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.
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Notes
- 1.
Meaningful words contain non-function words such as conjunctions, pronouns, and auxiliary verbs.
- 2.
Video category of our dataset includes film & animation, sports, travel & event, people & blogs, comedy, entertainment, howto & style, and education. Video category is based on the video content and is different from channel category.
- 3.
The channels of the dataset are from four countries, US, CA, GB, and CH.
References
Davenport, T.H., Beck, J.C.: The Attention Economy: Understanding the New Currency of Business. Harvard Business Press, Boston (2001). https://doi.org/10.1145/376625.376626
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.x
Goldhaber, M.H.: The attention economy and the net. First Monday 2(4) (1997). https://doi.org/10.5210/fm.v2i4.519
Pedrycz, W., Chen, S.-M.: Social Networks: A Framework of Computational Intelligence, vol. 526. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02993-1
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)
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/10.1080/00091380009601719
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/10.1108/00220410510632040
InternetLiveStats (2017). http://www.internetlivestats.com/one-second. Accessed 3 May 2017
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
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010). https://doi.org/10.1145/1787234.1787254
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
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: the million follower fallacy. ICWSM 10(10–17), 30 (2010)
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.2012.01.003
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
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
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
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.1100.0339
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
Broadbent, D.: Perception and Communication (1958)
Deutsch, J.A., Deutsch, D.: Attention: some theoretical considerations. Psychol. Rev. 70(1), 80 (1963). https://doi.org/10.1037/h0039515
Norman, D.A.: Toward a theory of memory and attention. Psychol. Rev. 75(6), 522 (1968). https://doi.org/10.1037/h0026699
Treisman, A.M.: Contextual cues in selective listening. Q. J. Exp. Psychol. 12(4), 242–248 (1960). https://doi.org/10.1080/17470216008416732
Treisman, A.M.: Selective attention in man. Br. Med. Bull. 20(1), 12–16 (1964). https://doi.org/10.1093/oxfordjournals.bmb.a070274
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
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.2004.09.041
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.18.030195.001205
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)00163-7
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)00044-3
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.2010.02.006
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)90005-5
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001). https://doi.org/10.1038/35058500
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001). https://doi.org/10.1038/35058500
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/BF03206059
Theeuwes, J.: Perceptual selectivity for color and form. Percept. Psychophys. 51(6), 599–606 (1992). https://doi.org/10.3758/BF03211656
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)00151-4
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.27
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/10.1086/208815
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/10.1086/209313
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)00037-2
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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
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
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_34
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
Rhoades, S.A.: The Herfindahl-Hirschman index. Fed. Res. Bull. 79, 188 (1993)
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
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Zhang, Y., Goh, K.H. (2018). Attracting Versus Sustaining Attention in the Information Economy. In: Cho, W., Fan, M., Shaw, M., Yoo, B., Zhang, H. (eds) Digital Transformation: Challenges and Opportunities. WEB 2017. Lecture Notes in Business Information Processing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-99936-4_1
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