Skip to main content Accessibility help

Existence of outsiders as a characteristic of online communication networks


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.

Hide All
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.
Alderson, D., & Li, L. (2007). Diversity of graphs with highly variable connectivity. Physical Review E, 75 (4), 046102.
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.
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.
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), 388397.
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.
Burt, R. S. (1995). Structural holes: The social structure of competition. Cambridge: Harvard University Press.
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.
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.
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.
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. Nature Physics, 2 (2), 110115.
Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016) The rise of social bots. Communications of the ACM, 59 (7), 96104.
Freeman, L. C. (1977). A set of measures of centrality based upon betweenness. Sociometry, 40, 3541.
Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333 (6051), 18781881.
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.
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.
Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users' activity on Twitter networks: Validation of Dunbar's number. PLOS ONE, 6 (8), e22656.
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.
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78 (6), 13601380.
Hu, H. B., & Wang, X. F. (2009). Disassortative mixing in online social networks. EPL, 86 (1), 18003.
Huss, M., & Holme, P. (2007). Currency and commodity metabolites: Their identification and relation to the modularity of metabolic networks. LET Systems Biology, 1 (5), 280285.
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.
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. 217226.
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.
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.
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 1 (1), 2.
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.
Menche, J., Valleriani, A., & Lipowsky, R. (2010). Asymptotic properties of degree-correlated scale-free networks. Physical Review E, 81 (4), 046103.
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.
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.
Molloy, M., & Reed, B. (1995). A critical point for random graphs with a given degree sequence. Random Structures and Algorithms, 6 (1995), 161179.
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.
Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89 (20), 208701.
Newman, M. E. J. (2003). Mixing patterns in networks. Physical Review E, 67 (2), 026126.
Newman, M. E. J., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68 (3), 036122.
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), 73327336.
Pastor-Satorras, R., Vázquez, A., & Vespignani, A. (2001). Dynamical and correlation properties of the Internet. Physical Review Letters, 87 (25), 258701.
Saito, K., & Masuda, N. (2014). Two types of well followed users in the followership networks of Twitter. PLOS ONE, 9 (1), e84265.
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.
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.
Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., & Aihara, K. (2013). Quantifying collective attention from tweet stream. PLOS ONE, 8 (4), e61823.
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. 3565). Singapore: World Scientific, Chap. 3.
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.
Szell, M., Grauwin, S., & Ratti, C. (2014). Contraction of online response to major events. PLOS ONE, 9 (2), e89052.
Takaguchi, T., Nakamura, M., Sato, N., Yano, K., & Masuda, N. (2011). Predictability of conversation partners. Physical Review X, 1 (1), 011008.
Takhteyev, Y., Gruzd, A., & Wellman, B. (2012). Geography of Twitter networks. Social Networks, 34 (1), 7381.
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.
Vázquez, A., Pastor-Satorras, R., & Vespignani, A. (2002). Large-scale topological and dynamical properties of the Internet. Physical Review E, 65 (6), 066130.
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.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393 (6684), 440442.
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.
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.
Zhou, S., & Mondragón, R. J. (2004). The rich-club phenomenon in the Internet topology. IEEE Communications Letters, 8 (3), 180182.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Network Science
  • ISSN: 2050-1242
  • EISSN: 2050-1250
  • URL: /core/journals/network-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Type Description Title
Supplementary materials

Takaguchi et al. supplementary material
Takaguchi et al. supplementary material

 PDF (682 KB)
682 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed