Advertisement

Analysis of social interaction network properties and growth on Twitter

  • Arif Mohaimin Sadri
  • Samiul Hasan
  • Satish V. Ukkusuri
  • Juan Esteban Suarez Lopez
Original Article

Abstract

The complex topology of real networks allows its actors to change their functional behavior. Network models provide better understanding of the evolutionary mechanisms being accountable for the growth of such networks by capturing the dynamics in the ways network agents interact and change their behavior. Considerable amount of research efforts is required for developing novel network modeling techniques to understand the structural properties of such networks, reproducing similar properties based on empirical evidence, and designing such networks efficiently. In this study, we first demonstrate how to construct social interaction networks using social media data and then present the key findings obtained from the network analytics. We analyze the characteristics and growth of online social interaction networks, examine the network properties and derive important insights based on the theories of network science literature. We observed that the degree distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interaction. While the network elements and average user degree grow linearly each day, densities of such networks tend to become zero. Largest connected components exhibit higher connectivity (density) when compared with the whole graph. Network radius and diameter become stable over time evidencing the small-world property. We also observe increased transitivity and higher stability of the power-law exponents as the networks grow. Since the data is specific to the Purdue University community, we also observe two very big events, namely Purdue Day of Giving and Senator Bernie Sanders’ visit to Purdue University as part of Indiana Primary Election 2016. The social interaction network properties that are revealed in this study can be useful in disseminating targeted information during planned special events.

Keywords

Planned special events Social media Twitter User mention Social interaction Network science 

Notes

Acknowledgements

The authors are grateful to National Science Foundation for the Grant CMMI-1131503 and CMMI-1520338 to support the research presented in this paper. However, the authors are solely responsible for the findings presented in this study.

Author contributions

All the authors have contributed to the design of the study, conduct of the research, and writing the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interests.

References

  1. Abbasi A, Rashidi TH, Maghrebi M, Waller ST (eds) (2015) Utilising location based social media in travel survey methods: bringing Twitter data into the play. In: Proceedings of the 8th ACM SIGSPATIAL international workshop on location-based social networks. ACMGoogle Scholar
  2. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47MathSciNetzbMATHCrossRefGoogle Scholar
  3. Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382CrossRefGoogle Scholar
  4. Alstott J, Bullmore E, Plenz D (2014) powerlaw: a Python package for analysis of heavy-tailed distributions. PloS One 9(1):e85777CrossRefGoogle Scholar
  5. Anderson RM, May RM, Anderson B (1992) Infectious diseases of humans: dynamics and control: Wiley Online LibraryGoogle Scholar
  6. Bagrow JP, Wang D, Barabasi A-L (2011) Collective response of human populations to large-scale emergencies. PloS One 6(3):e17680CrossRefGoogle Scholar
  7. Balthrop J, Forrest S, Newman ME, Williamson MM (2004) Technological networks and the spread of computer viruses. Science 304(5670):527–529CrossRefGoogle Scholar
  8. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetzbMATHCrossRefGoogle Scholar
  9. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308MathSciNetzbMATHCrossRefGoogle Scholar
  10. Borondo J, Morales A, Benito R, Losada J (2015) Multiple leaders on a multilayer social media. Chaos Solitons Fractals 72:90–98MathSciNetCrossRefGoogle Scholar
  11. Caragea C, McNeese N, Jaiswal A, Traylor G, Kim H-W, Mitra P et al (eds) (2011) Classifying text messages for the haiti earthquake. In: Proceedings of the 8th international conference on information systems for crisis response and management (ISCRAM2011). CiteseerGoogle Scholar
  12. Carrasco J-A, Miller EJ (2009) The social dimension in action: a multilevel, personal networks model of social activity frequency between individuals. Transp Res Part A Policy Pract 43(1):90–104CrossRefGoogle Scholar
  13. Carson JL, Bylsma RG (2003) Transportation planning and management for special eventsGoogle Scholar
  14. Cebelak MK (2013) Location-based social networking data: doubly-constrained gravity model origin-destination estimation of the urban travel demand for Austin, TXGoogle Scholar
  15. Chen Y, Mahmassani HS (eds) (2016). Exploring activity and destination choice behavior in two metropolitan areas using social networking data. In: Transportation research board 95th annual meetingGoogle Scholar
  16. Coleman JS, Katz E, Menzel H (1966) Medical innovation: a diffusion study: Bobbs-Merrill CoGoogle Scholar
  17. Collins C, Hasan S, Ukkusuri SV (2013) A novel transit rider satisfaction metric: rider sentiments measured from online social media data. J Public Transp 16(2):2CrossRefGoogle Scholar
  18. Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci 105(7):2301–2306CrossRefGoogle Scholar
  19. Dunn W Jr (1989) Traffic management of special events: the 1986 US Open Golf Tournament. Trans Res Circ 344Google Scholar
  20. Earle PS, Bowden DC, Guy M (1989) Twitter earthquake detection: earthquake monitoring in a social world. Ann Geophysics 54(6)Google Scholar
  21. Freeman M (2011) Fire, wind and water: social networks in natural disasters. JCIT 13(2):69–79Google Scholar
  22. Granovetter MS (1973) The strength of weak ties. Am J Sociol 1360–1380Google Scholar
  23. Guy M, Earle P, Ostrum C, Gruchalla K, Horvath S (eds) (2010). Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. In: International symposium on intelligent data analysis. SpringerGoogle Scholar
  24. Hasan S, Ukkusuri SV (2014) Urban activity pattern classification using topic models from online geo-location data. Transp Res Part C Emerg Technol 44:363–381CrossRefGoogle Scholar
  25. Hasan S, Ukkusuri SV (2015) Location contexts of user check-ins to model urban geo life-style patterns. PloS One 10(5):e0124819CrossRefGoogle Scholar
  26. Helbing D (2013) Globally networked risks and how to respond. Nature 497(7447):51–59CrossRefGoogle Scholar
  27. Hughes AL, Palen L (2009) Twitter adoption and use in mass convergence and emergency events. Int J Emerg Manage 6(3–4):248–260CrossRefGoogle Scholar
  28. Jin P, Cebelak M, Yang F, Zhang J, Walton C, Ran B (2014) Location-based social networking data: exploration into use of doubly constrained gravity model for origin-destination estimation. Transp Res Rec J Transp Res Board 2430:72–82CrossRefGoogle Scholar
  29. Kinney R, Crucitti P, Albert R, Latora V (2005) Modeling cascading failures in the North American power grid. Eur Phys J B Condens Matter Complex Syst 46(1):101–107CrossRefGoogle Scholar
  30. Korolov R, Peabody J, Lavoie A, Das S, Magdon-Ismail M, Wallace W (eds) (2015) Actions are louder than words in social media. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ACMGoogle Scholar
  31. Krikorian R (2013) New tweets per second record, and how. Twitter Eng Blog 16Google Scholar
  32. Kryvasheyeu Y, Chen H, Obradovich N, Moro E, Van Hentenryck P, Fowler J et al (2016) Rapid assessment of disaster damage using social media activity. Sci Adv 2(3):e1500779CrossRefGoogle Scholar
  33. Latoski SP, Dunn WM, Wagenblast B, Randall J, Walker MD (2003) Managing travel for planned special events: final reportGoogle Scholar
  34. Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D et al (2009) Life in the network: the coming age of computational social science. Science 323(5915):721CrossRefGoogle Scholar
  35. Lee JH, Gao S, Goulias KG (eds) (2016). Comparing the origin-destination matrices from travel demand model and social media data. In: Transportation research board 95th annual meetingGoogle Scholar
  36. Li J, Rao HR (2010) Twitter as a rapid response news service: an exploration in the context of the 2008 China earthquake. Electron J Inf Syst Dev Countries 42Google Scholar
  37. Lu X, Brelsford C (2014) Network structure and community evolution on twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami. Sci Rep 4:6773CrossRefGoogle Scholar
  38. Maghrebi M, Abbasi A, Rashidi TH, Waller ST (eds) (2015) Complementing travel diary surveys with twitter data: application of text mining techniques on activity location, type and time. In: 2015 IEEE 18th international conference on intelligent transportation systems. IEEEGoogle Scholar
  39. Malevergne Y, Pisarenko V, Sornette D (2005) Empirical distributions of stock returns: between the stretched exponential and the power law? Quant Fin 5(4):379–401MathSciNetzbMATHCrossRefGoogle Scholar
  40. Malevergne Y, Pisarenko V, Sornette D (2009) Gibrat’s law for cities: uniformly most powerful unbiased test of the Pareto against the lognormal. Swiss Finance Institute Research Paper 09–40Google Scholar
  41. Milgram S (1967) The small world problem. Psychol Today 2(1):60–67Google Scholar
  42. Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102CrossRefGoogle Scholar
  43. Morales AJ, Creixell W, Borondo J, Losada JC, Benito RM (2015) Characterizing ethnic interactions from human communication patterns in Ivory Coast. NHM 10(1):87–99MathSciNetCrossRefGoogle Scholar
  44. Murray JD (2002) Mathematical biology I: an introduction, vol 17 of interdisciplinary applied mathematics. Springer, New YorkGoogle Scholar
  45. Myers SA, Sharma A, Gupta P, Lin J (eds) (2014) Information network or social network? The structure of the twitter follow graph. In: Proceedings of the 23rd international conference on World Wide Web. ACMGoogle Scholar
  46. Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256MathSciNetzbMATHCrossRefGoogle Scholar
  47. Newman ME, Forrest S, Balthrop J (2002) Email networks and the spread of computer viruses. Phys Rev E 66(3):035101CrossRefGoogle Scholar
  48. Pickard G, Pan W, Rahwan I, Cebrian M, Crane R, Madan A et al (2011) Time-critical social mobilization. Science 334(6055):509–512CrossRefGoogle Scholar
  49. Sachtjen M, Carreras B, Lynch V (2000) Disturbances in a power transmission system. Phys Rev E 61(5):4877CrossRefGoogle Scholar
  50. Sadri AM, Lee S, Ukkusuri SV (2015) Modeling social network influence on joint trip frequency for regular activity travel decisions. Transp Res Record J Transp Res Board 2495:83–93CrossRefGoogle Scholar
  51. Sadri AM, Hasan S, Ukkusuri SV, Cebrian M (2017a) Crisis communication patterns in social media during hurricane sandy. Transp Res Record.  https://doi.org/10.1177/0361198118773896 CrossRefGoogle Scholar
  52. Sadri AM, Hasan S, Ukkusuri SV, Cebrian M (2017b) Understanding information spreading in social media during Hurricane Sandy: user activity and network properties. arXiv preprint arXiv:170603019Google Scholar
  53. Sadri AM, Ukkusuri SV, Gladwin H (2017c) The role of social networks and information sources on hurricane evacuation decision making. Nat Hazards Rev.  https://doi.org/10.1061/(ASCE)NH.1527-6996.0000244 CrossRefGoogle Scholar
  54. Sadri AM, Ukkusuri SV, Gladwin H (2017d) Modeling joint evacuation decisions in social networks: the case of Hurricane Sandy. J Choice Model 25:50–60CrossRefGoogle Scholar
  55. Sadri AM, Hasan S, Ukkusuri SV (2017e) Joint inference of user community and interest patterns in social interaction networks. arXiv preprint arXiv:170401706Google Scholar
  56. Sakaki T, Okazaki M, Matsuo Y (eds) (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World wide web. ACMGoogle Scholar
  57. Scott J (2012) Social network analysis. SageGoogle Scholar
  58. Skinner J (2013) Natural disasters and Twitter: thinking from both sides of the tweet. First Monday 18(9)Google Scholar
  59. Skolnik J, Chami R, Walker M (2008) Planned special events—economic role and congestion effectsGoogle Scholar
  60. Sornette D (2009) Why stock markets crash: critical events in complex financial systems. Princeton University Press, PrincetonGoogle Scholar
  61. Travers J, Milgram S (1969) An experimental study of the small world problem. Sociometry 425–443Google Scholar
  62. Ukkusuri S, Zhan X, Sadri A, Ye Q (2014) Use of social media data to explore crisis informatics: Study of 2013 Oklahoma tornado. Transport Res Rec J Transp Res Board 2459:110–118Google Scholar
  63. Ukkusuri SV, Mesa-Arango R, Narayanan B, Sadri AM, Qian X (2016) Evolution of the commonwealth trade network. In: International Trade Working Paper 2016/07, Commonwealth Secretariat, LondonGoogle Scholar
  64. Van Hentenryck P (ed) (2013) Computational disaster management. IJCAIGoogle Scholar
  65. Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939):425–428MathSciNetzbMATHCrossRefGoogle Scholar
  66. Wang D, Lin Y-R, Bagrow JP (2014) Social networks in emergency response. Encyclopedia of social network analysis and mining. Springer. p 1904–1914Google Scholar
  67. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440–442zbMATHCrossRefGoogle Scholar
  68. Watts D, Cebrian M, Elliot M (2013) Dynamics of social media. Public response to alerts and warnings using social media: report of a workshop on current knowledge and research gaps. The National Academies Press, Washington, DCGoogle Scholar
  69. Yang F, Jin PJ, Wan X, Li R, Ran B (eds) (2014) Dynamic origin-destination travel demand estimation using location based social networking data. In: Transportation research board 93rd annual meetingGoogle Scholar
  70. Zhao S, Zhang K (eds) (2016) Observing individual dynamic choices of activity chains from location-based crowdsourced data. In: Transportation research board 95th annual meetingGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Arif Mohaimin Sadri
    • 1
  • Samiul Hasan
    • 2
  • Satish V. Ukkusuri
    • 3
  • Juan Esteban Suarez Lopez
    • 4
  1. 1.Moss School of Construction, Infrastructure and SustainabilityFlorida International UniversityMiamiUSA
  2. 2.Department of Civil, Environmental, and Construction EngineeringUniversity of Central FloridaOrlandoUSA
  3. 3.Lyles School of Civil EngineeringPurdue UniversityWest LafayetteUSA
  4. 4.School of Civil EngineeringNational University of ColombiaMedellínColombia

Personalised recommendations