Abstract
When people engage in Social Networking Sites, they influence one another through their contributions. Prior research suggests that the interplay between individual differences and environmental variables, such as a person’s openness to conflicting information, can give rise to either public spheres or echo chambers. In this work, we aim to unravel critical processes of this interplay in the context of learning. In particular, we observe high school students’ information behavior (search and evaluation of Web resources) to better understand a potential coupling between confirmatory search and polarization and, in further consequence, improve learning analytics and information services for individual and collective search in learning scenarios. In an empirical study, we had 91 high school students performing an information search in a social bookmarking environment. Gathered log data was used to compute indices of confirmatory search and polarisation as well as to analyze the impact of social stimulation. We find confirmatory search and polarization to correlate positively and social stimulation to mitigate, i.e., reduce the two variables’ relationship. From these findings, we derive practical implications for future work that aims to refine our formalism to compute confirmatory search and polarisation indices and to apply it for depolarizing information services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of WWW 2012, pp. 519–528. ACM (2012)
Bateman, S., Brooks, C., Mccalla, G., Brusilovsky, P.: Applying collaborative tagging to e-learning. In: Proceedings of WWW (2007)
Brown, C., Czerniewicz, L.: Debunking the ‘digital native’: beyond digital apartheid, towards digital democracy. J. Comput. Assist. Learn. 26(5), 357–369 (2010)
Brundidge, J.: Encountering “difference” in the contemporary public sphere: the contribution of the internet to the heterogeneity of political discussion networks. J. Commun. 60(4), 680–700 (2010)
Dahlgren, P.: The internet, public spheres, and political communication: dispersion and deliberation. Polit. Commun. 22(2), 147–162 (2005)
Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C.: No country for old members: user lifecycle and linguistic change in online communities. In: Proceedings of WWW 2013, pp. 307–318. ACM (2013)
De Liddo, A., Buckingham Shum, S.: The evidence hub: harnessing the collective intelligence of communities to build evidence-based knowledge (2013)
DiMaggio, P., Evans, J., Bryson, B.: Have American’s social attitudes become more polarized? Am. J. Sociol. 102(3), 690–755 (1996)
Font, F., Serrà, J., Serra, X.: Analysis of the impact of a tag recommendation system in a real-world folksonomy. ACM Trans. Intell. Syst. Technol. 7(1), 6:1–6:27 (2015)
Fu, W.T.: The microstructures of social tagging: a rational model. In: Proceedings of CSCW 2008, pp. 229–238. ACM (2008)
Fu, W.T., Dong, W.: Collaborative indexing and knowledge exploration: a social learning model. IEEE Intell. Syst. 27(1), 39–46 (2012)
Fu, W.T., Kannampallil, T.G., Kang, R.: A semantic imitation model of social tag choices. In: Proceedings of CSE 2009, vol. 4, pp. 66–73. IEEE (2009)
Gallardo-Echenique, E.E., Marqués-Molías, L., Bullen, M., Strijbos, J.W.: Let’s talk about digital learners in the digital era. Int. Rev. Res. Open Distrib. Learn. 16(3), 156–187 (2015)
Habermas, J., Habermas, J., Mccarthy, T.: The Structural Transformation of the Public Sphere: An Inquiry Into a Category of Bourgeois Society. MIT Press, Cambridge (1991)
Hommel, B.: Convergent and divergent operations in cognitive search. Cogn. Search: Evol. Algorithms Brain 9, 221–235 (2012)
Huckfeldt, R., Mendez, J.M., Osborn, T.: Disagreement, ambivalence, and engagement: the political consequences of heterogeneous networks. Polit. Psychol. 25(1), 65–95 (2004)
Kopeinik, S., Eskandar, A., Ley, T., Albert, D., Seitlinger, P.C.: Adapting an open source social bookmarking system to observe critical information behaviour. In: Proceedings of LILE (2018)
Kopeinik, S., Lex, E., Seitlinger, P., Albert, D., Ley, T.: Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project. In: Proc. of LAK 2017, pp. 409–418. ACM (2017)
Kuhn, A., Cahill, C., Quintana, C., Schmoll, S.: Using tags to encourage reflection and annotation on data during nomadic inquiry. In: Proceedings of CHI 2011, pp. 667–670. ACM (2011)
Ley, T., Seitlinger, P.: Dynamics of human categorization in a collaborative tagging system: how social processes of semantic stabilization shape individual sensemaking. Comput. Hum. Behav. 51, 140–151 (2015)
Lin, N., et al.: The dynamic features of delicious, Flickr, and YouTube. J. Am. Soc. Inform. Sci. Technol. 63(1), 139–162 (2012)
Livingstone, S.: Critical reflections on the benefits of ICT in education. Oxford Rev. Educ. 38(1), 9–24 (2012)
MacKuen, M., Wolak, J., Keele, L., Marcus, G.E.: Civic engagements: resolute partisanship or reflective deliberation. Am. J. Polit. Sci. 54(2), 440–458 (2010)
Marinho, L.B., et al.: Recommender Systems for Social Tagging Systems. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4614-1894-8
McGrew, S., Breakstone, J., Ortega, T., Smith, M., Wineburg, S.: Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory Res. Soc. Educ. 46(2), 165–193 (2018)
Nikolov, D., Oliveira, D.F., Flammini, A., Menczer, F.: Measuring online social bubbles. PeerJ Comput. Sci. 1, e38 (2015)
Pariser, E.: The Filter Bubble: What the Internet is Hiding From You. Penguin, London (2011)
Sarmiento, J.W., Stahl, G.: Group creativity in interaction: collaborative referencing, remembering, and bridging. Intl. J. Hum.-Comput. Interact. 24(5), 492–504 (2008)
Schulz-Hardt, S., Frey, D., Lüthgens, C., Moscovici, S.: Biased information search in group decision making. J. Pers. Soc. Psychol. 78(4), 655 (2000)
Schwind, C., Buder, J.: Reducing confirmation bias and evaluation bias: when are preference-inconsistent recommendations effective-and when not? Comput. Hum. Behav. 28(6), 2280–2290 (2012)
Seitlinger, P., Ley, T., Albert, D.: Verbatim and semantic imitation in indexing resources on the web: a fuzzy-trace account of social tagging. Appl. Cogn. Psychol. 29(1), 32–48 (2015)
Seitlinger, P., et al.: Balancing the fluency-consistency tradeoff in collaborative information search with a recommender approach. Int. J. Hum.-Comput. Interact. 34(6), 557–575 (2018)
Stroud, N.J.: Polarization and partisan selective exposure. J. Commun. 60(3), 556–576 (2010)
Wagner, C., Singer, P., Strohmaier, M., Huberman, B.: Semantic stability and implicit consensus in social tagging streams. IEEE Trans. Comput. Soc. Syst. 1(1), 108–120 (2014)
Wang, Y., Luo, J., Niemi, R., Li, Y.: To follow or not to follow: analyzing the growth patterns of the trumpists on Twitter. In: Proceedings of ICWSM (2016)
Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: collaborative tag suggestions. In: Proceedings of WWW (2006)
Acknowledgements
This work is supported by the Austrian Science Fund (FWF) TCS-034 Project, the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 669074 and by the Know-Center. The Know-Center is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies. We are grateful for the help of Helena Flemming, Kevin Harkim and Marcel Jud in the realization of the school workshops, and the Projects Miles and HELI-D funded the Gesundheitsfond Steiermark.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kopeinik, S., Lex, E., Kowald, D., Albert, D., Seitlinger, P. (2019). A Real-Life School Study of Confirmation Bias and Polarisation in Information Behaviour. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-29736-7_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29735-0
Online ISBN: 978-3-030-29736-7
eBook Packages: Computer ScienceComputer Science (R0)