Skip to main content
SpringerLink
Log in

Evaluation of Media-Based Social Interactions: Linking Collective Actions to Media Types, Applications, and Devices in Social Networks

  • Chapter
  • First Online:
Online Collective Action

Part of the book series: Lecture Notes in Social Networks ((LNSN))

  • 2024 Accesses

Abstract

There is a growing number of opportunities for users to perform collective actions in social networks: Such collective actions engage users in correspondents social interactions. Although some models for representing users and their relationships in social networks have been proposed, to the best of our knowledge, these models do not explain what the underlying social interactions are. In previous work, we have proposed a human-readable technique for modeling and measuring social interactions, which resulted from users’ actions that involved, for instance, media types, interaction devices, and viral content. In our technique, social interactions are represented as behavioral contingencies in the form of if-then rules, which are then measured using an established data mining procedure. After being able to represent and measure a variety of social interactions, we identified the opportunity of transforming our technique into a method for capturing, representing, and measuring collective actions in social networks. In this chapter, we present our method and detail how it was applied to represent and measure social interactions among a group of 1,600 Facebook users over the period of 7 months. Our results report the link among actions (e.g., like), media objects (e.g., photo), application type (Web or mobile), and device type (e.g., Android).

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    www.python.org

  2. 2.

    Most users (954) are from Brazil. Other users come from a variety of countries, such as USA, Canada, Mexico, Argentina, Uruguay, Colombia, England, Portugal, Spain, France, Italy, Belgium, Holland, Russia, Czech Republic, Kosovo, Israel, Turkey, Australia, New Zealand, among others.

  3. 3.

    We have built a separate Facebook network in which each of the users have both accepted friendship and explicitly authorized the use of information associated with their social interactions.

References

  • Abrol S, Khan L (2010) Tweethood: agglomerative clustering on fuzzy k-closest friends with variable depth for location mining. In: IEEE international conference on social computing (SocialCom). Minneapolis, MN, pp 153–160

    Google Scholar 

  • Azevedo PJ, Jorge AM (2007) Comparing rule measures for predictive association rules. In: ACM European conference on machine learning (ECML ’07). Berlin, pp 510–517

    Google Scholar 

  • Backstrom L, Bakshy E, Kleinberg J, Lento T, Rosenn I (2011) Center of attention: how Facebook users allocate attention across friends. In: Proceedings of the international AAAI conference on Weblogs and social media

    Google Scholar 

  • Bentley F, Metcalf C (2009) The use of mobile social presence. Pervasive Comp IEEE 8(4):35–41

    Article  Google Scholar 

  • Bigonha CAS, Cardoso TNC, Moro MM, Almeida VAF, Gonçalves MA (2010) Detecting evangelists and detractors on twitter. In: Proceedings of 16th Brazilian symposium on multimedia and Web (WebMedia᾿10), pp 107–114

    Google Scholar 

  • Bonchi F, Castillo C, Gionis A, Jaimes A (2011) Social network analysis and mining for business applications. ACM Trans Intell Syst Technol 2:22:1–22:37

    Article  Google Scholar 

  • Chorianopoulos K (2010) Scenarios of use for sociable mobile TV. In: Marcus A, Sala R, Roibs AC (eds) Mobile TV: customizing content and experience. Springer, London, pp 243–254

    Google Scholar 

  • Cowan LG, Weibel N, Pina LR, Hollan JD, Griswold WG (2011) Ubiquitous sketching for social media. In: Proceedings of the 13th international conference on human computer interaction with mobile devices and services. ACM, New York, NY, pp 395–404

    Google Scholar 

  • De Choudhury M, Counts S, Czerwinski M (2011) Identifying relevant social media content: leveraging information diversity and user cognition. In: Proceedings of the 22nd ACM conference on hypertext and hypermedia. ACM, New York, NY, pp 161–170

    Google Scholar 

  • Ekenel H, Semela T (2011) Multimodal genre classification of TV programs and Youtube videos. Multimedia Tools Appl 63:547–567. doi:10.1007/s11042-011-0923-x

    Article  Google Scholar 

  • Fagá R Jr, Motti VG, Cattelan RG, Teixeira CAC, Pimentel MGC (2010) A social approach to authoring media annotations. In: ACM symposium on document engineering (DocEng ᾿10). ACM, New York, pp 17–26

    Google Scholar 

  • Freeman LC (2004) The development of social network analysis: a study in the sociology of science. Empirical Press, Vancouver, CA

    Google Scholar 

  • Fürnkranz J, Gamberger D, Lavrac N (2011) Rule learning: essentials of machine learning and relational data mining, 1st edn. Springer, Dordrecht

    Google Scholar 

  • Geerts D (2010) The sociability of mobile TV. In: Marcus A, Sala R, Roibs AC (eds) Mobile TV: customizing content and experience. Springer, London, pp 25–28

    Google Scholar 

  • Geerts D, Grooff DD (2009). Supporting the social uses of television: sociability heuristics for social TV. In: Proceedings of the 27th international conference on human factors in computing systems, CHI 2009. ACM, New York, pp 595–604

    Google Scholar 

  • Gomes AK, Pimentel MdGC (2011a) Measuring media-based social interactions provided by smartphone applications in social networks. In: Proceedings of the 2011 ACM workshop on social and behavioral networked media access (SBNMA ᾿11). ACM, New York, NY, pp 59–64

    Google Scholar 

  • Gomes AK, Pimentel MdGC (2011b) Measuring synchronous and asynchronous sharing of collaborative annotations sessions on ubi-videos as social interactions. In: Proceedings of the 2011 international conference on Ubi-media computing (U-MEDIA ᾿11). Sao Paulo, pp 122–129

    Google Scholar 

  • Gomes AK, Pimentel MdGC (2011c) Social interactions representation as users behavioral contingencies and evaluation in social networks. In: Proceedings of the 2011 I.E. international conference on semantic computing (ICSC ᾿11). Palo Alto, CA, pp 275–278

    Google Scholar 

  • Gomes AK, Pimentel MdGC (2011d) A technique for human-readable representation and evaluation of media-based social interactions in social networks. In: Proceedings of the 17th Brazilian symposium on multimedia and the Web (WebMedia ᾿11), pp 119–126

    Google Scholar 

  • Gomes AK, Pimentel MdGC (2011e) Um método para análise de interações sociais na web social como regras se-então (in portuguese). In: Workshop sobre Aspectos da Interação Humano-Computador para a Web Social (WAIHCWS ᾿11) no X Simpósio Brasileiro de Fatores Humanos em Sistemas de Computação (IHC ᾿11)

    Google Scholar 

  • Gomes AK, Pimentel MGC (2012a) Measuring media-based social interactions in online civic mobilization against corruption in Brazil. In: Proceedings of the 18th Brazilian symposium on multimedia and the Web (WebMedia ᾿12). ACM, New York

    Google Scholar 

  • Gomes AK, Pimentel MGC (2012b) A media-based social interactions analysis procedure. In: Proceedings of the 27th annual ACM symposium on applied computing (SAC ᾿12). ACM, New York, NY, pp 1018–1024

    Google Scholar 

  • Gomes AK, Pedrosa DdC, Pimentel MdGC (2011) Evaluating asynchronous sharing of links and annotation sessions as social interactions on internet videos. In: Proceedings of the 2011 IEEE/IPSJ international symposium on applications and the internet (SAINT ᾿11). Munich, pp 184–189

    Google Scholar 

  • Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):13–60

    Article  Google Scholar 

  • Han J, Kamber M (2005) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, Amsterdam

    Google Scholar 

  • Holland PW, Leinhardt S (1970) A method for detecting structure in sociometric data. Am J Sociol 76(3):492–513

    Article  Google Scholar 

  • Jin X, Gallagher A, Cao L, Luo J, Han J (2010) The wisdom of social multimedia: using flickr for prediction and forecast. In: ACM international conference on multimedia (MM ᾿10). ACM, New York, pp 1235–1244

    Google Scholar 

  • Kleinberg J (2000) The small-world phenomenon: an algorithm perspective. In: ACM symposium on theory of computing (STOC). ACM, New York, pp 163–170

    Google Scholar 

  • Lavrac N, Flach PA, Zupan B (1999) Rule evaluation measures: a unifying view. In: International workshop on inductive logic programming (ILP). Springer, London, pp 174–185

    Google Scholar 

  • Martínez-Ballesteros M, Riquelme JC (2011) Analysis of measures of quantitative association rules. In: Proceedings of the 6th international conference on hybrid artificial intelligent systems - part II. Springer, Berlin, pp 319–326

    Google Scholar 

  • Mattaini M (1995) Contingency diagrams as teaching tools. J Behav Anal 18:93–98

    Google Scholar 

  • Mechner F (1959) A notation system for the description of behavioral procedures. J Exp Anal Behav 2:133–150

    Article  Google Scholar 

  • Mechner F (2008) Behavioral contingency analysis. J Behav Process 78(2):124–144

    Article  Google Scholar 

  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: ACM conference on internet measurement (ICM). ACM, New York, pp 29–42

    Google Scholar 

  • Negoescu R-A, Adams B, Phung D, Venkatesh S, Gatica-Perez D (2009) Flickr hypergroups. In: ACM international conference on multimedia (MM ᾿09). ACM, New York, pp 813–816

    Google Scholar 

  • Rummel R (1976) Social behavior and interaction – chapter 9. In: Sage Publications (ed) Understanding conflict and war – the conflict. Wiley, New York

    Google Scholar 

  • Scott JP (2000) Social network analysis: a handbook, 2nd edn. Sage, Thousands Oaks, CA

    Google Scholar 

  • Shi X, Li Y, Yu P (2011) Collective prediction with latent graphs, In: Proceedings of the 20th ACM international conference on information and knowledge management. New York, NY, pp 1127–1136

    Google Scholar 

  • Skinner BF (1953) Science and human behavior. New York Press, New York

    Google Scholar 

  • Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison Wesley, Boston

    Google Scholar 

  • Tan S, Bu J, Chen C, Xu B, Wang C, He X (2011) Using rich social media information for music recommendation via hypergraph model. In: ACM transactions on multimedia computing, communications, and applications (TOMCCAP), 7S, pp 22:1–22:22

    Google Scholar 

  • Bourlard H, Vinciarelli A, Pantic M (2009) Social signal processing: survey of an emerging domain. Image Vis Comput 27:1743–1759

    Article  Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Watts DJ (1999) Small worlds: the dynamics of networks between order and randomness. Princeton University Press, Princeton, NJ

    Google Scholar 

  • Weingarten K, Mechner F (1966) The contingency as an independent variable of social interaction. In: Verhave T (ed) Readings of experimental analysis of behavior. Appleton Century Crofts, New York, pp 447–459

    Google Scholar 

  • Wilkinson D, Thelwall M (2011) Researching personal information on the public Web. Soc Sci Comput Rev 29(4):387–401

    Article  Google Scholar 

  • Wilson C, Boe B, Sala A, Puttaswamy KP, Zhao BY (2009) User interactions in social networks and their implications. In: ACM European conference on computer systems. ACM, New York, pp 205–218

    Google Scholar 

Download references

Acknowledgments

We thank CAPES, CNPq, FAPESP, MCT, and FINEP. The author Alan Keller Gomes also thanks PIQS—IFG.

Author information

Authors and Affiliations

  1. Federal Institute of Goiás—IFG, College of Inhumas, Inhumas, GO, Brazil

    Alan Keller Gomes

  2. University of São Paulo, Institute of Mathematics and Computer Science, São Carlos, SP, Brazil

    Alan Keller Gomes & Maria da Graça Campos Pimentel

Authors
  1. Alan Keller Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria da Graça Campos Pimentel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alan Keller Gomes .

Editor information

Editors and Affiliations

  1. Department of Information Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA

    Nitin Agarwal

  2. School of Journalism and Communication, Carleton University, Ottawa, Ontario, Canada

    Merlyna Lim

  3. Departments of Information Science and Business Information Systems, University of Arkansas at Little Rock, Little Rock, USA

    Rolf T. Wigand

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Wien

About this chapter

Cite this chapter

Gomes, A.K., da Graça Campos Pimentel, M. (2014). Evaluation of Media-Based Social Interactions: Linking Collective Actions to Media Types, Applications, and Devices in Social Networks. In: Agarwal, N., Lim, M., Wigand, R. (eds) Online Collective Action. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1340-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-1340-0_5

  • Published:

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1339-4

  • Online ISBN: 978-3-7091-1340-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation