberg, M., Magdon-Ismail, M., and Wallace, W. 2004. Discovering hidden groups in communication networks. In Proceedings of the 2nd NSF/NIJ Symposium on Intelligence and Security Informatics.

[6]

Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022.

[7]

Bott, H. 1928. Observation of play activities in a nursery school. Genetic Psychol. Monographs 4, 44--88.

[8]

Chakrabarti, D. and Faloutsos, C. 2006. Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38, 1, 2.

Digital Library

[9]

Chang, J., Boyd-Graber, J., and Blei, D. M. 2009. Connections between the lines: Augmenting social networks with text. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). ACM, New York, 169--178.

Digital Library

[10]

Chen, W.-Y., Chu, J.-C., Luan, J., Bai, H., Wang, Y., and Chang, E. Y. 2009. Collaborative filtering for orkut communities: Discovery of user latent behavior. In Proceedings of the 18th International Conference on World Wide Web (WWW’09). ACM, New York, 681--690.

Digital Library

[11]

Dunning, T. 1993. Accurate methods for the statistics of surprise and coincidence. Comput. Linguist. 19, 1, 61--74.

Digital Library

[12]

Erosheva, E., Fienberg, S., and Lafferty, J. 2004. Mixed-Membership models of scientific publications. Proc. Nat. Acad. Sci. 101, 90001, 5220--5227.

[13]

Fiore, A. T. and Donath, J. S. 2005. Homophily in online dating: When do you like someone like yourself? In CHI’05 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, 1371--1374.

Digital Library

[14]

Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289--1305.

[15]

Guyon, I. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157--1182.

Digital Library

[16]

Hechter, M. 1988. Principles of Group Solidarity. University of California Press.

[17]

Hopcroft, J., Khan, O., Kulis, B., and Selman, B. 2004. Tracking evolving communities in large linked networks. Proc. Nat. Acad. Sci. U.S.A. 101, 1, 5249--5253.

[18]

Jaccard, P. 1901. Tude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Socit Vaudoise des Sciences Naturelles 37, 547--579.

[19]

Jones, K. S. 1972. A statistical interpretation of term specificity and its application in retrieval. J. Document. 28, 1, 1121.

[20]

Kamath, K. and Caverlee, J. 2011. Transient crowd discovery on the real-time social web. In Proceedings of the ACM Intenational Conference on Web Search and Data Mining (WSDM).

Digital Library

[21]

Kelly, J. and Etling, B. 2008. Mapping Iran’s Online Public: Politics and Culture in the Persian Blogosphere. Berkman Center for Internet and Society at Harvard University.

[22]

Kendall, M. 1938. A new measure of rank correlation. Biometrika 30, 81--89.

[23]

Kumar, R., Novak, J., and Tomkins, A. 2006. Structure and evolution of online social networks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06). ACM, New York, 611--617.

Digital Library

[24]

Lauw, H. W., Shafer, J. C., Agrawal, R., and Ntoulas, A. 2010. Homophily in the digital world: A livejournal case study. IEEE Internet Comput. 14, 15--23.

Digital Library

[25]

Leskovec, J., Kleinberg, J., and Faloutsos, C. 2007. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1, 1, 2.

Digital Library

[26]

Leskovec, J., Backstrom, L., Kumar, R., and Tomkins, A. 2008a. Microscopic evolution of social networks. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). ACM, New York, 462--470.

Digital Library

[27]

Leskovec, J., Lang, K. J., Dasgupta, A., and Mahoney, M. W. 2008b. Statistical properties of community structure in large social and information networks. In Proceeding of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, 695--704.

Digital Library

[28]

Liu, H. and Motoda, H. 1998. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers.

Digital Library

[29]

Liu, Y., Niculescu-Mizil, A., and Gryc, W. 2009. Topic-Link lda: Joint modeling of topic and community for blog analysis. In Proceedins of the 26th International Conference on Machine Learning.

Digital Library

[30]

Macdonald, C., Santos, R. L., Ounis, I., and Soboroff, I. 2010. Blog track research at trec. SIGIR Forum 44, 1, 57--74.

Digital Library

[31]

McPherson, M., Smith-Lovin, L., and Cook, J. M. 2001. Birds of a feather: Homophily in social networks. Ann. Rev. Sociol. 27, 415--444.

[32]

Mei, Q., Cai, D., Zhang, D., and Zhai, C. 2008. Topic modeling with network regularization. In Proceeding of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, 101--110.

Digital Library

[33]

Nallapati, R. M., Ahmed, A., Xing, E. P., and Cohen, W. W. 2008. Joint latent topic models for text and citations. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). ACM, New York, 542--550.

Digital Library

[34]

Newman, M. 2003. The structure and function of complex networks. SIAM Review 45, 167--256.

Digital Library

[35]

Palla, G., Barabasi, A.-L., and Vicsek, T. 2007. Quantifying social group evolution. Nature 446, 7136, 664--667.

[36]

Roy, D. M., Kemp, C., Mansinghka, V. K., and Tenenbaum, J. B. 2006. Learning annotated hierarchies from relational data. In Proceedings of the Conference on Naval Information Processing System (NIPS). 1185--1192.

[37]

Shi, X., Chang, K., Narayanan, V. K., Josifovski, V., and Smola, A. J. 2010. A compression framework for generating user profiles. In Proceedings of the ACM SIGIR Workshop on Feature Generation and Selection for Information Retrieval.

[38]

Shmueli-Scheuer, M., Roitman, H., Carmel, D., Mass, Y., and Konopnicki, D. 2010. Extracting user profiles from large scale data. In Proceedings of the Workshop on Massive Data Analytics on the Cloud (MDAC’10). ACM, New York, 4:1--4:6.

Digital Library

[39]

Sun, J., Faloutsos, C., Papadimitriou, S., and Yu, P. S. 2007. Graphscope: Parameter-free mining of large time-evolving graphs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07). ACM, New York, 687--696.

Digital Library

[40]

Tang, L. and Liu, H. 2005. Bias analysis in text classification for highly skewed data. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05). IEEE Computer Society, 781--784.

Digital Library

[41]

Tang, L. and Liu, H. 2009. Scalable learning of collective behavior based on sparse social dimensions. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM’09). 1107--1116.

Digital Library

[42]

Tang, L. and Liu, H. 2010. Toward predicting collective behavior via social dimension extraction. IEEE Intell. Syst. 25, 19--25.

Digital Library

[43]

Tang, L., Liu, H., Zhang, J., Agarwal, N., and Salerno, J. J. 2008. Topic taxonomy adaptation for group profiling. ACM Trans. Knowl. Discov. Data 1, 4, 1--28.

Digital Library

[44]

Tantipathananandh, C., Berger-Wolf, T., and Kempe, D. 2007. A framework for community identification in dynamic social networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, NY, 717--726.

Digital Library

[45]

Thelwall, M. 2009. Homophily in myspace. J. Amer. Soc. Inf. Sci. Technol. 60, 2, 219--231.

Digital Library

[46]

Wang, X., Tang, L., Gao, H., and Liu, H. 2010a. Discovering overlapping groups in social media. In Proceedings of the 10th IEEE International Conference on Data Mining.

Digital Library

[47]

Wang, Y., Cong, G., Song, G., and Xie, K. 2010b. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10). ACM, New York, 1039--1048.

Digital Library

[48]

Wasserman, S. and Faust, K. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press.

[49]

Watts, D. and Dodds, P. 2007. Influentials, networks, and public opinion formation. J. Consum. Res. 34, 4, 441--458.

[50]

Wellman, B. 1926. The school child’s choice of companions. J. Educ. Res. 14, 126--132.

[51]

Yang, Y. and Pedersen, J. O. 1997. A Comparative Study on Feature Selection in Text Categorization. Morgan Kaufmann Publishers, 412--420.

Digital Library

Cited By

Ahn YYan MLin YChung WHwa R(2022)Tribe or Not? Critical Inspection of Group Differences Using TribalGramACM Transactions on Interactive Intelligent Systems10.1145/348450912:1(1-34)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3484509
Lucas TGomes JVimieiro RPrudencio RLudermir T(2019)A Multivariate Method for Group Profiling Using Subgroup Discovery2019 8th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2019.00072(371-376)Online publication date: Oct-2019
https://doi.org/10.1109/BRACIS.2019.00072
Aslam WButt WAnwar M(2018)A Systematic Review on Social Network AnalysisProceedings of the 2018 International Conference on Computing and Big Data10.1145/3277104.3277112(92-97)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1145/3277104.3277112
Show More Cited By

Index Terms

Group Profiling for Understanding Social Structures
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Uses and gratifications of social networking sites for bridging and bonding social capital

Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Social Media Usage, Social Relations, and a Sense of Community in Indonesia

Because of their participatory nature, social media can facilitate and accelerate social relationships, but their functions may vary from one cultural context to another. This article aims to investigate whether social media usage affects social ...
Social Watching a Civic Broadcast: Understanding the Effects of Positive Feedback and Other Users' Opinions
CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

People increasingly turn to social media to augment their broadcast viewing experience with a parallel stream of information and opinion. Known as "social watching," the practice of integrating broadcast media and social media has become routine for ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 1

October 2011

391 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2036264

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2011

Accepted: 01 April 2011

Revised: 01 January 2011

Received: 01 September 2010

Published in TIST Volume 3, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
775
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)5

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahn YYan MLin YChung WHwa R(2022)Tribe or Not? Critical Inspection of Group Differences Using TribalGramACM Transactions on Interactive Intelligent Systems10.1145/348450912:1(1-34)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3484509
Lucas TGomes JVimieiro RPrudencio RLudermir T(2019)A Multivariate Method for Group Profiling Using Subgroup Discovery2019 8th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2019.00072(371-376)Online publication date: Oct-2019
https://doi.org/10.1109/BRACIS.2019.00072
Aslam WButt WAnwar M(2018)A Systematic Review on Social Network AnalysisProceedings of the 2018 International Conference on Computing and Big Data10.1145/3277104.3277112(92-97)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1145/3277104.3277112
Li YGan XFu LTian XQin ZZhou Y(2018)Conformity-Aware Influence Maximization with User Profiles2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)10.1109/WCSP.2018.8555685(1-6)Online publication date: Oct-2018
https://doi.org/10.1109/WCSP.2018.8555685
Aghasaryan ABouzid MKostadinov DPicault J(2018)Group Representation and ProfilingEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_221(979-983)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_221
Aiello L(2018)Nature of Social StructuresEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110180(1435-1450)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110180
Gomes JPrudêncio RNascimento A(2017)Centrality-Based Group Profiling: A Comparative Study in Co-authorship NetworksNew Generation Computing10.1007/s00354-017-0028-936:1(59-89)Online publication date: 21-Nov-2017
https://doi.org/10.1007/s00354-017-0028-9
Aghasaryan ABouzid MKostadinov DPicault J(2017)Group Representation and ProfilingEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_221-1(1-5)Online publication date: 6-Feb-2017
https://doi.org/10.1007/978-1-4614-7163-9_221-1
Aiello L(2017)The Nature of Social StructuresEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110180-1(1-16)Online publication date: 4-Jul-2017
https://doi.org/10.1007/978-1-4614-7163-9_110180-1
Dehghani MAzarbonyad HKamps JMarx MKelly DCapra RBelkin NTeevan JVakkari P(2016)Generalized Group Profiling for Content CustomizationProceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval10.1145/2854946.2855003(245-248)Online publication date: 13-Mar-2016
https://dl.acm.org/doi/10.1145/2854946.2855003
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents