Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3395–3407 | Cite as

Influential users in Twitter: detection and evolution analysis

  • Giambattista Amati
  • Simone Angelini
  • Giorgio Gambosi
  • Gianluca Rossi
  • Paola VoccaEmail author
Article
  • 14 Downloads

Abstract

In this paper, we study how to detect the most influential users in the microblogging social network platform Twitter and their evolution over time. To this aim, we consider the Dynamic Retweet Graph (DRG) proposed in Amati et al. (2016) and partially analyzed in Amati et al. (IADIS Int J Comput Sci Inform Syst, 11(2) 2016), Amati et al. (2016). The model of the evolution of the Twitter social network is based here on the retweet relationship. In a DRGs, the last time a tweet has been retweeted we delete all the edges representing this tweet. In this way we model the decay of tweet life in the social platform. To detect the influential users, we consider the central nodes in the network with respect to the following centrality measures: degree, closeness, betweenness and PageRank-centrality. These measures have been widely studied in the static case and we analyze them on the sequence of DRG temporal graphs with special regard to the distribution of the \(75\%\) most central nodes. We derive the following results: (a) in all cases, applying the closeness measure results into many nodes with high centrality, so it is useless to detect influential users; (b) for all other measures, almost all nodes have null or very low centrality and (c) the number of vertices with significant centrality are often the same; (d) the above observations hold also for the cumulative retweet graph and, (e) central nodes in the sequence of DRG temporal graphs have high centrality in cumulative graph.

Keywords

Graph analysis Social media Twitter graph Retweet graph Graph dynamics Centrality 

Notes

References

  1. 1.
    Amati G, Angelini S, Bianchi M, Costantini L, Marcone G (2014) A scalable approach to near real-time sentiment analysis on social networks. In: CEUR-WS.org, editor DART 2014 Information Filtering and Retrieval, Proceedings of the 8th international workshop on information filtering and retrieval co-located with XIII AI*IA symposium on artificial intelligence (AI*IA 2014), vol 1314, pp 12–23Google Scholar
  2. 2.
    Amati G, Angelini S, Capri F, Gambosi G, Rossi G, Vocca P (2016) Modelling the temporal evolution of the retweet graph. IADIS Int J Comput Sci Inform Syst, 11(2):19–30Google Scholar
  3. 3.
    Amati G, Angelini S, Capri F, Gambosi G, Rossi G, Vocca P (2016) Twitter temporal evolution analysis: comparing event and topic driven retweet graphs. In: BIGDACI 2016 - Proceedings of the international conference on big data analytics, data mining and computational intelligence, Volume 1, Funchal, Madeira, Portugal, July 2–4, 2016Google Scholar
  4. 4.
    Amati G, Angelini S, Capri F, Gambosi G, Rossi G, Vocca P (2017) On the retweet decay of the evolutionary retweet graph. In: Smart objects and technologies for social good: second international conference, GOODTECHS 2016, Venice, Italy, November 30 – December 1, 2016, Proceedings. Springer International Publishing, Cham, pp 243–253Google Scholar
  5. 5.
    Bavelas A (1950) Communication patterns in task-oriented groups. J Acoustic Soc America 22(6):725–730CrossRefGoogle Scholar
  6. 6.
    Bhattacharya D, Ram S (2012) Sharing news articles using 140 characters: a diffusion analysis on twitter, pp 966–971Google Scholar
  7. 7.
    Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92 (5):1170–1182CrossRefGoogle Scholar
  8. 8.
    Borgatti. SP (2005) Centrality and network flow. Soc Netw 27(1):55–71MathSciNetCrossRefGoogle Scholar
  9. 9.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1-7):107–117CrossRefGoogle Scholar
  10. 10.
    Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter the million follower fallacy. Icwsm 10(10-17):30Google Scholar
  11. 11.
    Conti M, De Salve A, Guidi B, Ricci L (2014) Epidemic diffusion of social updates in Dunbar-Based DOSN. In: Proceedings of parallel processing workshops: Euro-Par 2014 international workshops, pp 311–322Google Scholar
  12. 12.
    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry:35–41Google Scholar
  13. 13.
    Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239CrossRefGoogle Scholar
  14. 14.
    Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. SIGMOD Rec 42(2):17–28CrossRefGoogle Scholar
  15. 15.
    Hage P, Harary F (1995) Eccentricity and centrality in networks. Soc Netw 17(1):57–63CrossRefGoogle Scholar
  16. 16.
    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, WWW ’10. ACM, New York, pp 591–600Google Scholar
  17. 17.
    Myers SA, Sharma A, Gupta P, Lin J (2014) Information network or social network?: the structure of the twitter follow graph. In: Proceedings of the 23rd international conference on world wide web, WWW ’14 companion. ACM, New York, pp 493–498Google Scholar
  18. 18.
    Nieminen J (1974) On centrality in a graph. Scand J Psychol 15:322–336CrossRefGoogle Scholar
  19. 19.
    Shimbel A (1953) Structural parameters of communication networks. Bullet Math Biophys 15(4):501–507MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fondazione Ugo BordoniRomeItaly
  2. 2.University of Rome “Tor Vergata”RomeItaly
  3. 3.University of TusciaViterboItaly

Personalised recommendations