Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Social–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks

  • Mauro ColettoEmail author
  • Claudio Lucchese
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_110182-1



Computer science (CS)

Discipline based on a scientific and practical approach to computation and its applications.

Computational social science (CSS)

New discipline based on interdisciplinary investigation of the social universe on many scales, ranging from individual actors to the largest groupings, through the medium of computation (Cioffi-Revilla 2014).

Dunbar number

Value of the cognitive limit to the number of people with whom a person can maintain stable social relationships (150).

Echo chamber

“Enclosed” system in which information, ideas, or beliefs are amplified or reinforced by internal transmission and repetition.

Ego network

Focal node (“ego”) and the nodes to which the ego is directly connected (friends or alters) plus the ties, if any, among the alters.

Group or community

Set of...

This is a preview of subscription content, log in to check access.


  1. Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on link discovery. ACM, New York, NY, USA, pp 36–43Google Scholar
  2. Aiello LM (2015) Group types in social media. In: Paliouras G, Papadopoulos S, Vogiatzis D, Kompatsiaris Y (eds) User community discovery, Human-computer interaction series. Springer International Publishing, Switzerland, pp 97–134Google Scholar
  3. Aiello LM, Barrat A, Schifanella R, Cattuto C, Markines B, Menczer F (2012) Friendship prediction and homophily in social media. ACM Trans Web (TWEB) 6(2):9Google Scholar
  4. Allan J (2012) Topic detection and tracking: event-based information organization, vol 12. Springer Science & Business MediaGoogle Scholar
  5. Arnaboldi V, Conti M, Passarella A, Pezzoni F (2012) Analysis of ego network structure in online social networks. In: Privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international conference on social computing (SocialCom), pp 31–40. IEEEGoogle Scholar
  6. 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 first ACM conference on online social networks, COSN ’13. ACM, New York, pp 15–26Google Scholar
  7. Attwood F (2005) What do people do with porn? Qualitative research into the comsumption, use, and experience of pornography and other sexually explicit media. Sex Cult 9(2)Google Scholar
  8. Backstrom L, Huttenlocher D, Kleinberg J, 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. ACM, Ithaca, NY, pp 44–54Google Scholar
  9. Backstrom L, Boldi P, Rosa M, Ugander J, Vigna S (2012) Four degrees of separation. In: Proceedings of the 3rd annual ACM web science conference. ACM, Ithaca, NY, pp 33–42Google Scholar
  10. Barbieri N, Bonchi F, Manco G (2013) Cascade-based community detection. In: WSDM. ACM, New York, NY, USAGoogle Scholar
  11. Barrett L, Henzi P, Rendall D (2007) Social brains, simple minds: does social complexity really require cognitive complexity? Philos Trans R Soc Lond B: Biol Sci 362(1480):561–575CrossRefGoogle Scholar
  12. Bessi A, Coletto M, Davidescu GA, Scala A, Caldarelli G, Quattrociocchi W (2015) Science vs conspiracy: collective narratives in the age of misinformation. PLoS One 10(2)Google Scholar
  13. Bisgin H, Agarwal N, Xu X (2010) Investigating homophily in online social networks. In: Web intelligence and intelligent agent technology (WI-IAT), 2010 IEEE/WIC/ACM international conference on, vol 1. IEEE, Toronto, ON, Canada, pp 533–536Google Scholar
  14. Blei DM, Lafferty JD (2007) A correlated topic model of science. Ann Appl Stat:17–35Google Scholar
  15. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  16. Buettner, R. (2016) Getting a job via career-oriented social networking sites: the weakness of ties. In: 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, Koloa, HI, USA, pp 2156–2165Google Scholar
  17. Buyukkokten O, Adar E, Adamic L (2005) A social network caught in the web. First Monday 8(6):15–40Google Scholar
  18. Cioffi-Revilla C (2010) Computational social science. Wiley Interdiscip Rev: Comput Stat 2(3):259–271CrossRefGoogle Scholar
  19. Cioffi-Revilla C (2014) Introduction to computational social science: principles and applications. Berlin/New York: Springer 10 (2014): 978–1Google Scholar
  20. Clinard M, Meier R (2015) Sociology of deviant behavior. Cengage Learning, WadsworthGoogle Scholar
  21. Coletto M, Lucchese C, Orlando S, Perego R (2015) Electoral predictions with Twitter: a machine-learning approach. IIRGoogle Scholar
  22. Coletto M, Aiello LM, Lucchese C, Silvestri F (2016a) On the behaviour of deviant communities in online social networks. In: Tenth international AAAI conference on web and social media (ICWSM), pp 72–81Google Scholar
  23. Coletto M, Lucchese C, Muntean CI, Nardini FM, Esuli A., Renso C, Perego R (2016b) Sentiment-enhanced multidimensional analysis of online social networks: perception of the Mediterranean refugees crisis. In: Advances in social networks analysis and mining (ASONAM), 2016 IEEE/ACM international conference on, pp 1270–1277. IEEEGoogle Scholar
  24. Coletto M, Lucchese C, Orlando S, Perego R (2016c) Polarized user and topic tracking in Twitter. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval. ACM, Pisa, ITALY, pp 945–948Google Scholar
  25. Coletto M, Esuli A, Lucchese C, Muntean CI, Nardini FM, Perego R, Renso C (2017a) Perception of social phenomena through the multidimensional analysis of online social networks. Online Soc Netw Media 1:14–32CrossRefGoogle Scholar
  26. Coletto M, Garimella K, Gionis A, Lucchese C (2017b) A motif-based approach for identifying controversy. In: Proceedings of the eleventh international conference on web and social media, ICWSM 2017, Montréal, 15–18 May 2017, pp 496–499. https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15653
  27. Conover M, Ratkiewicz J, Francisco M, Gonçalves B, Menczer F, Flammini A (2011) Political polarization on Twitter. In: ICWSM, vol 133, pp 89–96Google Scholar
  28. Conte R, Gilbert N, Bonelli G, Cioffi-Revilla C, Deffuant G, Kertesz J, Loreto V, Moat S, Nadal JP, Sanchez A et al (2012) Manifesto of computational social science. Eur Phys J Spec Top 214(1):325–346CrossRefGoogle Scholar
  29. Cristianini N (2014) On the current paradigm in artificial intelligence. AI Commun 27(1):37–43MathSciNetGoogle Scholar
  30. De Choudhury M (2015) Anorexia on Tumblr: a characterization study. In: Florence, Italy, Digital health. ACMGoogle Scholar
  31. de Sola Pool I, Kochen M (1979) Contacts and influence. Soc Netw 1(1):5–51CrossRefMathSciNetGoogle Scholar
  32. Dori-Hacohen S (2015) Controversy detection and stance analysis. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, New York, NY, pp 1057–1057Google Scholar
  33. Dori-Hacohen S, Allan J (2013) Detecting controversy on the web. In: Proceedings of the 22nd ACM international conference on information & knowledge management. ACM, New York, NY, pp 1845–1848Google Scholar
  34. Dunbar RI (1992) Neocortex size as a constraint on group size in primates. J Hum Evol 22(6):469–493CrossRefGoogle Scholar
  35. Dunbar RI (1993) Coevolution of neocortical size, group size and language in humans. Behav Brain Sci 16(04):681–694CrossRefGoogle Scholar
  36. Esuli A, Fagni T, Sebastiani F (2008) Boosting multi-label hierarchical text categorization. Inf Retr 11(4):287–313CrossRefGoogle Scholar
  37. Garimella K, De Francisci Morales G, Gionis A, Mathioudakis M (2016) Quantifying controversy in social media. In: Proceedings of the ninth ACM international conference on web search and data mining (WSDM). ACM, pp 33–42Google Scholar
  38. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826CrossRefzbMATHMathSciNetGoogle Scholar
  39. Gonçalves B, Perra N, Vespignani A (2011) Modeling users’ activity on Twitter networks: validation of Dunbar’s number. PLoS One 6(8):e22,656CrossRefGoogle Scholar
  40. Granovetter MS (1973) The strength of weak ties. Am J Sociol:1360–1380Google Scholar
  41. Haas SM, Irr ME, Jennings NA, Wagner LM (2010) Online negative enabling support groups. New Media SocGoogle Scholar
  42. Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41(3):260–271CrossRefGoogle Scholar
  43. Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the fourth ACM international conference on web search and data mining. ACM, New York, NY, pp 815–824Google Scholar
  44. Kuhn TS (1962) The structure of scientific revolutions. University of Chicago PressGoogle Scholar
  45. Kulshrestha J, Kooti F, Nikravesh A, Gummadi KP (2012) Geographic dissection of the Twitter network. In: Proceedings of the sixth international AAAI conference on weblogs and social media (ICWSM)Google Scholar
  46. Kumar R, Liben-Nowell D, Novak J, Raghavan P, Tomkins A (2005) Theoretical analysis of geographic routing in social networks. CSAIL Technical Reports, MIT Massachusetts, USAGoogle Scholar
  47. Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Link mining: models, algorithms, and applications. Springer, New York, pp 337–357CrossRefGoogle Scholar
  48. 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. ACM, New York, NY, USA, pp 591–600Google Scholar
  49. Leenders, R.: Longitudinal behavior of network structure and actor attributes: modeling interdependence of contagion and selection. Evolution of social networks 1 (1997). Evolution of social networks, 1997, 1: 165–184.Google Scholar
  50. Leskovec J, Horvitz E (2008) Planetary-scale views on a large instant-messaging network. In: Proceedings of the 17th international conference on World Wide Web. ACM, New York, NY, USA, pp 915–924Google Scholar
  51. Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5CrossRefGoogle Scholar
  52. Lin C, He Y (2009) Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management. ACM, New York, NY, USA, pp 375–384Google Scholar
  53. Lu H, Caverlee J, Niu W (2015) Biaswatch: a lightweight system for discovering and tracking topic-sensitive opinion bias in social media. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, New York, NY, USA, pp 213–222Google Scholar
  54. Ludford PJ, Cosley D, Frankowski D, Terveen L (2004) Think different: increasing online community participation using uniqueness and group dissimilarity. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, NY, USA, pp 631–638Google Scholar
  55. Magno G, Comarela G, Saez-Trumper D, Cha M, Almeida V (2012) New kid on the block: exploring the Google+ social graph. In: Proceedings of the 2012 ACM conference on internet measurement conference. ACM, New York, NY, USA, pp 159–170Google Scholar
  56. Makazhanov A, Rafiei D, Waqar M (2014) Predicting political preference of Twitter users. Soc Netw Anal Min 4(1):1–15CrossRefGoogle Scholar
  57. Marcheggiani D, Täckström O, Esuli A, Sebastiani F (2014) Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In: Advances in information retrieval. Springer, pp 273–285Google Scholar
  58. Marsden PV, Campbell KE (1984) Measuring tie strength. Soc Forces 63(2):482–501CrossRefGoogle Scholar
  59. Martin-Borregon D, Aiello LM, Grabowicz P, Jaimes A, Baeza-Yates R (2014) Characterization of online groups along space, time, and social dimensions. EPJ Data Sci 3(1):8CrossRefGoogle Scholar
  60. Maslow AH (1943) A theory of human motivation. Psychol Rev 50(4):370CrossRefGoogle Scholar
  61. McAuliffe JD, Blei DM (2008) Supervised topic models. In: Advances in neural information processing systems, pp 121–128Google Scholar
  62. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27:415–444CrossRefGoogle Scholar
  63. Mei Q, Ling X, Wondra M, Su H, Zhai C (2007) Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th international conference on World Wide Web. ACM, New York, NY, USA, pp 171–180Google Scholar
  64. Messinger PR, Stroulia E, Lyons K (2008) A typology of virtual worlds: historical overview and future directions. J Virtual Worlds Res 1(1)Google Scholar
  65. Milgram S (1967) The small world problem. Psychol Today 2(1):60–67Google Scholar
  66. Miritello G (2013a) Information spreading on communication networks. In: Temporal patterns of communication in social networks. Springer, Switzerland, pp 107–130Google Scholar
  67. Miritello G (2013b) Temporal patterns of communication in social networks. SpringerGoogle Scholar
  68. Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102CrossRefGoogle Scholar
  69. Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement. ACM, New York, NY, USA, pp 29–42Google Scholar
  70. Morgan EM, Snelson C, Elison-Bowers P (2010) Image and video disclosure of substance use on social media websites. Comput Hum Behav 26(6):1405–1411CrossRefGoogle Scholar
  71. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  72. Oh HJ, Ozkaya E, LaRose R (2014) How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Comput Hum Behav 30:69–78CrossRefGoogle Scholar
  73. Prentice DA, Miller DT, Lightdale JR (1994) Asymmetries in attachments to groups and to their members: distinguishing between common-identity and common-bond groups. Key Readings Soc Psychol 20(5):484–493Google Scholar
  74. Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2010) Detecting and tracking the spread of astroturf memes in microblog streams, Palo Alto, California. arXiv preprint arXiv:1011.3768Google Scholar
  75. Sabina C, Wolak J, Finkelhor D (2008) The nature and dynamics of internet pornography exposure for youth. CyberPshychol Behav 11(6)Google Scholar
  76. Scellato S, Mascolo C, Musolesi M, Latora V (2010) Distance matters: geo-social metrics for online social networks. In: Conference on online social networks, WOSN’10Google Scholar
  77. Tajfel H (1982) Social psychology of intergroup relations. Annu Rev Psychol 33(1):1–39CrossRefGoogle Scholar
  78. Takhteyev Y, Gruzd A, Wellman B (2012) Geography of Twitter networks. Soc Netw 34(1):73–81CrossRefGoogle Scholar
  79. Tatemura J (2000) Virtual reviewers for collaborative exploration of movie reviews. In: Proceedings of the 5th international conference on intelligent user interfaces. ACM, New York, NY, USA, pp 272–275Google Scholar
  80. Titov I, McDonald RT (2008) A joint model of text and aspect ratings for sentiment summarization. In: ACL, vol 8. Citeseer, pp 308–316Google Scholar
  81. Turner JC (1981) Towards a cognitive redefinition of the social group. Cahiers de Psychologie Cognitive/Current Psychol Cognition, pp 15–40Google Scholar
  82. Tyson G, Elkhatib Y, Sastry N, Uhlig S (2015) Are people really social in porn 2.0? In: ICWSM. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10511
  83. Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the Facebook social graph. arXiv preprint arXiv:1111.4503Google Scholar
  84. Van Gysel C, Goethals B, de Rijke M (2015) Determining the presence of political parties in social circles. In: ICWSM, pp 690–693Google Scholar
  85. Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM workshop on online social networks. ACM, New York, NY, USA, pp 37–42Google Scholar
  86. Walls F, Jin H, Sista S, Schwartz R (1999) Topic detection in broadcast news. In: Proceedings of the DARPA broadcast news workshop, Morgan Kaufmann Publishers, Inc., pp 193–198Google Scholar
  87. Wang Y, Bai H, Stanton M, Chen WY, Chang EY (2009) Plda: Parallel latent Dirichlet allocation for large-scale applications. In: Algorithmic aspects in information and management. Springer, pp 301–314Google Scholar
  88. Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  89. Wilson C, Sala A, Puttaswamy KP, Zhao BY (2012) Beyond social graphs: user interactions in online social networks and their implications. ACM Trans Web (TWEB) 6(4):17Google Scholar
  90. Zagheni E, Garimella VRK, Weber I, State B (2014) Inferring international and internal migration patterns from Twitter data. In: WWW conference, WWW’14 Companion, April 7–11, 2014, Seoul, Korea.Google Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.IMT School for Advanced StudiesCa’ Foscari University of VenicePisaItaly
  2. 2.Ca’ Foscari University of VenicePisaItaly

Section editors and affiliations

  • Fabrizio Silvestri
    • 1
  • Andrea Tagarelli
    • 2
  1. 1.Yahoo IncLondonUK
  2. 2.University of CalabriaArcavacata di RendeItaly