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Multimedia Tools and Applications

, Volume 78, Issue 23, pp 33747–33804 | Cite as

User behavior mining on social media: a systematic literature review

  • Rahebeh Mojtahedi Safari
  • Amir Masoud RahmaniEmail author
  • Sasan H. Alizadeh
Article
  • 138 Downloads

Abstract

User behavior mining on Social Media (UBMSM) is the process of representing, analyzing, and extracting operational and behavioral patterns from user behavioral data in social media. It discusses theories and methodologies from different disciplines such as combining theorems and techniques from computer science, data mining, machine learning, social network analysis, and other related disciplines. User behavior mining provides a deep understanding of user behavioral data such that we observe not only individual behavioral patterns, but also interaction and communication among users by considering collective behavior of users. The aim of this study is to provide a systematic literature review on the significant aspects and approaches in addressing user behavior mining on social media. A systematic literature review was performed to find the related literature, and 174 articles were selected as primary studies. We classified the surveyed studies into four categories based on their focused area: users, user-generated content, the structure of network that content spreads on it and information diffusion. The majority of the primary articles focus on user aspect (66%); 6% of them focus on content aspect; 6% of them focus on network structure aspect, 22% of them focus on information diffusion aspect.

Keywords

Systematic literature review User behavior mining Behavioral data Individual behavior Collective behavior Social media User-generated content Information diffusion 

Notes

References

  1. 1.
    Abel F, Gao Q, Houben G-J, Tao K (2011a) Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web. Paper presented at the proceedings of the 3rd international web science conferenceGoogle Scholar
  2. 2.
    Abel F, Gao Q, Houben G-J, Tao K (2011b) Analyzing user modeling on twitter for personalized news recommendations. Paper presented at the international conference on user modeling, adaptation, and personalizationGoogle Scholar
  3. 3.
    Abel F, Gao Q, Houben G-J, Tao K (2011c). Semantic enrichment of twitter posts for user profile construction on the social web. Paper presented at the Extended Semantic Web ConferenceGoogle Scholar
  4. 4.
    Abel F, Hauff C, Houben G-J, Tao K (2012) Leveraging user modeling on the social web with linked data. Paper presented at the International Conference on Web EngineeringGoogle Scholar
  5. 5.
    Abel F, Herder E, Houben G-J, Henze N, Krause D (2013) Cross-system user modeling and personalization on the social web. User Modeling and User-Adapted Interaction:1–41Google Scholar
  6. 6.
    Abrahao B, Chierichetti F, Kleinberg R, Panconesi A (2013) Trace complexity of network inference. Paper presented at the proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  7. 7.
    Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. Paper presented at the proceedings of the 3rd international workshop on link discoveryGoogle Scholar
  8. 8.
    Aggarwal CC, Subbian K (2012) Event detection in social streams. Paper presented at the proceedings of the 2012 SIAM international conference on data miningGoogle Scholar
  9. 9.
    Ahmed A, Low Y, Aly M, Josifovski V, Smola AJ (2011) Scalable distributed inference of dynamic user interests for behavioral targeting. Paper presented at the proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  10. 10.
    Aiello LM, Barrat A, Schifanella R, Cattuto C, Markines B, Menczer F (2012) Friendship prediction and homophily in social media. ACM Trans Web 6(2):9Google Scholar
  11. 11.
    Almars A, Li X, Zhao X (2019) Modelling user attitudes using hierarchical sentiment-topic model. Data Knowl Eng 119:139–149Google Scholar
  12. 12.
    Almgren K, Lee J (2015) A hybrid framework to predict influential users on social networks. Paper presented at the digital information management (ICDIM), 2015 tenth international conference onGoogle Scholar
  13. 13.
    Alp ZZ, Öğüdücü ŞG (2018) Identifying topical influencers on twitter based on user behavior and network topology. Knowl-Based Syst 141:211–221Google Scholar
  14. 14.
    Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21544–21549Google Scholar
  15. 15.
    Artzi Y, Pantel P, Gamon M (2012) Predicting responses to microblog posts. Paper presented at the proceedings of the 2012 conference of the north American chapter of the Association for Computational Linguistics: human language technologiesGoogle Scholar
  16. 16.
    Backstrom L, Bakshy E, Kleinberg JM, Lento TM, Rosenn I (2011) Center of attention: how facebook users allocate attention across friends. ICWSM 11:23Google Scholar
  17. 17.
    Baingana B, Giannakis GB (2017) Tracking switched dynamic network topologies from information cascades. IEEE Trans Signal Process 65(4):985–997MathSciNetzbMATHGoogle Scholar
  18. 18.
    Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone's an influencer: quantifying influence on twitter. Paper presented at the Proceedings of the fourth ACM international conference on Web search and data miningGoogle Scholar
  19. 19.
    Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. Paper presented at the proceedings of the 21st international conference on world wide webGoogle Scholar
  20. 20.
    Bao Q, Cheung WK, Zhang Y, Liu J (2017) A component-based diffusion model with structural diversity for social networks. IEEE T Cybernetics 47(4):1078–1089Google Scholar
  21. 21.
    Barabasi A-L (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039):207–211Google Scholar
  22. 22.
    Becker H, Naaman M, Gravano L (2010) Learning similarity metrics for event identification in social media. Paper presented at the Proceedings of the third ACM international conference on Web search and data miningGoogle Scholar
  23. 23.
    Bhattacharya P, Zafar MB, Ganguly N, Ghosh S, Gummadi KP (2014) Inferring user interests in the twitter social network. Paper presented at the proceedings of the 8th ACM conference on recommender systemsGoogle Scholar
  24. 24.
    Bloch, F., Demange, G., & Kranton, R. (2018). Rumors and social networks. International Economic Review 59(2), 421-448.Google Scholar
  25. 25.
    Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. (JSTAT) 2008(10):P10008zbMATHGoogle Scholar
  26. 26.
    Bordianu GC (2012) Learning influence probabilities in social networks. McGill UniversityGoogle Scholar
  27. 27.
    Borgatti SP (2005) Centrality and network flow. Soc Networks 27(1):55–71MathSciNetGoogle Scholar
  28. 28.
    Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80(4):571–583Google Scholar
  29. 29.
    Budak C, Kannan A, Agrawal R, Pedersen J (2014). Inferring user interests from microblogs. Technical Report, MSR-TR-2014-68Google Scholar
  30. 30.
    Cano AE,Mazumdar S, Ciravegna F (2014) Social influence analysis in microblogging platforms–a topic- sensitive based approach. Semantic Web 5(5):357Google Scholar
  31. 31.
    Cao L (2010) In-depth behavior understanding and use: the behavior informatics approach. Inf Sci 180(17):3067–3085Google Scholar
  32. 32.
    Cha Y, Cho J (2012) Social-network analysis using topic models. Paper presented at the proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval.Google Scholar
  33. 33.
    Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17):30Google Scholar
  34. 34.
    Cha Y, Bi B, Hsieh C-C, Cho J (2013) Incorporating popularity in topic models for social network analysis. Paper presented at the proceedings of the 36th international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  35. 35.
    Chen J, Nairn R, Nelson L, Bernstein M, Chi E (2010) Short and tweet: experiments on recommending content from information streams. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsGoogle Scholar
  36. 36.
    Colleoni E, Rozza A, Arvidsson A (2014) Echo chamber or public sphere? Predicting political orientation and measuring political homophily in twitter using big data. J Commun 64(2):317–332Google Scholar
  37. 37.
    Conover M, Ratkiewicz J, Francisco MR, Gonçalves B, Menczer F, Flammini A (2011) Political polarization on twitter. ICWSM 133:89–96Google Scholar
  38. 38.
    Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. Paper presented at the proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  39. 39.
    Cui P, Wang F, Liu S, Ou M, Yang S, Sun L (2011) Who should share what?: item-level social influence prediction for users and posts ranking. Paper presented at the Proceedings of the 34th international ACM SIGIR conference on Research and development in Information retrievalGoogle Scholar
  40. 40.
    Daneshmand H, Gomez-Rodriguez M, Song L, Schoelkopf B (2014) Estimating diffusion network structures: recovery conditions, Sample Complexity & Soft-thresholding Algorithm. Paper presented at the ICMLGoogle Scholar
  41. 41.
    Dang A, Moh’d A, Gruzd A, Milios E, Minghim R (2015) A visual framework for clustering memes in social media. Paper presented at the advances in social networks analysis and mining (ASONAM), 2015 IEEE/ACM international conference onGoogle Scholar
  42. 42.
    Davenport TH, Beck JC (2013) The attention economy: Understanding the new currency of business. Harvard Business PressGoogle Scholar
  43. 43.
    Davoudi A, Chatterjee M (2016) Prediction of information diffusion in social networks using dynamic carrying capacity. Paper presented at the big data (big data), 2016 IEEE international conference on.Google Scholar
  44. 44.
    De Choudhury M (2011) Tie formation on twitter: homophily and structure of egocentric networks. Paper presented at the privacy, security, risk and trust (PASSAT) and 2011 IEEE third International conference on social computing (SocialCom), 2011 IEEE third international conference onGoogle Scholar
  45. 45.
    Deutsch M, Gerard HB (1955) A study of normative and informational social influences upon individual judgment. J Abnorm Soc Psychol 51(3):629Google Scholar
  46. 46.
    Ding Y, Jiang J (2014) Extracting interest tags from twitter user biographies. Paper presented at the Asia Information Retrieval SymposiumGoogle Scholar
  47. 47.
    Dougnon RY, Fournier-Viger P, Nkambou R (2015) Inferring user profiles in online social networks using a partial social graph. Paper presented at the Canadian Conference on Artificial IntelligenceGoogle Scholar
  48. 48.
    Efstathiades H, Antoniades D, Pallis G, Dikaiakos MD, Szlávik Z, Sips R-J (2016) Online social network evolution: revisiting the Twitter graph. Paper presented at the big data (big data), 2016 IEEE international conference onGoogle Scholar
  49. 49.
    Ferrara E, JafariAsbagh M, Varol O, Qazvinian V, Menczer F, Flammini A (2013). Clustering memes in social media. Paper presented at the advances in social networks analysis and mining (ASONAM), 2013 IEEE/ACM international conference onGoogle Scholar
  50. 50.
    Fogués RL, Such JM, Espinosa A, Garcia-Fornes A (2014) BFF: a tool for eliciting tie strength and user communities in social networking services. Inf Syst Front 16(2):225–237Google Scholar
  51. 51.
    Gabrilovich E, Markovitch S (2007) Computing semantic relatedness using wikipedia-based explicit semantic analysis. Paper presented at the IJcAIGoogle Scholar
  52. 52.
    Gallos LK, Rybski D, Liljeros F, Havlin S, Makse HA (2012) How people interact in evolving online affiliation networks. Physical Review X 2(3):031014Google Scholar
  53. 53.
    Garousi V, Zhi J (2013) A survey of software testing practices in Canada. J Syst Softw 86(5):1354–1376Google Scholar
  54. 54.
    Ghosh R, Lerman K (2010) Predicting influential users in online social networks. arXiv preprint arXiv:1005.4882Google Scholar
  55. 55.
    Ghosh R, Lerman K (2011) A framework for quantitative analysis of cascades on networks. Paper presented at the Proceedings of the fourth ACM international conference on Web search and data miningGoogle Scholar
  56. 56.
    Ghosh S, Sharma N, Benevenuto F, Ganguly N, Gummadi K (2012) Cognos: crowdsourcing search for topic experts in microblogs. Paper presented at the proceedings of the 35th international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  57. 57.
    Gilbert E, Karahalios K (2009) Predicting tie strength with social media. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systemsGoogle Scholar
  58. 58.
    Golub B, Jackson MO (2012) How homophily affects the speed of learning and best-response dynamics. Q J Econ 127(3):1287–1338zbMATHGoogle Scholar
  59. 59.
    Gomez Rodriguez M, Leskovec J, Schölkopf B (2013) Structure and dynamics of information pathways in online media. Paper presented at the Proceedings of the sixth ACM international conference on Web search and data miningGoogle Scholar
  60. 60.
    Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data 5(4):21Google Scholar
  61. 61.
    Gonçalves B, Perra N, Vespignani A (2011) Modeling users’ activity on twitter networks: validation of Dunbar’s number. PLoS One 6(8):e22656Google Scholar
  62. 62.
    Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. Paper presented at the Proceedings of the third ACM international conference on Web search and data miningGoogle Scholar
  63. 63.
    Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380Google Scholar
  64. 64.
    Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443Google Scholar
  65. 65.
    Greenberger M (1971) Computers, communications, and the public interest. Johns Hopkins University PressGoogle Scholar
  66. 66.
    Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(2):17–28Google Scholar
  67. 67.
    Halberstam Y, Knight B (2016) Homophily, group size, and the diffusion of political information in social networks: evidence from twitter. J Public Econ 143:73–88Google Scholar
  68. 68.
    Han J, Lee H (2016) Characterizing the interests of social media users: refinement of a topic model for incorporating heterogeneous media. Inf Sci 358:112–128Google Scholar
  69. 69.
    Hannon J, Bennett M, Smyth B (2010) Recommending twitter users to follow using content and collaborative filtering approaches. Paper presented at the Proceedings of the fourth ACM conference on Recommender systemsGoogle Scholar
  70. 70.
    Hannon, J., McCarthy, K., O’Mahony, M. P., & Smyth, B. (2012). A multi-faceted user model for twitter. Paper presented at the International Conference on User Modeling, Adaptation, and PersonalizationGoogle Scholar
  71. 71.
    Harvey M, Crestani F, Carman MJ (2013) Building user profiles from topic models for personalised search. Paper presented at the proceedings of the 22nd ACM international conference on conference on information & knowledge managementGoogle Scholar
  72. 72.
    He JS, Kaur H, Talluri M (2016) Positive opinion influential node set selection for social networks: considering both positive and negative relationships. In: Wireless communications, networking and applications. Springer, p 935-948Google Scholar
  73. 73.
    Holland PW, Laskey KB, Leinhardt S (1983) Stochastic blockmodels: first steps. Soc Networks 5(2):109–137MathSciNetGoogle Scholar
  74. 74.
    Hong L, Doumith AS, Davison BD (2013) Co-factorization machines: modeling user interests and predicting individual decisions in twitter. Paper presented at the Proceedings of the sixth ACM international conference on Web search and data miningGoogle Scholar
  75. 75.
    Hosseini-Pozveh M, Zamanifar K, Naghsh-Nilchi AR, Dolog P (2016) Maximizing the spread of positive influence in signed social networks. Intell Data Anal 20(1):199–218Google Scholar
  76. 76.
    Hosseini-Pozveh M, Zamanifar K, Naghsh-Nilchi AR (2019) Assessing information diffusion models for influence maximization in signed social networks. Expert Syst Appl 119:476–490Google Scholar
  77. 77.
    Hu Y, Chen M (2016) Information diffusion prediction in mobile social networks with hydrodynamic model. Paper presented at the communications (ICC), 2016 IEEE international conference onGoogle Scholar
  78. 78.
    Hu Z, Wang C, Yao J, Xing E, Yin H, Cui B (2013) Community specific temporal topic discovery from social media. arXiv preprint arXiv:1312.0860Google Scholar
  79. 79.
    Huang, A. H., Lehavy, R., Zang, A. Y., & Zheng, R. (2017). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 2833-2855.Google Scholar
  80. 80.
    Huang, X., Yang, Y., Hu, Y., Shen, F., & Shao, J. (2016). Dynamic user attribute discovery on social media. In Asia-Pacific Web Conference (pp. 256-267). Springer, Cham.. Paper presented at the Asia-Pacific Web ConferenceGoogle Scholar
  81. 81.
    Hung C-C, Huang Y-C, Hsu JY-j, Wu DK-C (2008) Tag-based user profiling for social media recommendation. Paper presented at the Workshop on Intelligent Techniques for Web Personalization & Recommender Systems at AAAIGoogle Scholar
  82. 82.
    Jackson MO, López-Pintado D (2013) Diffusion and contagion in networks with heterogeneous agents and homophily. Network Science 1(01):49–67Google Scholar
  83. 83.
    JafariAsbagh, M., Ferrara, E., Varol, O., Menczer, F., & Flammini, A. (2014). Clustering memes in social media streams. Soc Netw Anal Min 4(1): 237.Google Scholar
  84. 84.
    Jiang B, Sha Y (2015) Modeling temporal dynamics of user interests in online social networks. ProcediaComput Sci 51:503–512Google Scholar
  85. 85.
    Jiang C, Chen Y, Liu KR (2014) Evolutionary dynamics of information diffusion over social networks. IEEE Trans Signal Process 62(17):4573–4586MathSciNetzbMATHGoogle Scholar
  86. 86.
    Jin D, Ma X, Zhang Y, Abbas H, Yu H (2018) Information diffusion model based on social big data. Mobile NETW APPL 23(4):717–722Google Scholar
  87. 87.
    Jones JJ, Settle JE, Bond RM, Fariss CJ, Marlow C, Fowler JH (2013) Inferring tie strength from online directed behavior. PLoS One 8(1):e52168Google Scholar
  88. 88.
    Kang J, Choi H, Lee H (2019) Deep recurrent convolutional networks for inferring user interests from social media. J Intell Inf Syst 52(1):191–209Google Scholar
  89. 89.
    Kapanipathi P, Jain P, Venkataramani C, Sheth A (2014) User interests identification on twitter using a hierarchical knowledge base. Paper presented at the European Semantic Web ConferenceGoogle Scholar
  90. 90.
    Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53(1):59–68Google Scholar
  91. 91.
    Karrer B, Newman ME (2011) Competing epidemics on complex networks. Phys Rev E 84(3):036106Google Scholar
  92. 92.
    Karsai M, Kivelä M, Pan RK, Kaski K, Kertész J, Barabási A-L, Saramäki J (2011) Small but slow world: how network topology and burstiness slow down spreading. Phys Rev E 83(2):025102Google Scholar
  93. 93.
    Kelman, H. C. (2017). Further thoughts on the processes of compliance, identification, and internalization Social power and political influence (pp. 125-171): Routledge.Google Scholar
  94. 94.
    Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. Paper presented at the Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data miningGoogle Scholar
  95. 95.
    Khan HU, Daud A, Ishfaq U, Amjad T, Aljohani N, Abbasi RA, Alowibdi JS (2017) Modelling to identify influential bloggers in the blogosphere: a survey. Comput Hum Behav 68:64–82Google Scholar
  96. 96.
    Khemmarat S, Saha S, Song HH, Baldi M, Gao L (2014) On understanding user interests through heterogeneous data sources. Paper presented at the International Conference on Passive and Active Network MeasurementGoogle Scholar
  97. 97.
    Kim H-N, Ha I, Lee K-S, Jo G-S, El-Saddik A (2011) Collaborative user modeling for enhanced content filtering in recommender systems. Decis Support Syst 51(4):772–781Google Scholar
  98. 98.
    Kossinets G, Watts DJ (2009) Origins of homophily in an evolving social network 1. Am J Sociol 115(2):405–450Google Scholar
  99. 99.
    Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Link mining: models, algorithms, and applications. Springer, p 337-357Google Scholar
  100. 100.
    Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web (pp. 591-600). AcM.Google Scholar
  101. 101.
    Lahuerta-Otero E, Cordero-Gutiérrez R (2016) Looking for the perfect tweet. The use of data mining techniques to find influencers on twitter. Computers in Human behavior 64:575–583Google Scholar
  102. 102.
    Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D, … Gutmann M (2009) Life in the network: the coming age of computational social science. Science (New York, NY) 323(5915):721Google Scholar
  103. 103.
    Lehmann J, Gonçalves B, Ramasco JJ, Cattuto C (2012) Dynamical classes of collective attention in twitter. Paper presented at the proceedings of the 21st international conference on world wide webGoogle Scholar
  104. 104.
    Lerman K, Ghosh R, Surachawala T (2012) Social contagion: an empirical study of information spread on Digg and twitter follower graphs. arXiv preprint arXiv:1202.3162Google Scholar
  105. 105.
    Li J, Peng W, Li T, Sun T, Li Q, Xu J (2014) Social network user influence sense-making and dynamics prediction. Expert Syst Appl 41(11):5115–5124Google Scholar
  106. 106.
    Lim KH, Datta A (2013) Interest classification of Twitter users using Wikipedia. Paper presented at the proceedings of the 9th international symposium on open collaborationGoogle Scholar
  107. 107.
    Liu C, Zhang Z-K (2014) Information spreading on dynamic social networks. Commun Nonlinear Sci Numer Simul 19(4):896–904MathSciNetGoogle Scholar
  108. 108.
    Liu L, Tang J, Han J, Yang S (2012) Learning influence from heterogeneous social networks. Data Min Knowl Disc 25(3):511–544MathSciNetzbMATHGoogle Scholar
  109. 109.
    Liu L, Qu B, Chen B, Hanjalic A, Wang H (2018) Modelling of information diffusion on social networks with applications to WeChat. Physica A 496:318–329Google Scholar
  110. 110.
    Lobel I, Sadler E (2015) Information diffusion in networks through social learning. Theor Econ 10(3):807–851MathSciNetzbMATHGoogle Scholar
  111. 111.
    Locher DA (2002) Collective behavior. Prentice Hall, Upper Saddle RiverGoogle Scholar
  112. 112.
    Lomi, A., Robins, G., & Tranmer, M. (2016). Introduction to multilevel social networks. Social networks(44), 266-268.Google Scholar
  113. 113.
    Lu C, Lam W, Zhang Y (2012) Twitter user modeling and tweets recommendation based on wikipedia concept graph. Paper presented at the Workshops at the Twenty-Sixth AAAI Conference on Artificial IntelligenceGoogle Scholar
  114. 114.
    Ma Y, Zeng Y, Ren X, Zhong N (2011) User interests modeling based on multi-source personal information fusion and semantic reasoning. Paper presented at the International Conference on Active Media TechnologyGoogle Scholar
  115. 115.
    Mahmud J (2014) Why do you write this? Prediction of Influencers from Word Use. Paper presented at the ICWSMGoogle Scholar
  116. 116.
    Mashayekhi Y, Meybodi MR, Rezvanian A (2018) Weighted estimation of information diffusion probabilities for independent cascade model. Paper presented at the 2018 4th international conference on web research (ICWR)Google Scholar
  117. 117.
    McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annu Rev Sociol 27(1):415–444Google Scholar
  118. 118.
    Mehrotra R, Sanner S, Buntine W, Xie L (2013) Improving lda topic models for microblogs via tweet pooling and automatic labeling. Paper presented at the proceedings of the 36th international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  119. 119.
    Meng L, Hulovatyy Y, Striegel A, Milenković T (2016) On the interplay between individuals’ evolving interaction patterns and traits in dynamic multiplex social networks. IEEE Trans Netw Sci Eng 3(1):32–43Google Scholar
  120. 120.
    Mezghani M, Zayani CA, Amous I, Péninou A, Sedes F (2014) Dynamic enrichment of social users’ interests. Paper presented at the research challenges in information science (RCIS), 2014 IEEE eighth international conference onGoogle Scholar
  121. 121.
    Michelle GG, Kumaran P, Chitrakala S (2016) Topic sensitive information diffusion modelling in online social networks. Paper presented at the advances in electrical, electronics, information, communication and bio-informatics (AEEICB), 2016 2nd international conference onGoogle Scholar
  122. 122.
    Michelson M, Macskassy SA (2010) Discovering users’ topics of interest on twitter: a first look. Paper presented at the Proceedings of the fourth workshop on Analytics for noisy unstructured text dataGoogle Scholar
  123. 123.
    Milenković T, Memišević V, Bonato A, Pržulj N (2011) Dominating biological networks. PLoS One 6(8):e23016Google Scholar
  124. 124.
    Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102Google Scholar
  125. 125.
    Mislove A, Viswanath B, Gummadi KP, Druschel P (2010) You are who you know: inferring user profiles in online social networks. Paper presented at the Proceedings of the third ACM international conference on Web search and data miningGoogle Scholar
  126. 126.
    MUSCILLO, A. (2014). Discrete Models of Information Diffusion in Networks.Google Scholar
  127. 127.
    Myers S, Leskovec J (2010) On the convexity of latent social network inference. Paper presented at the Advances in Neural Information Processing SystemsGoogle Scholar
  128. 128.
    Narducci F, Musto C, Semeraro G, Lops P, de Gemmis M (2013) Exploiting big data for enhanced representations in content-based recommender systems. Paper presented at the International Conference on Electronic Commerce and Web TechnologiesGoogle Scholar
  129. 129.
    Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256MathSciNetzbMATHGoogle Scholar
  130. 130.
    Orlandi F, Breslin J, Passant A (2012) Aggregated, interoperable and multi-domain user profiles for the social web. Paper presented at the proceedings of the 8th international conference on semantic systemsGoogle Scholar
  131. 131.
    Oselio B, Kulesza A, Hero AO (2014) Multi-layer graph analysis for dynamic social networks. IEEE J Sel Topics Signal Process 8(4):514–523Google Scholar
  132. 132.
    Ottoni R, Las Casas DB, Pesce JP, Meira Jr W, Wilson C, Mislove A, Almeida VA (2014) Of pins and tweets: investigating how users behave across image-and text-based social networks. Paper presented at the ICWSMGoogle Scholar
  133. 133.
    Pal A, Herdagdelen A, Chatterji S, Taank S, Chakrabarti D (2016) Discovery of topical authorities in instagram. Paper presented at the proceedings of the 25th international conference on world wide webGoogle Scholar
  134. 134.
    Pennacchiotti M, Gurumurthy S (2011) Investigating topic models for social media user recommendation. Paper presented at the proceedings of the 20th international conference companion on world wide webGoogle Scholar
  135. 135.
    Pereira FS, Gama J, de Amo S, Oliveira GM (2018) On analyzing user preference dynamics with temporal social networks. Mach Learn 107(11):1745–1773MathSciNetzbMATHGoogle Scholar
  136. 136.
    Piao G (2016) Towards comprehensive user modeling on the social web for personalized link recommendations. Paper presented at the proceedings of the 2016 conference on user modeling adaptation and personalizationGoogle Scholar
  137. 137.
    Piao G, Breslin JG (2016a) Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. Paper presented at the proceedings of the 2016 conference on user modeling adaptation and personalizationGoogle Scholar
  138. 138.
    Piao G, Breslin JG (2016b) Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. Paper presented at the proceedings of the 12th international conference on semantic systemsGoogle Scholar
  139. 139.
    Piao G, Breslin JG (2016c). User modeling on Twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. Paper presented at the proceedings of the 25th ACM international on conference on information and knowledge managementGoogle Scholar
  140. 140.
    Pochampally R, Varma V (2011) User context as a source of topic retrieval in Twitter. Paper presented at the workshop on enriching information retrieval (with ACM SIGIR)Google Scholar
  141. 141.
    Ramage D, Hall D, Nallapati R, Manning CD (2009) Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. Paper presented at the proceedings of the 2009 conference on empirical methods in natural language processing: volume 1-volume 1Google Scholar
  142. 142.
    Ramanathan K, Kapoor K (2009) Creating user profiles using wikipedia. Paper presented at the International Conference on Conceptual ModelingGoogle Scholar
  143. 143.
    Rashotte L (2007) Social influence. The Blackwell Encyclopedia of Social Psychology 9:562–563Google Scholar
  144. 144.
    Riquelme F, González-Cantergiani P (2016) Measuring user influence on twitter: a survey. Inf Process Manag 52(5):949–975Google Scholar
  145. 145.
    Rodriguez MG, Schölkopf B (2012) Submodular inference of diffusion networks from multiple trees. arXiv preprint arXiv:1205.1671Google Scholar
  146. 146.
    Rodriguez MG, Balduzzi D, Schölkopf B (2011). Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697Google Scholar
  147. 147.
    Rogers EM, Cartano DG (1962) Methods of measuring opinion leadership. Public Opin Q:435–441Google Scholar
  148. 148.
    Romero DM, Galuba W, Asur S, Huberman BA (2011a) Influence and passivity in social media. Paper presented at the Joint European Conference on Machine Learning and Knowledge Discovery in DatabasesGoogle Scholar
  149. 149.
    Romero DM, Tan C, Ugander J (2011b) On the interplay between social and topical structure. arXiv preprint arXiv:1112.1115Google Scholar
  150. 150.
    Sadikov E, Medina M, Leskovec J, Garcia-Molina H (2011) Correcting for missing data in information cascades. Paper presented at the Proceedings of the fourth ACM international conference on Web search and data miningGoogle Scholar
  151. 151.
    Saito K, Nakano R, Kimura M (2008). Prediction of information diffusion probabilities for independent cascade model. Paper presented at the Knowledge-based intelligent information and engineering systemsGoogle Scholar
  152. 152.
    Saito K, Kimura M, Ohara K, Motoda H (2010) Generative models of information diffusion with asynchronous Timedelay. Paper presented at the ACMLGoogle Scholar
  153. 153.
    Saito K, Kimura M, Ohara K, Motoda H (2012a) Efficient discovery of influential nodes for SIS models in social networks. Knowl Inf Syst 30(3):613–635Google Scholar
  154. 154.
    Saito K, Kimura M, Ohara K, Motoda H (2012b) Learning asynchronous-time information diffusion models and its application to behavioral data analysis over social networks. arXiv preprint arXiv:1204.4528Google Scholar
  155. 155.
    Schifanella R, Barrat A, Cattuto C, Markines B, Menczer F (2010) Folks in folksonomies: social link prediction from shared metadata. Paper presented at the Proceedings of the third ACM international conference on Web search and data miningGoogle Scholar
  156. 156.
    Servia-Rodríguez S, Díaz-Redondo R, Fernández-Vilas A, Pazos-Arias JJ (2013) Mining facebook activity to discover social ties: towards a social-sensitive ecosystem. Closer 367:71–85Google Scholar
  157. 157.
    Shah B, Verma AP, Tiwari S (2018) User interest modeling from social media network graph. Enriched with semantic web. Paper presented at the Proceedings of International Conference on Computational Intelligence and Data EngineeringGoogle Scholar
  158. 158.
    Sharma NK, Ghosh S, Benevenuto F, Ganguly N, Gummadi K (2012) Inferring who-is-who in the twitter social network. ACM Sigcomm Comp Com 42(4):533–538Google Scholar
  159. 159.
    Sheikhahmadi A, Nematbakhsh MA (2017) Identification of multi-spreader users in social networks for viral marketing. J Inf Sci 43(3):412–423Google Scholar
  160. 160.
    Shu X, Qi G-J, Tang J, Wang J (2015) Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. Paper presented at the Proceedings of the 23rd ACM international conference on MultimediaGoogle Scholar
  161. 161.
    Slaughter AJ, Koehly LM (2016) Multilevel models for social networks: hierarchical Bayesian approaches to exponential random graph modeling. Soc Networks 44:334–345Google Scholar
  162. 162.
    Sneppen K, Trusina A, Jensen MH, Bornholdt S (2010) A minimal model for multiple epidemics and immunity spreading. PLoS One 5(10):e13326Google Scholar
  163. 163.
    Snijders TA (2016) The multiple flavours of multilevel issues for networks Multilevel network analysis for the social sciences. Springer, p 15-46Google Scholar
  164. 164.
    Sohrabi MK, Akbari S (2016) A comprehensive study on the effects of using data mining techniques to predict tie strength. Comput Hum Behav 60:534–541Google Scholar
  165. 165.
    Steeg GV, Ghosh R, Lerman K (2011) What stops social epidemics? arXiv preprint arXiv:1102.1985Google Scholar
  166. 166.
    Sun Q, Wang N, Zhou Y, Luo Z (2016) Identification of influential online social network users based on multi-features. Int J Pattern Recognit Artif Intell 30(06):1659015Google Scholar
  167. 167.
    Szabo G, Huberman BA (2010) Predicting the popularity of online content. Commun ACM 53(8):80–88Google Scholar
  168. 168.
    Tabasso N (2015) Diffusion of multiple information: on information resilience and the power of segregationGoogle Scholar
  169. 169.
    Tan C, Tang J, Sun J, Lin Q, Wang F (2010) Social action tracking via noise tolerant time-varying factor graphs. Paper presented at the proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining.Google Scholar
  170. 170.
    Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. Paper presented at the proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  171. 171.
    Tang S, Blenn N, Doerr C, Van Mieghem P (2011) Digging in the digg social news website. IEEE Trans Multimedia 13(5):1163–1175Google Scholar
  172. 172.
    Tang J, Shu X, Qi G-J, Li Z, Wang M, Yan S, Jain R (2017) Tri-clustered tensor completion for social-aware image tag refinement. IEEE Trans Pattern Anal Mach Intell 39(8):1662–1674Google Scholar
  173. 173.
    Tang J, Shu X, Li Z, Jiang Y-G, Tian Q (2019) Social anchor-unit graph regularized tensor completion for large-scale image retagging. IEEE Trans Pattern Anal Mach IntellGoogle Scholar
  174. 174.
    Tao K, Abel F, Gao Q, Houben G-J (2011) Tums: twitter-based user modeling service. Paper presented at the Extended Semantic Web ConferenceGoogle Scholar
  175. 175.
    Tong C, He W, Niu J, Xie Z (2016) A novel information cascade model in online social networks. Physica A 444:297–310Google Scholar
  176. 176.
    Trikha AK, Zarrinkalam F, Bagheri E (2018) Topic-association mining for user interest detection. Paper presented at the European Conference on Information RetrievalGoogle Scholar
  177. 177.
    Uysal I, Croft WB (2011) User oriented tweet ranking: a filtering approach to microblogs. Paper presented at the proceedings of the 20th ACM international conference on information and knowledge managementGoogle Scholar
  178. 178.
    Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939):425–428MathSciNetzbMATHGoogle Scholar
  179. 179.
    Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in Facebook. Paper presented at the proceedings of the 2nd ACM workshop on online social networksGoogle Scholar
  180. 180.
    Wan J, Chen X, Du Y, Jia M (2019) Information propagation model based on hybrid social factors of opportunity, trust and motivation. Neurocomputing 333:169–184Google Scholar
  181. 181.
    Wang F, Wang H, Xu K (2012) Diffusive logistic model towards predicting information diffusion in online social networks. Paper presented at the distributed computing systems workshops (ICDCSW), 2012 32nd international conference on.Google Scholar
  182. 182.
    Wang F, Wang H, Xu K, Wu J, Jia X (2013a) Characterizing information diffusion in online social networks with linear diffusive model. Paper presented at the distributed computing systems (ICDCS), 2013 IEEE 33rd international conference onGoogle Scholar
  183. 183.
    Wang T, Liu H, He J, Du X (2013b) Mining user interests from information sharing behaviors in social media. Paper presented at the Pacific-Asia Conference on Knowledge Discovery and Data MiningGoogle Scholar
  184. 184.
    Wang Y, Shen H-W, Liu S, Cheng X-Q (2013c) Learning user-specific latent influence and susceptibility from information cascades. arXiv preprint arXiv:1310.3911Google Scholar
  185. 185.
    Wang F, Wang G, Xie D (2016a) Maximizing the spread of positive influence under LT-MLA model. Paper presented at the Asia-Pacific Services Computing ConferenceGoogle Scholar
  186. 186.
    Wang P, Robins G, Pattison P, Lazega E (2016b) Social selection models for multilevel networks. Soc Networks 44:346–362Google Scholar
  187. 187.
    Wang H, Huang X, Li L (2018) Microblog oriented interest extraction with both content and network structure. Intell Data Anal 22(3):515–532Google Scholar
  188. 188.
    Watts DJ (2007) A twenty-first century science. Nature 445(7127):489–489Google Scholar
  189. 189.
    Weber I, Garimella VRK, Batayneh A (2013) Secular vs. Islamist polarization in Egypt on twitter. Paper presented at the proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and miningGoogle Scholar
  190. 190.
    Weng L (2014) Information diffusion on online social networks. CiteseerGoogle Scholar
  191. 191.
    Weng L, Lento TM (2014) Topic-based clusters in egocentric networks on Facebook. Paper presented at the ICWSMGoogle Scholar
  192. 192.
    Weng L, Menczer F (2015) Topicality and impact in social media: diverse messages, focused messengers. PLoS One 10(2):e0118410Google Scholar
  193. 193.
    Weng J, Lim E-P, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. Paper presented at the Proceedings of the third ACM international conference on Web search and data miningGoogle Scholar
  194. 194.
    Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2Google Scholar
  195. 195.
    Wu F, Huberman BA (2007) Novelty and collective attention. Proc Natl Acad Sci 104(45):17599–17601Google Scholar
  196. 196.
    Wuchty S, Stadler PF (2003) Centers of complex networks. J Theor Biol 223(1):45–53MathSciNetGoogle Scholar
  197. 197.
    Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. Paper presented at the proceedings of the 19th international conference on world wide webGoogle Scholar
  198. 198.
    Xiao Y, Wang Z, Li Q, Li T (2019) Dynamic model of information diffusion based on multidimensional complex network space and social game. Physica A 521:578–590MathSciNetGoogle Scholar
  199. 199.
    Xu KS, Hero AO (2014) Dynamic stochastic blockmodels for time-evolving social networks. J Sel Topics Signal Process 8(4):552–562Google Scholar
  200. 200.
    Xu T, Oard DW (2011) Wikipedia-based topic clustering for microblogs. Proc Am Soc Inf Sci Technol 48(1):1–10Google Scholar
  201. 201.
    Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. Paper presented at the data mining (ICDM), 2010 IEEE 10th international conference onGoogle Scholar
  202. 202.
    Yang L, Sun T, Zhang M, Mei Q (2012) We know what@ you# tag: does the dual role affect hashtag adoption? Paper presented at the proceedings of the 21st international conference on world wide webGoogle Scholar
  203. 203.
    Ye S, Wu SF (2010) Measuring message propagation and social influence on Twitter. com. Paper presented at the international conference on social informaticsGoogle Scholar
  204. 204.
    Yi Y, Zhang Z, Gan C (2018) The effect of social tie on information diffusion in complex networks. Physica A 509:783–794MathSciNetGoogle Scholar
  205. 205.
    Yin H, Cui B, Chen L, Hu Z, Zhou X (2015) Dynamic user modeling in social media systems. ACM Trans Inf Syst 33(3):10Google Scholar
  206. 206.
    Zarrinkalam F, Kahani M, Bagheri E (2018) Mining user interests over active topics on social networks. Inf Process Manag 54(2):339–357Google Scholar
  207. 207.
    Zarrinkalam F, Kahani M, Bagheri E (2019) User interest prediction over future unobserved topics on social networks. Inform Retrieval J 22(1–2):93–128Google Scholar
  208. 208.
    Zhang X, Su Y, Qu S, Xie S, Fang B, Yu P (2018) IAD: interaction-aware diffusion framework in social networks. IEEE Trans Knowl Data EngGoogle Scholar
  209. 209.
    Zhao WX, Jiang J, Weng J, He J, Lim E-P, Yan H, Li X (2011) Comparing twitter and traditional media using topic models. Paper presented at the European Conference on Information RetrievalGoogle Scholar
  210. 210.
    Zhao Z, Cheng Z, Hong L, Chi EH (2015) Improving user topic interest profiles by behavior factorization. Paper presented at the proceedings of the 24th international conference on world wide webGoogle Scholar
  211. 211.
    Zhong E, Fan W, Wang J, Xiao L, Li Y (2012) Comsoc: adaptive transfer of user behaviors over composite social network. Paper presented at the proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  212. 212.
    Zhou E, Li D, Madden A, Chen Y, Ding Y, Kang Q, Su H (2019) Modeling adoption behavior for innovation diffusion. Paper presented at the International Conference on InformationGoogle Scholar
  213. 213.
    Zhu Z (2013) Discovering the influential users oriented to viral marketing based on online social networks. Physica A 392(16):3459–3469MathSciNetzbMATHGoogle Scholar
  214. 214.
    Zhu Z, Su J, Kong L (2015) Measuring influence in online social network based on the user-content bipartite graph. Comput Hum Behav 52:184–189Google Scholar
  215. 215.
    Zhuang K, Shen H, Zhang H (2017) User spread influence measurement in microblog. Multimed Tools Appl 76(3):3169–3185Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of computer and Information Technology Engineering, Qazvin BranchIslamic Azad UniversityQazvinIran
  2. 2.Computer Engineering Department, Science and Research BranchIslamic Azad UniversityTehranIran
  3. 3.Department of Information TechnologyICT Research Institute (Iran Telecommunication Research Center)TehranIran

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