Advertisement

Modeling cascade formation in Twitter amidst mentions and retweets

  • Soumajit Pramanik
  • Qinna Wang
  • Maximilien Danisch
  • Jean-Loup Guillaume
  • Bivas Mitra
Original Article

Abstract

This paper presents an analytical framework for cascade formation considering both retweet and mentioning activities into account. We introduce two mention strategies (a) random mention and (b) smart mention to model the mention preferences of the users. The proposed framework \({\mathcal {C}}^M_F\) analytically computes the cascade size, depicting tweet popularity and discovers the presence of a critical retweet rate, under which mentioning in a tweet significantly helps in cascade formation. We validate the proposed framework with the help of Monte Carlo simulation; we demonstrate the generality of the framework taking both empirical and synthetic follower networks into consideration. This framework proves the elegance of smart mention strategy in boosting tweet popularity, specially in the low retweeting environment.

Keywords

Multiplex network Epidemic models Information diffusion Twitter 

Notes

Acknowledgements

This work has been partially supported by the SAP Labs India Doctoral Fellowship program, DST—CNRS funded Indo—French collaborative project ‘Evolving Communities and Information Spreading’ and French National Research Agency contract CODDDE ANR-13-CORD-0017-01.

References

  1. Abdullah S, Wu X (2011) An epidemic model for news spreading on twitter. In: 2011 IEEE 23rd international conference on tools with artificial intelligence, pp 163–169. doi: 10.1109/ICTAI.2011.33
  2. Arenas A, Borge-Holthoefer J, Meloni S, Moreno Y et al (2010) Discrete-time markov chain approach to contact-based disease spreading in complex networks. EPL (Europhys Lett) 89(3):38,009CrossRefGoogle Scholar
  3. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 65–74Google Scholar
  4. Bao P, Shen HW, Jin X, Cheng XQ (2015) Modeling and predicting popularity dynamics of microblogs using self-excited hawkes processes. In: Proceedings of the 24th international conference on world wide web, WWW ’15 Companion. ACM, New York, pp 9–10. doi: 10.1145/2740908.2742744
  5. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefzbMATHGoogle Scholar
  6. Barabâsi AL, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Phys A Stat Mech Appl 311(3):590–614MathSciNetCrossRefzbMATHGoogle Scholar
  7. Boccaletti S, Bianconi G, Criado R, del Genio C, Gmez-Gardees J, Romance M, Sendia-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122. doi: 10.1016/j.physrep.2014.07.001 MathSciNetCrossRefGoogle Scholar
  8. Bodine-Baron E, Hassibi B, Wierman A (2010) Distance-dependent Kronecker graphs for modeling social networks. IEEE J Sel Top Signal Process 4(4):718–731CrossRefGoogle Scholar
  9. Boguñá M, Pastor-Satorras R, Vespignani A (2004) Cut-offs and finite size effects in scale-free networks. Eur Phys J B Condens Matter Complex Syst 38(2):205–209. doi: 10.1140/epjb/e2004-00038-8 CrossRefzbMATHGoogle Scholar
  10. Borge-Holthoefer J, Rivero A, Moreno Y (2012) Locating privileged spreaders on an online social network. Phys Rev E 85(6):066,123CrossRefGoogle Scholar
  11. Buono C, Alvarez-Zuzek LG, Macri PA, Braunstein LA (2014) Epidemics in partially overlapped multiplex networks. PloS ONE 9(3):e92,200CrossRefGoogle Scholar
  12. Cerchiello P, Giudici P (2016) How to measure the quality of financial tweets. Qual Quant 50(4):1695–1713. doi: 10.1007/s11135-015-0229-6 CrossRefGoogle Scholar
  13. Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: KDD ’09. ACM, New York, pp 199–208. doi: 10.1145/1557019.1557047
  14. Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on World wide web. ACM, pp 925–936Google Scholar
  15. Chung F, Lu L, Vu V (2003) Eigenvalues of random power law graphs. Ann Comb 7(1):21–33. doi: 10.1007/s000260300002 MathSciNetCrossRefzbMATHGoogle Scholar
  16. Clauset A, Moore C, Newman ME (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98–101CrossRefGoogle Scholar
  17. Cozzo E, Baños RA, Meloni S, Moreno Y (2013) Contact-based social contagion in multiplex networks. Phys Rev E 88(050):801. doi: 10.1103/PhysRevE.88.050801 Google Scholar
  18. Darabi Sahneh F, Scoglio C (2014) Competitive epidemic spreading over arbitrary multilayer networks. Phys Rev E 89(062):817. doi: 10.1103/PhysRevE.89.062817 Google Scholar
  19. Dezső Z, Barabási AL (2002) Halting viruses in scale-free networks. Phys Rev E 65(5):055,103CrossRefGoogle Scholar
  20. Dickens L, Molloy I, Lobo J, Cheng PC, Russo A (2012a) Learning stochastic models of information flow. In: 2012 IEEE 28th international conference on data engineering, pp 570–581. doi: 10.1109/ICDE.2012.103
  21. Dickens L, Molloy I, Lobo J, Cheng PC, Russo A (2012b) Learning stochastic models of information flow. In: 2012 IEEE 28th international conference on data engineering. IEEE, pp 570–581Google Scholar
  22. Du N, Song L, Gomez-Rodriguez M, Zha H (2013) Scalable influence estimation in continuous-time diffusion networks. In: Advances in neural information processing systems, pp 3147–3155Google Scholar
  23. Erdös P, Rényi A (1959) On random graphs, i, vol 6. Publicationes Mathematicae, DebrecenzbMATHGoogle Scholar
  24. Galuba W, Aberer K, Chakraborty D, Despotovic Z, Kellerer W (2010) Outtweeting the twitterers-predicting information cascades in microblogs. WOSN 10:3–11Google Scholar
  25. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223. doi: 10.1023/A:1011122126881 CrossRefGoogle Scholar
  26. Gomez-rodriguez M, Schlkopf DBB (2011) Uncovering the temporal dynamics of diffusion networks. In: Proceedings of the 28th international conference on machine learning (ICML11)Google Scholar
  27. Gong Y, Zhang Q, Sun X, Huang X (2015) Who will you “@”? In: Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM ’15. ACM, New York, pp 533–542. doi: 10.1145/2806416.2806458
  28. González-Bailón S, Borge-Holthoefer J, Rivero A, Moreno Y (2011) The dynamics of protest recruitment through an online network. Scientific reports 1Google Scholar
  29. Granell C, Gómez S, Arenas A (2013) Dynamical interplay between awareness and epidemic spreading in multiplex networks. Phys Rev Lett 111(12):128,701CrossRefGoogle Scholar
  30. Granovetter M (1978) Threshold models of collective behavior. Am J Soc 83(6):1420–1443CrossRefGoogle Scholar
  31. Guo Q, Jiang X, Lei Y, Li M, Ma Y, Zheng Z (2015) Two-stage effects of awareness cascade on epidemic spreading in multiplex networks. Phys Rev E 91(1):012,822CrossRefGoogle Scholar
  32. Gupta M, Gao J, Zhai C, Han J (2012) Predicting future popularity trend of events in microblogging platforms. Proc Am Soc Inf Sci Technol 49(1):1–10CrossRefGoogle Scholar
  33. Jin F, Dougherty E, Saraf P, Cao Y, Ramakrishnan N (2013) Epidemiological modeling of news and rumors on twitter. In: Proceedings of the 7th workshop on social network mining and analysis, SNAKDD ’13, vol 8. ACM, New York, pp 1–8:9. doi: 10.1145/2501025.2501027
  34. Kato S, Koide A, Fushimi T, Saito K, Motoda H (2012) Network analysis of three twitter functions: favorite, follow and mention. In: Richards D, Kang B (eds) Knowledge management and acquisition for intelligent systems, Lecture notes in computer science. Springer, Berlin, pp 298–312. doi: 10.1007/978-3-642-32541-0_26
  35. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’03. ACM, New York, pp 137–146. doi: 10.1145/956750.956769
  36. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2(3):203–271CrossRefGoogle Scholar
  37. Kupavskii A, Ostroumova L, Umnov A, Usachev S, Serdyukov P, Gusev G, Kustarev A (2012) Prediction of retweet cascade size over time. In: Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12. ACM, New York, pp 2335–2338. doi: 10.1145/2396761.2398634
  38. 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, pp 591–600Google Scholar
  39. Lerman K, Ghosh R (2010) Information contagion: an empirical study of the spread of news on digg and twitter social networks. ICWSM 10:90–97Google Scholar
  40. Leskovec J, Faloutsos C (2007) Scalable modeling of real graphs using kronecker multiplication. In: Proceedings of the 24th international conference on machine learning, ICML ’07. ACM, New York, pp 497–504. doi: 10.1145/1273496.1273559
  41. Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2008) Statistical properties of community structure in large social and information networks. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 695–704Google Scholar
  42. Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C, Ghahramani Z (2010) Kronecker graphs: an approach to modeling networks. J Mach Learn Res 11:985–1042MathSciNetzbMATHGoogle Scholar
  43. Li W, Tang S, Fang W, Guo Q, Zhang X, Zheng Z (2015) How multiple social networks affect user awareness: the information diffusion process in multiplex networks. Phys Rev E 92(4):042,810CrossRefGoogle Scholar
  44. Li Y, Feng Z, Wang H, Kong S, Feng L (2013) ReTweet p: modeling and predicting tweets spread using an extended susceptible-infected- susceptible epidemic model. Springer, Berlin, pp 454–457. doi: 10.1007/978-3-642-37450-0_35
  45. Malhotra A, Malhotra CK, See A (2012) How to get your messages retweeted. MIT Sloan Manage Rev 53(2):61–66Google Scholar
  46. Newman ME (2002) Spread of epidemic disease on networks. Phys Rev E 66(1):016,128MathSciNetCrossRefGoogle Scholar
  47. Petrovic S, Osborne M, Lavrenko V (2011) Rt to win! predicting message propagation in twitter. In: ICWSMGoogle Scholar
  48. Pillai SU, Suel T, Cha S (2005) The Perron–Frobenius theorem: some of its applications. IEEE Signal Process Mag 22(2):62–75. doi: 10.1109/MSP.2005.1406483 CrossRefGoogle Scholar
  49. Pramanik S, Wang Q, Danisch M, Bandi S, Kumar A, Guillaume JL, Mitra B (2016) On the role of mentions on tweet virality. In: The 3rd IEEE international conference on data science and advanced analytics (DSAA)Google Scholar
  50. Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE second international conference on social computing (socialcom). IEEE, pp 177–184Google Scholar
  51. Tang L, Ni Z, Xiong H, Zhu H (2014) Locating targets through mention in twitter. World Wide Web, pp 1–31. doi: 10.1007/s11280-014-0299-8
  52. Uysal I, Croft WB (2011) User oriented tweet ranking: a filtering approach to microblogs. In: CIKM ’11. ACM, pp 2261–2264Google Scholar
  53. Valera I, Gomez-Rodriguez M, Gummadi K (2014) Modeling diffusion of competing products and conventions in social media. arXiv preprint arXiv:14060516
  54. Wang B, Wang C, Bu J, Chen C, Zhang WV, Cai D, He X (2013) Whom to mention: expand the diffusion of tweets by @ recommendation on micro-blogging systems. In: Proceedings of the 22nd international conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, WWW ’13, pp 1331–1340Google Scholar
  55. Xu Z, Yang Q (2012) Analyzing user retweet behavior on twitter. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012), ASONAM ’12. IEEE Computer Society, Washington, pp 46–50. doi: 10.1109/ASONAM.2012.18
  56. Youssef M, Scoglio C (2011) An individual-based approach to sir epidemics in contact networks. J Theor Biol 283(1):136–144MathSciNetCrossRefGoogle Scholar
  57. Zhao D, Li L, Peng H, Luo Q, Yang Y (2014) Multiple routes transmitted epidemics on multiplex networks. Phys Lett A 378(10):770–776MathSciNetCrossRefzbMATHGoogle Scholar
  58. Zhao Q, Erdogdu MA, He HY, Rajaraman A, Leskovec J (2015) Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’15. ACM, New York, pp 1513–1522. doi: 10.1145/2783258.2783401
  59. Zhou G, Yu L, Zhang CX, Liu C, Zhang ZK, Zhang J (2015) A novel approach for generating personalized mention list on micro-blogging system. In: 2015 IEEE international conference on data mining workshop (ICDMW), pp 1368–1374. doi: 10.1109/ICDMW.2015.51

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIIT KharagpurKharagpurIndia
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606ParisFrance
  3. 3.Telecom Paris TechParisFrance
  4. 4.L3IUniversity of La RochelleLa RochelleFrance

Personalised recommendations