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Existence of outsiders as a characteristic of online communication networks

Published online by Cambridge University Press:  05 November 2018

TARO TAKAGUCHI ,
TAKANORI MAEHARA ,
KEN-ICHI KAWARABAYASHI  and
MASASHI TOYODA
TARO TAKAGUCHI
Affiliation:
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan (e-mail: [email protected], [email protected], [email protected]) JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
TAKANORI MAEHARA
Affiliation:
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan (e-mail: [email protected], [email protected], [email protected]) JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
KEN-ICHI KAWARABAYASHI
Affiliation:
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan (e-mail: [email protected], [email protected], [email protected]) JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
MASASHI TOYODA
Affiliation:
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan (e-mail: [email protected])
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Abstract

Online social networking services involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest social networking services. We define a network of user pairs that send reciprocal messages. Based on the correlation between degree of adjacent nodes observed in this network, we hypothesize that this network differs from conventional understandings in the sense that there is a small number of distinctive users that we call outsiders. Outsiders do not belong to any user groups but they are connected with different groups, while not being well connected with each other. We identify outsiders by maximizing the degree assortativity coefficient of the network via node removal, thereby confirming that local structural properties of outsiders identified are consistent with our hypothesis. Our findings suggest that the existence of outsiders should be considered when using Twitter communication networks for social network analysis.

Keywords

online social networksdata analysisassortative mixing
Type
Research Article
Information
Network Science , Volume 6 , Issue 4 , December 2018 , pp. 431 - 447
Copyright
Copyright © Cambridge University Press 2018 

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References

Ahn, Y.-Y., Han, S., Kwak, H., Moon, S., & Jeong, H. (2007). Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th International Conference on World Wide Web. Banff, Alberta, Canada: ACM, pp. 835–844.Google Scholar
Alderson, D., & Li, L. (2007). Diversity of graphs with highly variable connectivity. Physical Review E, 75 (4), 046102.Google Scholar
Arnaboldi, V., Conti, M., Passarella, A., & Dunbar, R. (2013). Dynamics of personal social relationships in online social networks: A study on twitter. In Proceedings of the 1st ACM Conference on Online Social Networks. Boston, MA: ACM, pp. 15–26.Google Scholar
Bild, D. R., Liu, Y., Dick, R. P., Mao, Z. M. and Wallach, D. S. (2015). Aggregate characterization of user behavior in Twitter and analysis of the retweet graph.ACM Transactions on Internet Technology, 15 (1), Article 4.Google Scholar
Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C. M., & Dodds, P. S. (2012). Twitter reciprocal reply networks exhibit assortativity with respect to happiness. Journal of Computational Science, 3 (5), 388–397.Google Scholar
Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of the 43rd Hawaii International Conference on System Sciences. Honolulu, HI: IEEE, pp. 1–10.Google Scholar
Burt, R. S. (1995). Structural holes: The social structure of competition. Cambridge: Harvard University Press.Google Scholar
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the 14th International Conference on Weblogs and Social Media. Washington, D.C.: AAAI, pp. 10–17.Google Scholar
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2010). Who is tweeting on Twitter: Human, bot, or cyborg? In Proceedings of the 26th Annual Computer Security Applications Conference. Austin, Texas, USA: ACM, pp. 21–30.Google Scholar
Chun, H., Kwak, H., Eom, Y.-H., Ahn, Y.-Y., Moon, S., & Jeong, H. (2008). Comparison of online social relations in volume vs. interaction: A case study of Cyworld. In Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Vouliagmini, Greece: ACM, pp. 57–69.Google Scholar
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2 (2), 110–115.Google Scholar
Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016) The rise of social bots. Communications of the ACM, 59 (7), 96–104.Google Scholar
Freeman, L. C. (1977). A set of measures of centrality based upon betweenness. Sociometry, 40, 35–41.Google Scholar
Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333 (6051), 1878–1881.Google Scholar
Gómez, V., Kaltenbrunner, A., & López, V. (2008). Statistical analysis of the social network and discussion threads in slashdot. In Proceedings of the 17th International Conference on World Wide Web. Beijing, China: ACM, pp. 645–654.Google Scholar
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific Reports, 1 (Jan.), 197.Google Scholar
Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users' activity on Twitter networks: Validation of Dunbar's number. PLOS ONE, 6 (8), e22656.Google Scholar
Grabowicz, P. A., Ramasco, J. J., Moro, E., Pujol, J. M., & Eguiluz, V. M. (2012). Social features of online networks: The strength of intermediary ties in online social media. PLOS ONE, 7 (1), e29358.Google Scholar
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78 (6), 1360–1380.Google Scholar
Hu, H. B., & Wang, X. F. (2009). Disassortative mixing in online social networks. EPL, 86 (1), 18003.Google Scholar
Huss, M., & Holme, P. (2007). Currency and commodity metabolites: Their identification and relation to the modularity of metabolic networks. LET Systems Biology, 1 (5), 280–285.Google Scholar
Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why we Twitter: An analysis of a microblogging community. In Proceedings of the 9th WebKDD and first SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. San Jose, CA: ACM, pp. 56–65.Google Scholar
Klimt, B., & Yang, Y. (2004). The Enron corpus: A new dataset for Email classification research. In Machine Learning: ECML 2004 Lecture Notes in Computer Science Volume 3201. Heidelberg, Germany: Springer Berlin Heidelberg, pp. 217–226.Google Scholar
Kunegis, J. (2013) KONECT: The Koblenz network collection. In Proceedings of the 22nd International Conference on World Wide Web Companion., Rio de Janeiro, Brazil, ACM, pp. 1343–1350.Google Scholar
Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web. Raleigh, NC: ACM, pp. 591–600.Google Scholar
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 1 (1), 2.Google Scholar
Leskovec, J., Huttenlocher, D. P., & Kleinberg, J. M. (2010). Governance in social media: A case study of the Wikipedia promotion process. In Proceedings of the Fourth International Conference on Weblogs and Social Media. Washington, D.C.: AAAl, pp. 98–105.Google Scholar
Menche, J., Valleriani, A., & Lipowsky, R. (2010). Asymptotic properties of degree-correlated scale-free networks. Physical Review E, 81 (4), 046103.Google Scholar
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacherjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. San Diego, CA: ACM, pp. 29–42.Google Scholar
Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A. (2013). The Twitter of Babel: Mapping world languages through microblogging platforms. PLOS ONE, 8 (4), e61981.Google Scholar
Molloy, M., & Reed, B. (1995). A critical point for random graphs with a given degree sequence. Random Structures and Algorithms, 6 (1995), 161–179.Google Scholar
Naaman, M., Boase, J., & Lai, C.-H. (2010). Is it really about me? Message content in social awareness streams. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work. Savannah, Georgia, USA: ACM, pp. 189–192.Google Scholar
Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.Google Scholar
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89 (20), 208701.Google Scholar
Newman, M. E. J. (2003). Mixing patterns in networks. Physical Review E, 67 (2), 026126.Google Scholar
Newman, M. E. J., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68 (3), 036122.Google Scholar
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., & Barabási, A.-L. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences of the United States of America, 104 (18), 7332–7336.Google Scholar
Pastor-Satorras, R., Vázquez, A., & Vespignani, A. (2001). Dynamical and correlation properties of the Internet. Physical Review Letters, 87 (25), 258701.Google Scholar
Saito, K., & Masuda, N. (2014). Two types of well followed users in the followership networks of Twitter. PLOS ONE, 9 (1), e84265.Google Scholar
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web. Raleigh, NC: ACM, pp. 851–860.Google Scholar
Sano, Y., Yamada, K., Watanabe, H., Takayasu, H., & Takayasu, M. (2013). Empirical analysis of collective human behavior for extraordinary events in the blogosphere. Physical Review E, 87 (1), 012805.Google Scholar
Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., & Aihara, K. (2013). Quantifying collective attention from tweet stream. PLOS ONE, 8 (4), e61823.Google Scholar
Serrano, M. A., Boguñá, M., Pastor-Satorras, R., & Vespignani, A. (2007). Correlations in complex networks. In Caldarelli, G., & Vespignani, A. (Eds.), Large scale structure and dynamics of complex networks: From information technology to finance and natural science (pp. 35–65). Singapore: World Scientific, Chap. 3.Google Scholar
Sousa, D., Sarmento, L., & Rodrigues, E. M. (2010). 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. Toronto, Ontario, Canada: ACM, pp. 63–70.Google Scholar
Szell, M., Grauwin, S., & Ratti, C. (2014). Contraction of online response to major events. PLOS ONE, 9 (2), e89052.Google Scholar
Takaguchi, T., Nakamura, M., Sato, N., Yano, K., & Masuda, N. (2011). Predictability of conversation partners. Physical Review X, 1 (1), 011008.Google Scholar
Takhteyev, Y., Gruzd, A., & Wellman, B. (2012). Geography of Twitter networks. Social Networks, 34 (1), 73–81.Google Scholar
Tavares, G., & Faisal, A. (2013). Scaling-laws of human broadcast communication enable distinction between human, corporate and robot Twitter users. PLOS ONE, 8 (7), e65774.Google Scholar
Vázquez, A., Pastor-Satorras, R., & Vespignani, A. (2002). Large-scale topological and dynamical properties of the Internet. Physical Review E, 65 (6), 066130.Google Scholar
Viswanath, B., Mislove, A., Cha, M., & Gummadi, K. P. (2009). On the evolution of user interaction in Facebook. In Proceedings of the 2nd ACM Workshop on Online Social Networks. Barcelona, Spain: ACM, p. 37.Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393 (6684), 440–442.Google Scholar
Whitney, D. E., & Alderson, D. (2008). Are technological and social networks really different? In Minai, A., Braha, D., & Bar-Yam, Y. (Eds.), Unifying themes in complex systems. Heidelberg, Germany: Springer Berlin Heidelberg.Google Scholar
Zhou, Z., Bandari, R., Kong, J., Qian, H., and Roychowdhury, V. (2010). Information resonance on twitter: Watching Iran. In Proceedings of the 1st Workshop on Social Media Analysis. Washington, DC, USA, pp. 123–131.Google Scholar
Zhou, S., & Mondragón, R. J. (2004). The rich-club phenomenon in the Internet topology. IEEE Communications Letters, 8 (3), 180–182.Google Scholar
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