[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Understanding the behaviour of online TV users

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The amount of online video content available to us is rapidly increasing. Understanding how people are seeking and consuming this content is a prerequisite for providing good services. This paper investigates whether and how log data can be used to identify information-seeking behaviour in the context of online TV. A study was conducted where 27 participants performed given tasks on two Norwegian online TV sites. The participants were between 20 and 25 years old, and all of them were moderate or heavy users of online TV. Tasks that require both scanning and searching of information were given. Four main types of behaviour were identified in the qualitative data: goal-directed search, goal-directed metadata search followed by consumption, goal-directed search of metadata and video, and explorative behaviour. Detailed log event files were compared to self-reported data describing user’s activities (feedback collected at the end of each task and interviews) and screen captures. Our results indicate that the following four variables in the log files: number of (short navigation sequence, short video watching sequence) pairs, frequency of video search actions, percentage of time spent on sequences of navigate actions and percentage of time spent on watching videos can be used to characterise the four types of behaviour. This work extends previous research on usage of log files in describing user’s behaviour by providing simple way of characterising behaviour of online TV users. In particular, the results might be useful in supporting the personalisation of online TV services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. This is 2.7 followed by 21 zeros.

References

  1. Acharya S, Smith B, Parnes P (2000) Characterizing user access to videos on the world wide web. In: proceedings of multimedia computing and networking San Jose, CA, 2000. SPIE—International Society for Optical Engineering, Bellingham, Wash, pp 130–141

  2. Antonini A, Vignaroli L, Schifanella C, Pensa RG, Sapino ML (2013) MeSoOnTV: a media and social-driven ontology-based TV knowledge management system. In: Paper presented at the proceedings of the 24th ACM conference on hypertext and social media, Paris, France

  3. Baluja S, Seth R, Sivakumar D, Jing Y, Yagnik J, Kumar D, Ravichandran D, Aly M (2008) Video suggestion and discovery for YouTube: taking random walks through the view graph. In: Proceedings of the 17th international world wide web conference, WWW ‘08, Beijing, China, 2008. ACM, New York, NY, USA, pp 895–904

  4. Belkin NJ, Cool C, Stein A, Thiel U (1995) Cases, scripts, and information-seeking strategies: on the design of interactive information retrieval systems. Expert Syst Appl 9(3):379–395

    Article  Google Scholar 

  5. Brandtzæg PB (2010) Towards a unified media-user typology (MUT): a meta-analysis and review of the research literature on media-user typologies. Comput Human Behav 26(5):940–956. doi:10.1016/j.chb.2010.02.008

    Article  Google Scholar 

  6. Brandtzæg PB, Heim J, Karahasanovic A (2011) Understanding the new digital divide-A typology of Internet users in Europe. Int J Hum Comput Stud 69(3):123–138

    Article  Google Scholar 

  7. Chen HM, Cooper MD (2001) Using clustering techniques to detect usage patterns in a web-based information system. J Am Soc Infor Sci Technol 52(11):888–904

    Article  Google Scholar 

  8. Darnell MJ (2007) How do people really interact with tv? Naturalistic observations of digital TV and digital video recorder users. Computers in entertainment (CIE) - Interactive TV archive 5 (2, April/June 2007, Article No. 10)

  9. Elkhatib Y, Killick R, Mu M, Race N (2014) Just browsing? Understanding user journeys in online TV. In: MM ‘14 Proceedings of the ACM international conference on multimedia, Orlando, FL, USA, 2014. ACM, New York, pp 965–968. doi:10.1145/2647868.2654980

  10. Szabo G, Huberman BA (2010) Predicting the popularity of online content. Commun ACM 53(8):80–88

    Article  Google Scholar 

  11. Gantz J, Reinsel D (2010) The digital universe decade—are you ready?

  12. Gutschmidt A (2013) Classification of the user tasks by the user behaviour. Empirical Studies on the Usage of On-line Newspapers, Logos

    Google Scholar 

  13. Gutschmidt A, Cap CH (2008) User behaviour under the microscope. In: Paper presented at the WEBIST 2008, proceedings of the fourth international conference on web information systems and technologies, Funchal, Madeira, Portugal, May 4–7

  14. Hayes AF, Krippendorff K (2007) Answering the call for a standard reliability measure for coding data. Commun Methods Meas 1:77–89

    Article  Google Scholar 

  15. Hei X, Liang C, Liang J, Liu Y (2007) A measurement study of a large-scale P2P IPTV system. IEEE Trans Multimed 9(8):1672–1687

    Article  Google Scholar 

  16. Herder E (2007) An analysis of user behaviour on the web. VDM Verlag Dr. Müller e. K. und Lizenzgeber, Saarbrüchen

  17. Infogineering (2014) Understanding Information Overload. http://www.infogineering.net/understanding-information-overload.htm. Accessed 20 Dec 2014

  18. Karahasanovic A, Lüders M, Terradillos E, Alejandro M, Rodríguez J, Núñez JM, Flórez DR (2012) Insight into usage of multimedia streaming services. IADIS Int J WWW/Internet 10(1):18

    Google Scholar 

  19. Krippendorff K (2004) Content analysis: an introduction to its methodology. Sag, Thousand Oaks

    Google Scholar 

  20. Kunert T (2009) User tasks and requirements for iTV applications. In: In user- centered interaction design patterns for interactive digital television applications, human-computer interaction series, Springer London, pp 85–98

  21. Pace S (2003) A grounded theory of flow experiences of Web users. Int J Human-Comput Interact 60(2004):327–363

    Google Scholar 

  22. Rautiainen M, Heikkinen A, Sarvanko J, Chorianopoulos K, Kostakos V, Ylianttila M (2013) Time shifting patterns in browsing and search behavior for catch-up TV on the web. Paper presented at the Conference: 11th European conference on interactive TV and video (EuroITV’13)

  23. Sacchi S, Burigo M (2008) Strategies in the information search process: interaction among task structure. Knowl Sour J Gen Psychol 135(3):252–270

    Article  Google Scholar 

  24. Stenmark D (2008) Identifying clusters of user behaviour in intranet search engine log files. J Am Soc Inform Sci Technol 59(14):2232–2243

    Article  Google Scholar 

  25. UOK (2009) University of Kent, Digital Antropology Report 2009. http://www.antropologi.info/blog/anthropology/2009/digital-anthropology-report

  26. Vilas M, Paneda XG, Garcia R, Melendi D, Garcia VG (2005) User behavior analysis of a video-on-demand service with a wide variety of subjects and lengths In: software engineering and advanced applications, 2005. 31st EUROMICRO conference on 30 Aug–3 Sept. 2005. IEEE, pp 330–337. doi:10.1109/EUROMICRO.2005.63

  27. YouTube (2015) Statistics. https://www.youtube.com/yt/press/statistics.html

  28. Yu H, Zheng D, Zhao BY, Zheng W (2006) Understanding user behaviour in large-scale video-on-demand systems. In: 1st ACM SIGOPS/EuroSys European conference on computer systems 2006 Leuven, Belgium, April 18–21 2006. ACM, pp 333–344

Download references

Acknowledgments

This research is funded by the VERDIKT programme of the Research Council of Norway (CELTIC research project R2D2 Networks, Contract Nr. 193018) and by the Center for Service Innovation (Norwegian Research Council). We would like to thank all the participants in our study as well as our project partners. We thank Ida Maria Haugstveit, Maria Borén, Karen Ranestad and Ragnhild Halvorsrud for their help in the experiment conduct and to the anonymous reviewers for their useful suggestions.

Author information

Authors and Affiliations

  1. SINTEF ICT, Oslo, Norway

    Amela Karahasanović & Jan Heim

Authors
  1. Amela Karahasanović
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Heim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amela Karahasanović.

Appendix

Appendix

Scoring reliability

The results from applying the Krippendorff KALPHA procedure on four participants were scored by two judges.

The analysis was based on [14]. The first four participants with complete log files provided the data for the reliability analysis. Two coders used an initial version of the coding scheme. In the final analysis a slight modification of this scheme was used. Each participant completed a total of 10 tasks, including warm-up tasks. The coding scheme had a total of 48 categories. Each category could be coded zero, one or several times within each task. The basic data were the number of times a specific category was used within a task. Each participant had total of 480 scores. Analysing the four participants together gave a data set of 1920 values, scored by the two coders. As pointed out by Hayes and Krippendorff [14], “In its two-observer interval data version, alpha equals Pearson intraclass-correlation coefficient”.

In the analysis the measurement level was set at intervals (level set to 3 in the analysis). If one of the judges had used the category zero times within the task, that unit was left out of the analysis. Bootstraps were set to 1000.

Krippendorff alpha was 0.8727. As pointed out by Krippendorff [19] pp. 241–243, “social scientists commonly rely on data with reliabilities α ≥ 0.800, consider data with 0.800 > α ≥ 0.667 only to draw tentative conclusions, and discard data whose agreement measures α < 0.667” (Table 7).

Table 8 lists the results from the questionnaire the participants completed before the test started. Tables 9 and 10 show the differences between the participants with and without valid log data. The nonparametric “median test” was chosen since most of the data distributions were rather skewed. As expected, there were no significant differences between the two groups, although there was a slight tendency that, proportionally, more females than males had valid log data. In Table 9, one can see, for example, that all the participants with valid data went on Facebook daily, while only one of those without valid data did not.

Table 7 Sequence elements
Full size table
Table 8 Frequency of demographic characteristics and Web usage
Full size table
Table 9 Category of participation, compared to questionnaire responses
Full size table
Table 10 Median test of significance for questionnaire answers and category of participation—With valid log data versus without valid log data
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karahasanović, A., Heim, J. Understanding the behaviour of online TV users. Pers Ubiquit Comput 19, 839–852 (2015). https://doi.org/10.1007/s00779-015-0865-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-015-0865-9

Keywords

Search

Navigation