Abstract
We present a digital media literacy activity composed of (i) an educational talk and (ii) a game-based activity. The aim is to support teachers in developing learning activities to increase awareness of social media threats among students. Through this activity students directly experience phenomena like echo chambers and filter bubbles that can be provoked by harmful online interaction dynamics controlled by social media platforms’ recommender systems while remaining invisible to the affected users. Our preliminary findings show that a game-based direct experience, inspired by the wisdom of crowds phenomenon, can increase the perception of social media influence on participants with statistically significant results compared to standard lecture-based activity. We conclude that developing a tool enabling educators and scholars to easily perform the proposed activity can be helpful to improve digital media literacy effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Direct and indirect network effects are one of the main futures of platforms and can be defined as the fact that a platform service gains more value as more people use it. A direct network effect means that if more friends join the network, e.g. Facebook, each user will gain more utility from using it because he can keep in touch with all of his friends. Indirect network effects mean that e.g. if more vendors join Amazon each buyer will on average gain more utility e.g. because he has more chance to find the needed good.
- 2.
Find the Table in Social media use over time in https://www.pewresearch.org/internet/fact-sheet/social-media/, Retrieved on March 2, 2023.
- 3.
Source: Pew Research Center https://www.pewresearch.org/fact-tank/2019/08/23/most-u-s-teens-who-use-cellphones-do-it-to-pass-time-connect-with-others-learn-new-things/. Retrieved on March 2, 2023.
- 4.
Check here https://www.nytimes.com/2022/09/16/technology/gen-z-tiktok-search-engine.html. Source: New York Times. Retrieved on March 2, 2023.
- 5.
Mozilla released a report called YouTube Regrets that highlights how YouTube recommender system cannot act coherently with user’s preferences regarding undesired content https://foundation.mozilla.org/en/youtube/findings/.
References
Haucap, J., Heimeshoff, U.: Google, facebook, amazon, ebay: is the internet driving competition or market monopolization? IEEP 11(1), 49–61 (2014)
O’Callaghan, D., Greene, D., Conway, M., Carthy, J., Cunningham, P.: Down the (white) rabbit hole: the extreme right and online recommender systems. Soc. Sci. Comput. Rev. 33(4), 459–478 (2015)
Eli Pariser. The filter bubble: What the Internet is hiding from you. penguin UK, 2011
Gillani, N., Yuan, A., et al.: Me, my echo chamber, and i: introspection on social media polarization. In: Proceedings of the 2018 World Wide Web Conference, pp. 823–831 (2018)
O’Hara, K., Stevens, D.: Echo chambers and online radicalism: assessing the internet’s complicity in violent extremism. Policy Internet 7(4), 401–422 (2015)
Sunstein, C.R.: The law of group polarization. University of Chicago Law School, John M. Olin Law & Economics Working Paper, (91) (1999)
Valtonen, T., Tedre, M., Mäkitalo, K., Vartiainen, H.: Media literacy education in the age of machine learning. J. Media Literacy Educ. 11(2), 20–36 (2019)
Scolari, C.A., Masanet, M.-J., Guerrero-Pico, M., Establés, M.-J.: Transmedia literacy in the new media ecology: Teens’ transmedia skills and informal learning strategies. EPI 27(4), 801–812 (2018)
Bulger, M., Davison, P.: The promises, challenges, and futures of media literacy. J. Media Literacy Educ. 10(1), 1–21 (2018)
Hernández-Leo, D., Theophilou, E., Lobo, R., Sánchez-Reina, R., Ognibene, D.: Narrative scripts embedded in social media towards empowering digital and self-protection skills. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds.) EC-TEL 2021. LNCS, vol. 12884, pp. 394–398. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86436-1_42
Lorenz, J., Rauhut, H., et al.: How social influence can undermine the wisdom of crowd effect. Proc. National Acad. Sci. 108(22) (2011)
Becker, J., Brackbill, D., et al.: Network dynamics of social influence in the wisdom of crowds. Proc. Natl. Acad. Sci. 114(26) (2017)
Cannon, M., Connolly, S., Parry, R.: Media literacy, curriculum and the rights of the child. Discourse Stud. Cultural Polit. Educ. 43(2), 322–334 (2022)
Ribble, M.: Digital citizenship in schools: Nine elements all students should know. Int. Soc. Technol. Educ. (2015)
Potter, W.J.: The state of media literacy. J. Broadcasting Electron. Media 54(4), 675–696 (2010)
McGrew, S., Byrne, V.L.: Who is behind this? preparing high school students to evaluate online content. J. Res. Technol. Educ. 53(4), 457–475 (2020)
Nagle, J.: Twitter, cyber-violence, and the need for a critical social media literacy in teacher education: a review of the literature. Teach. Teach. Educ. 76, 86–94 (2018)
Taibi, D., Eulantelli, G., Monteleone, V., Schicchi, D., Scifo, L.: An innovative platform to promote social media literacy in school contexts. In: ECEL 2021 20th European Conference on e-Learning, page 460. Academic Conferences International limited (2021)
Gleason, B., Von Gillern, S.: Digital citizenship with social media: participatory practices of teaching and learning in secondary education. J. Educ. Technol. Soc. 21(1), 200–212 (2018)
Harris Hyun-soo Kim: The impact of online social networking on adolescent psychological well-being (wb): a population-level analysis of korean school-aged children. Int. J. Adolesc. Youth 22(3), 364–376 (2017)
Ognibene, D., et al.: Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion. Front. Artif. Intell. 5 (2023). https://doi.org/10.3389/frai.2022.654930. ISSN 2624-8212
Ognibene, D., et al.: Moving beyond benchmarks and competitions: towards addressing social media challenges in an educational context. Datenbank-Spektrum, 1–13 (2023). Springer. https://doi.org/10.1007/s13222-023-00436-3
Tsitsika, A.K., et al.: Online social networking in adolescence: Patterns of use in six european countries and links with psychosocial functioning. J. Adolescent Health 55(1), 141–147 (2014)
Sert, H.P., Başkale, H.: Students’ increased time spent on social media, and their level of coronavirus anxiety during the pandemic predict increased social media addiction. Health Inf. Libraries J. (2022)
Sasahara, K., Chen, W., Peng, H., Ciampaglia, A.F., Menczer, F.:. Social influence and unfollowing accelerate the emergence of echo chambers. J. Comput. Soc. Sci. 4(1), 381–402 (2021)
Cinelli, M., De Francisci, G., Morales, A.G., Quattrociocchi, W., Starnini, M.: The echo chamber effect on social media. Proc. Natl. Acad. Sci. 118(9), e2023301118 (2021)
Bail, C.A., et al.: Exposure to opposing views on social media can increase political polarization. Proc. Natl. Acad. Sci. 115(37), 9216–9221 (2018)
Choolarb, T., Premsmith, J., Wannapiroon, P.: Imagineering gamification using interactive augmented reality to develop digital literacy skills. In: Proceedings of the 2019 the 3rd International Conference on Digital Technology in Education, pp. 39–43 (2019)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Burke, R.D., Abdollahpouri, H., Mobasher, B., Gupta, T.: Towards multi-stakeholder utility evaluation of recommender systems. UMAP (Extended Proceedings), 750 (2016)
Milano, S., Taddeo, M., Floridi, L.: Recommender systems and their ethical challenges. AI Soc. 35(4), 957–967 (2020). https://doi.org/10.1007/s00146-020-00950-y
Boeker, M., Urman, A.: An empirical investigation of personalization factors on tiktok. In: Proceedings of the ACM Web Conference 2022, pp. 2298–2309 (2022)
Zhao, Z.: Analysis on the “douyin (tiktok) mania” phenomenon based on recommendation algorithms. In: E3S Web of Conferences, vol. 235, p. 03029. EDP Sciences (2021)
Kramer, A.D.I., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. 111(24), 8788–8790 (2014)
Brady, W.J., Wills, J.A., Jost, J.T., Tucker, J.A., Van Bavel, J.J.: Emotion shapes the diffusion of moralized content in social networks. Proc. Natl. Acad. Sci. 114(28), 7313–7318 (2017)
Bakshy, E., Messing, S., Adamic. L.A.: Exposure to ideologically diverse news and opinion on facebook. Science 348(6239), 1130–1132 (2015)
Galton, F.: Vox populi (the wisdom of crowds). Nature 75(7), 450–451 (1907)
Fleenor, J.W.: The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economics, societies and nations. Personnel Psychol. 59(4), 982 (2006)
Navajas, J., Niella, T., et al.: Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds. Nat. Hum. Behav. 2(2), 126–132 (2018)
Ostrom, E.: The difference: How the power of diversity creates better groups, firms, schools, and societies. by scott e. page. princeton: Princeton University Press, 2007. 448p. 19.95 paper. Perspectives on Politics, 6(4), 828–829 (2008)
Mavrodiev, P., Schweitzer, F.: The ambigous role of social influence on the wisdom of crowds: an analytic approach. Physica A 567, 125624 (2021)
Kunda, Z.: The case for motivated reasoning. Psychol. Bull. 108(3), 480 (1990)
Becker, J., Porter, E., Centola, D.: The wisdom of partisan crowds. Proc. Natl. Acad. Sci. 116(22), 10717–10722 (2019)
Allen, J., Arechar, A.A., Pennycook, G., Rand, D.G.: Scaling up fact-checking using the wisdom of crowds. Sci. Adv. 7(36), eabf4393 (2021)
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X.: Bias and debias in recommender system: a survey and future directions. arXiv preprint arXiv:2010.03240, 2020
Clark, D.B., Tanner-Smith, E.E., Killingsworth, S.S.: Digital games, design, and learning: a systematic review and meta-analysis. Rev. Educ. Res. 86(1), 79–122 (2016)
Casale, S., Fioravanti, G.: Factor structure and psychometric properties of the Italian version of the fear of missing out scale in emerging adults and adolescents. Addictive behaviors 102 (2020)
Przybylski, A.K., Murayama, K., DeHaan, C.R., Gladwell, V.: Motivational, emotional, and behavioral correlates of fear of missing out. Comput. Hum. Behav. 29(4), 1841–1848 (2013)
Acknowledgements
This work was supported by the project COURAGE: A Social Media Companion Safeguarding and Educating Students funded by the Volkswagen Foundation, grant number 95563.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lomonaco, F., Taibi, D., Trianni, V., Buršić, S., Donabauer, G., Ognibene, D. (2023). Yes, Echo-Chambers Mislead You Too: A Game-Based Educational Experience to Reveal the Impact of Social Media Personalization Algorithms. In: Fulantelli, G., Burgos, D., Casalino, G., Cimitile, M., Lo Bosco, G., Taibi, D. (eds) Higher Education Learning Methodologies and Technologies Online. HELMeTO 2022. Communications in Computer and Information Science, vol 1779. Springer, Cham. https://doi.org/10.1007/978-3-031-29800-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-29800-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29799-1
Online ISBN: 978-3-031-29800-4
eBook Packages: Computer ScienceComputer Science (R0)