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Analysis of social interaction network properties and growth on Twitter

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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.

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References

  • 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. ACM

  • Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  MATH  Google Scholar 

  • Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382

    Article  Google Scholar 

  • Alstott J, Bullmore E, Plenz D (2014) powerlaw: a Python package for analysis of heavy-tailed distributions. PloS One 9(1):e85777

    Article  Google Scholar 

  • Anderson RM, May RM, Anderson B (1992) Infectious diseases of humans: dynamics and control: Wiley Online Library

  • Bagrow JP, Wang D, Barabasi A-L (2011) Collective response of human populations to large-scale emergencies. PloS One 6(3):e17680

    Article  Google Scholar 

  • Balthrop J, Forrest S, Newman ME, Williamson MM (2004) Technological networks and the spread of computer viruses. Science 304(5670):527–529

    Article  Google Scholar 

  • Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308

    Article  MathSciNet  MATH  Google Scholar 

  • Borondo J, Morales A, Benito R, Losada J (2015) Multiple leaders on a multilayer social media. Chaos Solitons Fractals 72:90–98

    Article  MathSciNet  Google Scholar 

  • 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). Citeseer

  • 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):90–104

    Article  Google Scholar 

  • Carson JL, Bylsma RG (2003) Transportation planning and management for special events

  • Cebelak MK (2013) Location-based social networking data: doubly-constrained gravity model origin-destination estimation of the urban travel demand for Austin, TX

  • 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 meeting

  • Coleman JS, Katz E, Menzel H (1966) Medical innovation: a diffusion study: Bobbs-Merrill Co

  • 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):2

    Article  Google Scholar 

  • Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci 105(7):2301–2306

    Article  Google Scholar 

  • Dunn W Jr (1989) Traffic management of special events: the 1986 US Open Golf Tournament. Trans Res Circ 344

  • Earle PS, Bowden DC, Guy M (1989) Twitter earthquake detection: earthquake monitoring in a social world. Ann Geophysics 54(6)

  • Freeman M (2011) Fire, wind and water: social networks in natural disasters. JCIT 13(2):69–79

    Google Scholar 

  • Granovetter MS (1973) The strength of weak ties. Am J Sociol 1360–1380

  • 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. Springer

  • Hasan S, Ukkusuri SV (2014) Urban activity pattern classification using topic models from online geo-location data. Transp Res Part C Emerg Technol 44:363–381

    Article  Google Scholar 

  • Hasan S, Ukkusuri SV (2015) Location contexts of user check-ins to model urban geo life-style patterns. PloS One 10(5):e0124819

    Article  Google Scholar 

  • Helbing D (2013) Globally networked risks and how to respond. Nature 497(7447):51–59

    Article  Google Scholar 

  • Hughes AL, Palen L (2009) Twitter adoption and use in mass convergence and emergency events. Int J Emerg Manage 6(3–4):248–260

    Article  Google Scholar 

  • 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 2430:72–82

    Article  Google Scholar 

  • 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):101–107

    Article  Google Scholar 

  • 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. ACM

  • Krikorian R (2013) New tweets per second record, and how. Twitter Eng Blog 16

  • 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):e1500779

    Article  Google Scholar 

  • Latoski SP, Dunn WM, Wagenblast B, Randall J, Walker MD (2003) Managing travel for planned special events: final report

  • 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):721

    Article  Google Scholar 

  • 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 meeting

  • 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 42

  • 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 4:6773

    Article  Google Scholar 

  • 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. IEEE

  • Malevergne Y, Pisarenko V, Sornette D (2005) Empirical distributions of stock returns: between the stretched exponential and the power law? Quant Fin 5(4):379–401

    Article  MathSciNet  MATH  Google Scholar 

  • 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 09–40

  • Milgram S (1967) The small world problem. Psychol Today 2(1):60–67

    Google Scholar 

  • Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102

    Article  Google Scholar 

  • Morales AJ, Creixell W, Borondo J, Losada JC, Benito RM (2015) Characterizing ethnic interactions from human communication patterns in Ivory Coast. NHM 10(1):87–99

    Article  MathSciNet  Google Scholar 

  • Murray JD (2002) Mathematical biology I: an introduction, vol 17 of interdisciplinary applied mathematics. Springer, New York

    Google Scholar 

  • 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. ACM

  • Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Newman ME, Forrest S, Balthrop J (2002) Email networks and the spread of computer viruses. Phys Rev E 66(3):035101

    Article  Google Scholar 

  • Pickard G, Pan W, Rahwan I, Cebrian M, Crane R, Madan A et al (2011) Time-critical social mobilization. Science 334(6055):509–512

    Article  Google Scholar 

  • Sachtjen M, Carreras B, Lynch V (2000) Disturbances in a power transmission system. Phys Rev E 61(5):4877

    Article  Google Scholar 

  • 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 2495:83–93

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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:170603019

  • 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

    Article  Google Scholar 

  • Sadri AM, Ukkusuri SV, Gladwin H (2017d) Modeling joint evacuation decisions in social networks: the case of Hurricane Sandy. J Choice Model 25:50–60

    Article  Google Scholar 

  • Sadri AM, Hasan S, Ukkusuri SV (2017e) Joint inference of user community and interest patterns in social interaction networks. arXiv preprint arXiv:170401706

  • 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. ACM

  • Scott J (2012) Social network analysis. Sage

  • Skinner J (2013) Natural disasters and Twitter: thinking from both sides of the tweet. First Monday 18(9)

  • Skolnik J, Chami R, Walker M (2008) Planned special events—economic role and congestion effects

  • Sornette D (2009) Why stock markets crash: critical events in complex financial systems. Princeton University Press, Princeton

  • Travers J, Milgram S (1969) An experimental study of the small world problem. Sociometry 425–443

  • 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 2459:110–118

  • 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, London

  • Van Hentenryck P (ed) (2013) Computational disaster management. IJCAI

  • Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939):425–428

    Article  MathSciNet  MATH  Google Scholar 

  • Wang D, Lin Y-R, Bagrow JP (2014) Social networks in emergency response. Encyclopedia of social network analysis and mining. Springer. p 1904–1914

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440–442

    Article  MATH  Google Scholar 

  • 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, DC

    Google Scholar 

  • 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 meeting

  • Zhao S, Zhang K (eds) (2016) Observing individual dynamic choices of activity chains from location-based crowdsourced data. In: Transportation research board 95th annual meeting

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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.

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Authors and Affiliations

  1. Moss School of Construction, Infrastructure and Sustainability, Florida International University, 10555 West Flagler Street, Miami, FL, 33174, USA

    Arif Mohaimin Sadri

  2. Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Orlando, FL, 32816, USA

    Samiul Hasan

  3. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Satish V. Ukkusuri

  4. School of Civil Engineering, National University of Colombia, Medellín, Colombia

    Juan Esteban Suarez Lopez

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

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Correspondence to Arif Mohaimin Sadri.

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Sadri, A.M., Hasan, S., Ukkusuri, S.V. et al. Analysis of social interaction network properties and growth on Twitter. Soc. Netw. Anal. Min. 8, 56 (2018). https://doi.org/10.1007/s13278-018-0533-y

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