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Network-Based Group Account Classification

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Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9021))

Abstract

We propose a classification method for group vs. individual accounts on Twitter, based solely on communication network characteristics. While such a language-agnostic, network-based approach has been used in the past, this paper motivates the task from firmly established theories of human interactional constraints from cognitive science to sociology. Time, cognitive, and social role constraints limit the extent to which individuals can maintain social ties. These constraints are expressed in observable network metrics at the node (i.e. account) level which we identify and exploit for inferring group accounts.

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References

  1. Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on twitter. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 705–714. ACM, New York (2011)

    Google Scholar 

  2. Sharma, N., Ghosh, S., Benevenuto, F., Ganguly, N., Gummadi, K.P.: Inferring who-is-who in the twitter social network. In: Proceedings of the 2012 ACM Workshop on Online Social Networks, WOSN, pp. 55–60. ACM, New York (2012)

    Google Scholar 

  3. Tavares, G., Faisal, A.: Scaling-laws of human broadcast communication enable distinction between human, corporate and robot twitter users. PLoS One 8(7), e65774 (2013)

    Article  Google Scholar 

  4. De Choudhury, M., Diakopoulos, N., Naaman, M.: Unfolding the event landscape of twitter: Classification and exploration of user categories. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, CSCW, pp. 241-244. ACM, New York (2012)

    Google Scholar 

  5. De Silva, L., Riloff, E.: User type classification of tweets with implications for event recognition. In: Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media, pp. 98–108. Association for Computational Linguistics, Baltimore (2014)

    Google Scholar 

  6. Saramaki, J., Leicht, E.A., Lopez, E., Roberts, S.G.B., Reed-Tsochas, F., Dunbar, R.I.M.: Persistence of social signatures in human communication. PNAS 111(3), 942–947 (2014)

    Article  Google Scholar 

  7. Honeycutt, C., Herring, S.: Beyond microblogging: Conversation and collaboration via twitter. In: Proceedings of the 42nd Hawaii International Conference on System Sciences, HICSS 2009, pp. 1–10. IEEE (2009)

    Google Scholar 

  8. Brewer, D.D.: The social structural basis of the organization of persons in memory. Human Nature 6, 379–403 (1995)

    Article  Google Scholar 

  9. DeScioli, P., Kurzban, R.: The alliance hypothesis for human friendship. PLoS One 4(6), e5802 (2009)

    Article  Google Scholar 

  10. Dunbar, R.I.M.: Neocortex size and group size in primates: A test of the hypothesis. Journal of Human Evolution 28, 287–296 (1995)

    Article  Google Scholar 

  11. Goode, W.J.: A theory of role strain. American Sociological Review 25(4), 483–496 (1960)

    Article  Google Scholar 

  12. Miller, G.: The magical seven plus or minus two: Some limits on our capacity for processing information. Psychological Review 63, 81–97 (1956)

    Article  Google Scholar 

  13. Roberts, S., Dunbar, R.I.M., Pollet, T.V., Kuppens, T.: Exploring variation in active network size: Constraints and ego characteristics. Social Networks 31(2), 138–146 (2009)

    Article  Google Scholar 

  14. Blau, P.: Exchange and Power in Social Life. John Wiley and Sons, New York (1964)

    Google Scholar 

  15. Powell, J., Lewis, P.A., Roberts, N., Garcia-Finana, M., Dunbar, R.I.M.: Orbital prefrontal cortex volume predicts social network size: An imaging study of individual differences in humans. Proceedings of the Royal Society B 279(1736), 2157–2162 (2012)

    Article  Google Scholar 

  16. Zhou, W.X., Sornette, D., Hill, R.A., Dunbar, R.I.M.: Discrete hierarchical organization of social group sizes. Proceedings of the Royal Society B 272(1561), 439–444 (2005)

    Article  Google Scholar 

  17. Weber, M.: Economy and Society. University of California Press (1978)

    Google Scholar 

  18. Sousa, D., Sarmento, L., Rodrigues, E.M.: Characterization of the twitter @replies network: are user ties social or topical? In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, SMUC 2010, pp. 63–70. ACM, New York (2010)

    Google Scholar 

  19. Thomas, K., Grier, C., Paxson, V., Song, D.: Suspended accounts in retrospect: An analysis of twitter spam. Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC 2011, pp. 243–258. ACM, New York (2011)

    Chapter  Google Scholar 

  20. Jurgens, D.: That’s what friends are for: Inferring location in online social media platforms based on social relationships. In: The 7th International AAAI Conference on Weblogs and Social Media, ICWSM 2013, pp. 273–282 (2013)

    Google Scholar 

  21. Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. arXiv (2014) http://arxiv.org/abs/1404.7152

  22. Gouldner, A.W.: The norm of reciprocity: A preliminary statement. American Journal of Sociology 25(2), 161–178 (1960)

    Article  Google Scholar 

  23. Newman, M.E.J.: Assortative Mixing in Networks. Physical Review Letters 89(20), 208701 (2002)

    Article  Google Scholar 

  24. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)

    Google Scholar 

  25. Poblete, B., Garcia, R., Mendoza, M., Jaimes, A.: Do all birds tweet the same? characterizing twitter around the world. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 1025–1030. ACM, New York (2011)

    Google Scholar 

  26. Kulshrestha, J., Kooti, F., Ashkan, N., Krishna, P.G.: Geographic dissection of the twitter network. In: The 6th International AAAI Conference on Weblogs and Social Media, ICWSM 2012, pp. 202–209 (2012)

    Google Scholar 

  27. Quercia, D., Capra, L., Crowcroft, J.: The social world of twitter: topics, geography, and emotions. In: The 6th International AAAI Conference on Weblogs and Social Media, ICWSM 2012, pp. 298–305 (2012)

    Google Scholar 

  28. Hong, L., Convertino, G., Chi, E.H.: Language matters in twitter. In: The 5th International AAAI Conference on Weblogs and Social Media, ICWSM 2011, pp. 518–521 (2011)

    Google Scholar 

  29. van der Maaten, L., Hinton, G.: Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 2579–2605 (2008)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Cornell University, Ithaca, NY, USA

    Patrick S. Park

  2. HRL Laboratories, Malibu, CA, USA

    Patrick S. Park, Ryan F. Compton & Tsai-Ching Lu

Authors
  1. Patrick S. Park
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  2. Ryan F. Compton
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  3. Tsai-Ching Lu
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Corresponding author

Correspondence to Patrick S. Park .

Editor information

Editors and Affiliations

  1. University of Arkansas, Little Rock, Arkansas, USA

    Nitin Agarwal

  2. Technicolor Research, Los Altos, California, USA

    Kevin Xu

  3. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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© 2015 Springer International Publishing Switzerland

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Park, P.S., Compton, R.F., Lu, TC. (2015). Network-Based Group Account Classification. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-16268-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16267-6

  • Online ISBN: 978-3-319-16268-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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