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Joint inference of user community and interest patterns in social interaction networks

Abstract

Online social media have become an integral part of our social beings. Analyzing conversations in social media platforms can lead to complex probabilistic models to understand social interaction networks. In this paper, we present a modeling approach for characterizing social interaction networks by jointly inferring user communities and interests based on social media interactions. We present several pattern inference models: (1) interest pattern model (IPM) captures population level interaction topics, (2) user interest pattern model (UIPM) captures user specific interaction topics, and (3) community interest pattern model (CIPM) captures both community structures and user interests. We test our methods on Twitter data collected from Purdue University community. From our model results, we observe the interaction topics and communities related to two big events within Purdue University community, namely Purdue Day of Giving and Senator Bernie Sanders’ visit to Purdue University as part of Indiana Primary Election 2016. Constructing social interaction networks based on user interactions accounts for the similarity of users’ interactions on various topics of interest and indicates their community belonging further beyond connectivity. 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. We also discuss the application of such networks as a useful tool to effectively disseminate specific information to the target audience towards planning any large-scale events and demonstrate how to single out specific nodes in a given community by running network algorithms.

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References

  1. Abbasi A, Rashidi TH, Maghrebi M, Travis Waller S (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

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

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

  4. Anderson RM, May RM, Anderson B (1992) Infectious diseases of humans: dynamics and control, vol 28. Wiley Online Library, Boca Raton

  5. Balthrop J, Forrest S, Newman MEJ, Matthew MW (2004) Technological networks the spread of computer viruses. Science 304(5670):527–529

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

  7. Blei DM, Ng AY, Michael IJ (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

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

  9. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177

  10. Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Netw 30(2):136–145

  11. Brandes U, Pich C (2007) Centrality estimation in large networks. Int J Bifurcation Chaos 17(07):2303–2318

  12. Broido AD, Clauset A (2018) Scale-free networks are rare. arXiv preprint arXiv:1801.03400

  13. Caragea C, McNeese N, Jaiswal A, Traylor G, Kim H-W, Mitra P, Wu D, Tapia AH, Giles L, Bernard JJ (2011) Classifying text messages for the haiti earthquake. In: Proceedings of the 8th international conference on information systems for crisis response and management (ISCRAM2011)

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

  15. Chen Y, Mahmassani HS (2016) Exploring activity and destination choice behavior in two metropolitan areas using social networking data. In: Transportation research board 95th annual meeting

  16. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703

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

  18. 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

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

  20. Earle PS, Bowden DC, Guy M (2012) Twitter earthquake detection: earthquake monitoring in a social world. Ann Geophys 54(6):708–715. https://doi.org/10.4401/ag-5364

  21. Ferrara E (2012) Community structure discovery in facebook. Int J Soc Netw Min 1(1):67–90

  22. Fortunato S (2010) Community detection in graphs. Physics reports 486(3):75–174

  23. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

  24. Gao H, Barbier G, Goolsby R, Zeng D (2011) Harnessing the crowdsourcing power of social media for disaster relief. DTIC Document

  25. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235

  26. Guy M, Earle P, Ostrum C, Gruchalla K, Horvath S (2010) Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. In: International symposium on intelligent data analysis

  27. Hasan S, Ukkusuri SV (2011) A threshold model of social contagion process for evacuation decision making. Transp Res Part B Methodol 45(10):1590–1605

  28. 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

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

  30. Hasan S, Zhan X, Ukkusuri SV (2013) Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing

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

  32. Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics

  33. Hughes AL, St Denis LAA, Palen L, Anderson KM (2014) Online public communications by police & fire services during the 2012 Hurricane Sandy. In: Proceedings of the 32nd annual ACM conference on human factors in computing systems

  34. Imran M, Elbassuoni SM, Castillo C, Diaz F, Meier P (2013) Extracting information nuggets from disaster-related messages in social media. In: Proceeding of ISCRAM, Baden-Baden, Germany

  35. 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

  36. Kim Y, Shim K (2014) TWILITE: a recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation. Inf Syst 42:59–77

  37. 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

  38. Klarreich E (2018) Scant evidence of power laws found in real-world networks. Quanta Mag 20180215

  39. Kogan M, Palen L, Anderson KM (2015) Think local, retweet global: retweeting by the geographically-vulnerable during Hurricane Sandy. In: Proceedings of the 18th ACM conference on computer supported cooperative work & social computing

  40. Korolov R, Peabody J, Lavoie A, Das S, Magdon-Ismail M, Wallace W (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

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

  42. Kryvasheyeu Y, Chen H, Moro E, Van Hentenryck P, Cebrian M (2015) Performance of social network sensors during Hurricane Sandy. PLoS One 10(2):e0117288

  43. Kryvasheyeu Y, Chen H, Obradovich N, Moro E, Hentenryck PV, Fowler J, Cebrian M (2016) Rapid assessment of disaster damage using social media activity. Sci Adv 2(3):e1500779

  44. Kumar S, Hu X, Liu H (2014) A behavior analytics approach to identifying tweets from crisis regions. In: Proceedings of the 25th ACM conference on hypertext and social media

  45. Latoski SP, Dunn Jr WM, Wagenblast B, Randall J, Walker MD (2003) Managing travel for planned special events (No. FHWA-OP-04-010)

  46. Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M (2009) Life in the network: the coming age of computational social science. Science 323(5915):721

  47. Lee JH, Gao S, Goulias KG (2016a) Comparing the origin-destination matrices from travel demand model and social media data. In: Transportation research board 95th annual meeting

  48. Lee JH, Davis AW, Goulias KG (2016b) Activity space estimation with longitudinal observations of social media data. In: Paper submitted for presentation at the 95th annual meeting of the transportation research board. Washington, DC

  49. 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 Ctries 42:1–22

  50. Liang Y, Zheng X, Zeng DD, Zhou X, Leischow SJ, Chung W (2015) Characterizing social interaction in tobacco-oriented social networks: an empirical analysis. Sci Rep 5:10060

  51. Maghrebi M, Abbasi A, Rashidi TH, Travis Waller S (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

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

  53. 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

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

  55. Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B Condens Matter Complex Syst 38(2):321–330

  56. Newman M (2010) Networks: an introduction. Oxford University Press, Oxford

  57. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

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

  59. Power R, Robinson B, Colton J, Cameron M (2014) Emergency situation awareness: twitter case studies. In: International conference on information systems for crisis response and management in mediterranean countries

  60. Rezende PHR, Sadri AM, Ukkusuri SV (2016) Social network influence on mode choice and carpooling during special events: the case of Purdue game day. In: Transportation research board 95th annual meeting

  61. Robert C (2007) The Bayesian choice: from decision-theoretic foundations to computational implementation. Springer, Berlin

  62. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: Proceedings of the 20th conference on uncertainty in artificial intelligence

  63. Saberi M, Mahmassani HS, Brockmann D, Hosseini A (2017) A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks. Transportation 44(6):1383–1402

  64. Sachtjen ML, Carreras BA, Lynch VE (2000) Disturbances in a power transmission system. Phys Rev E 61(5):4877

  65. Sadri AM (2016) Social network influence on ridesharing, disaster communication and community interaction. Ph.D. dissertation, Ph.D. thesis, Purdue University (20874)

  66. Sadri AM, Lee S, Ukkusuri SV (2015) Modeling social network influence on joint trip frequency for regular activity travel decisions. Transp Res Rec 2495:83–93. https://doi.org/10.3141/2495-09

  67. Sadri AM, Hasan S, Ukkusuri SV, Cebrian M (2017a) Understanding information spreading in social media during Hurricane Sandy: user activity and network properties. arXiv preprint arXiv:1706.03019

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

  69. Sadri AM, Ukkusuri SV, Lee S, Clawson R, Aldrich D, Nelson MS, Seipel J, Kelly D (2017c) The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience: a study of rural communities in Indiana. Nat Hazard 90:1–30

  70. Sadri AM, Ukkusuri SV, Gladwin H (2017d) The role of social networks and information sources on hurricane evacuation decision making. Nat Hazard Rev. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000244

  71. Sadri AM, Hasan S, Ukkusuri SV, Cebrian M (2018a) Crisis communication patterns in social media during Hurricane Sandy. Transp Res Rec 2672(1):125–137

  72. Sadri AM, Hasan S, Ukkusuri SV, Lopez JES (2018b) Analysis of social interaction network properties and growth on Twitter. Soc Netw Anal Min 8(1):56

  73. Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World wide web

  74. Scott J (2012) Social network analysis. Sage, Thousand Oaks

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

  76. St Denis LA, Palen L, Anderson KM (2014) Mastering social media: an analysis of Jefferson county’s communications during the 2013 Colorado floods. In: 11th International ISCRAM conference

  77. Starbird K, Palen L (2010) Pass it on?: retweeting in mass emergency. In: International community on information systems for crisis response and management

  78. Steyvers M, Griffiths T (2007) Probabilistic topic models. Handb Latent Semant Anal 427(7):424–440

  79. Ukkusuri S, Zhan X, Sadri AM, Ye Q (2014) Use of social media data to explore crisis informatics: study of 2013 Oklahoma tornado. Transp Res Rec J Transp Res Board 2459:110–118

  80. Ukkusuri SV, Mesa-Arango R, Narayanan B, Sadri AM, Qian X (2016) Evolution of the commonwealth trade network. International trade working paper 2016/07, Commonwealth Secretariat, London

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

  82. Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI conference on human factors in computing systems

  83. Voitalov I, van der Hoorn P, van der Hofstad R, Krioukov D (2018) Scale-free networks well done. arXiv preprint arXiv:1811.02071

  84. Wang Q, Taylor JE (2014) Quantifying human mobility perturbation and resilience in Hurricane Sandy. PLoS One 9(11):e112608

  85. Wang Q, Taylor JE (2015) Resilience of human mobility under the influence of typhoons. Procedia Eng 118:942–949

  86. Weng L, Menczer F, Ahn Y-Y (2013) Virality prediction and community structure in social networks. Sci Rep 3:2522

  87. Xiao C, Zhang Y, Zeng X, Wu Y (2013) Predicting user influence in social media. JNW 8(11):2649–2655

  88. Yadav A, Wilder B, Rice E, Petering R, Craddock J, Yoshioka-Maxwell A, Hemler M, Onasch-Vera L, Tambe M, Woo D (2017) Influence maximization in the field: the arduous journey from emerging to deployed application. In: Proceedings of the 16th conference on autonomous agents and multiagent systems

  89. Yang F, Jin PJ, Wan X, Li R, Ran B (2014) Dynamic origin-destination travel demand estimation using location based social networking data. In: Transportation research board 93rd annual meeting

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

  91. Zhao Y, Levina E, Zhu J (2011) Community extraction for social networks. Proc Natl Acad Sci 108(18):7321–7326

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

Correspondence to Arif Mohaimin Sadri.

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Sadri, A.M., Hasan, S. & Ukkusuri, S.V. Joint inference of user community and interest patterns in social interaction networks. Soc. Netw. Anal. Min. 9, 11 (2019). https://doi.org/10.1007/s13278-019-0551-4

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