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Attracting Versus Sustaining Attention in the Information Economy

  • Yimiao ZhangEmail author
  • Kim Huat Goh
Conference paper
  • 849 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 328)

Abstract

Attention is a scarce resource possessed by a person. In the information age, we observe the rapid increase of information available and the decrease of individual’s attention. This calls for efficient attention allocation amidst information overload. Prior literature suggests attention allocation is a two-stage process – attracting attention and sustaining attention. In this study, we refer to attention theory from psychology literature to explore what attract users’ attention and why users stay on with particular social media content. We use YouTube as the empirical setting to differentiate attracting attention from sustaining attention and examine factors that influence attracting and sustaining attention. The results of this study show that factors that attract attention are different from the factors that will sustain attention in the information age.

Keywords

Attention economy Attention theory Econometrics Social media YouTube 

References

  1. 1.
    Davenport, T.H., Beck, J.C.: The Attention Economy: Understanding the New Currency of Business. Harvard Business Press, Boston (2001).  https://doi.org/10.1145/376625.376626CrossRefGoogle Scholar
  2. 2.
    Falkinger, J.: Limited attention as a scarce resource in information-rich economies. Econ. J. 118(532), 1596–1620 (2008).  https://doi.org/10.1111/j.1468-0297.2008.02182.xCrossRefGoogle Scholar
  3. 3.
    Goldhaber, M.H.: The attention economy and the net. First Monday 2(4) (1997).  https://doi.org/10.5210/fm.v2i4.519
  4. 4.
    Pedrycz, W., Chen, S.-M.: Social Networks: A Framework of Computational Intelligence, vol. 526. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-02993-1CrossRefGoogle Scholar
  5. 5.
    Simon, H.A.: Designing organizations for an information-rich world. In: Greenberger, M. (ed.) Computers, Communication, and the Public Interest, pp. 40–41. The Johns Hopkins Press, Baltimore (1971)Google Scholar
  6. 6.
    Brown, J.S.: Growing up: digital: how the web changes work, education, and the ways people learn. Change Mag. High. Learn. 32(2), 11–20 (2000).  https://doi.org/10.1080/00091380009601719CrossRefGoogle Scholar
  7. 7.
    Liu, Z.: Reading behavior in the digital environment: changes in reading behavior over the past ten years. J. Documentation 61(6), 700–712 (2005).  https://doi.org/10.1108/00220410510632040CrossRefGoogle Scholar
  8. 8.
    InternetLiveStats (2017). http://www.internetlivestats.com/one-second. Accessed 3 May 2017
  9. 9.
    Figueiredo, F., Benevenuto, F., Almeida, J.M.: The tube over time: characterizing popularity growth of youtube videos. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 745–754. ACM (2011)  https://doi.org/10.1145/1935826.1935925
  10. 10.
    Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010).  https://doi.org/10.1145/1787234.1787254CrossRefGoogle Scholar
  11. 11.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528. ACM (2012).  https://doi.org/10.1145/2187836.2187907
  12. 12.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: the million follower fallacy. ICWSM 10(10–17), 30 (2010)Google Scholar
  13. 13.
    De Vries, L., Gensler, S., Leeflang, P.S.: Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. J. Interact. Mark. 26(2), 83–91 (2012).  https://doi.org/10.1016/j.intmar.2012.01.003CrossRefGoogle Scholar
  14. 14.
    Chatzopoulou, G., Sheng, C., Faloutsos, M.: A first step towards understanding popularity in YouTube. In: INFOCOM IEEE Conference on Computer Communications Workshops, San Diego, CA, USA, pp. 1–6. IEEE (2010).  https://doi.org/10.1109/infcomw.2010.5466701
  15. 15.
    Zhou, R., Khemmarat, S., Gao, L.: The impact of YouTube recommendation system on video views. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 404–410. ACM (2010).  https://doi.org/10.1145/1879141.1879193
  16. 16.
    Cheng, X., Dale, C., Liu, J.: Statistics and social network of YouTube videos. In: 16th International Workshop on Quality of Service, IWQoS 2008, Enschede, Netherlands, pp. 229–238. IEEE (2008).  https://doi.org/10.1109/iwqos.2008.32
  17. 17.
    Susarla, A., Oh, J.-H., Tan, Y.: Social networks and the diffusion of user-generated content: evidence from YouTube. Inf. Syst. Res. 23(1), 23–41 (2012).  https://doi.org/10.1287/isre.1100.0339CrossRefGoogle Scholar
  18. 18.
    Brodersen, A., Scellato, S., Wattenhofer, M.: Youtube around the world: geographic popularity of videos. In: Proceedings of the 21st International Conference on World Wide Web, pp. 241–250. ACM (2012).  https://doi.org/10.1145/2187836.2187870
  19. 19.
    Broadbent, D.: Perception and Communication (1958)Google Scholar
  20. 20.
    Deutsch, J.A., Deutsch, D.: Attention: some theoretical considerations. Psychol. Rev. 70(1), 80 (1963).  https://doi.org/10.1037/h0039515CrossRefGoogle Scholar
  21. 21.
    Norman, D.A.: Toward a theory of memory and attention. Psychol. Rev. 75(6), 522 (1968).  https://doi.org/10.1037/h0026699CrossRefGoogle Scholar
  22. 22.
    Treisman, A.M.: Contextual cues in selective listening. Q. J. Exp. Psychol. 12(4), 242–248 (1960).  https://doi.org/10.1080/17470216008416732CrossRefGoogle Scholar
  23. 23.
    Treisman, A.M.: Selective attention in man. Br. Med. Bull. 20(1), 12–16 (1964).  https://doi.org/10.1093/oxfordjournals.bmb.a070274
  24. 24.
    Buschman, T.J., Miller, E.K.: Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315(5820), 1860–1862 (2007).  https://doi.org/10.1126/science.1138071
  25. 25.
    Connor, C.E., Egeth, H.E., Yantis, S.: Visual attention: bottom-up versus top-down. Curr. Biol. 14(19), R850–R852 (2004).  https://doi.org/10.1016/j.cub.2004.09.041CrossRefGoogle Scholar
  26. 26.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18(1), 193–222 (1995).  https://doi.org/10.1146/annurev.ne.18.030195.001205CrossRefGoogle Scholar
  27. 27.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vis. Res. 40(10), 1489–1506 (2000).  https://doi.org/10.1016/S0042-6989(99)00163-7CrossRefGoogle Scholar
  28. 28.
    Sarter, M., Givens, B., Bruno, J.P.: The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Res. Rev. 35(2), 146–160 (2001).  https://doi.org/10.1016/S0165-0173(01)00044-3CrossRefGoogle Scholar
  29. 29.
    Theeuwes, J.: Top–down and bottom–up control of visual selection. Acta Physiol. (Oxf) 135(2), 77–99 (2010).  https://doi.org/10.1016/j.actpsy.2010.02.006CrossRefGoogle Scholar
  30. 30.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980).  https://doi.org/10.1016/0010-0285(80)90005-5CrossRefGoogle Scholar
  31. 31.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001).  https://doi.org/10.1038/35058500
  32. 32.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001).  https://doi.org/10.1038/35058500CrossRefGoogle Scholar
  33. 33.
    Cepeda, N.J., Cave, K.R., Bichot, N.P., Kim, M.-S.: Spatial selection via feature-driven inhibition of distractor locations. Percept. Psychophys. 60(5), 727–746 (1998).  https://doi.org/10.3758/BF03206059CrossRefGoogle Scholar
  34. 34.
    Theeuwes, J.: Perceptual selectivity for color and form. Percept. Psychophys. 51(6), 599–606 (1992).  https://doi.org/10.3758/BF03211656CrossRefGoogle Scholar
  35. 35.
    Steinman, B.A., Steinman, S.B., Lehmkuhle, S.: Research note transient visual attention is dominated by the magnocellular stream. Vis. Res. 37(1), 17–23 (1997).  https://doi.org/10.1016/S0042-6989(96)00151-4CrossRefGoogle Scholar
  36. 36.
    Leuba, C.: Toward some integration of learning theories: the concept of optimal stimulation. Psychol. Rep. (1955).  https://doi.org/10.2466/pr0.1955.1.g.27CrossRefGoogle Scholar
  37. 37.
    Raju, P.S.: Optimum stimulation level: its relationship to personality, demographics, and exploratory behavior. J. Consum. Res. 7(3), 272–282 (1980).  https://doi.org/10.1086/208815CrossRefGoogle Scholar
  38. 38.
    Steenkamp, J.-B.E., Baumgartner, H.: The role of optimum stimulation level in exploratory consumer behavior. J. Consum. Res. 19(3), 434–448 (1992).  https://doi.org/10.1086/209313CrossRefGoogle Scholar
  39. 39.
    Baumgartner, H., Steenkamp, J.-B.E.: Exploratory consumer buying behavior: conceptualization and measurement. Int. J. Res. Mark. 13(2), 121–137 (1996).  https://doi.org/10.1016/0167-8116(95)00037-2CrossRefGoogle Scholar
  40. 40.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Google Scholar
  41. 41.
    Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM (2006).  https://doi.org/10.1145/1148170.1148204
  42. 42.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010).  https://doi.org/10.1145/1718487.1718520
  43. 43.
    Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-20161-5_34CrossRefGoogle Scholar
  44. 44.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014).  https://doi.org/10.3115/v1/p14-5010
  45. 45.
    Rhoades, S.A.: The Herfindahl-Hirschman index. Fed. Res. Bull. 79, 188 (1993)Google Scholar
  46. 46.
    Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM (2010).  https://doi.org/10.1145/1964858.1964870

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Nanyang Business SchoolNanyang Technological UniversitySingaporeSingapore

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