Skip to main content

Yes, Echo-Chambers Mislead You Too: A Game-Based Educational Experience to Reveal the Impact of Social Media Personalization Algorithms

  • Conference paper
  • First Online:
Higher Education Learning Methodologies and Technologies Online (HELMeTO 2022)

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.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 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. 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. 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. 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. 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

  1. Haucap, J., Heimeshoff, U.: Google, facebook, amazon, ebay: is the internet driving competition or market monopolization? IEEP 11(1), 49–61 (2014)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Eli Pariser. The filter bubble: What the Internet is hiding from you. penguin UK, 2011

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Sunstein, C.R.: The law of group polarization. University of Chicago Law School, John M. Olin Law & Economics Working Paper, (91) (1999)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. Bulger, M., Davison, P.: The promises, challenges, and futures of media literacy. J. Media Literacy Educ. 10(1), 1–21 (2018)

    Article  Google Scholar 

  10. 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

  11. Lorenz, J., Rauhut, H., et al.: How social influence can undermine the wisdom of crowd effect. Proc. National Acad. Sci. 108(22) (2011)

    Google Scholar 

  12. Becker, J., Brackbill, D., et al.: Network dynamics of social influence in the wisdom of crowds. Proc. Natl. Acad. Sci. 114(26) (2017)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Ribble, M.: Digital citizenship in schools: Nine elements all students should know. Int. Soc. Technol. Educ. (2015)

    Google Scholar 

  15. Potter, W.J.: The state of media literacy. J. Broadcasting Electron. Media 54(4), 675–696 (2010)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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

  22. 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

  23. 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)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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

  30. Burke, R.D., Abdollahpouri, H., Mobasher, B., Gupta, T.: Towards multi-stakeholder utility evaluation of recommender systems. UMAP (Extended Proceedings), 750 (2016)

    Google Scholar 

  31. 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

    Article  Google Scholar 

  32. Boeker, M., Urman, A.: An empirical investigation of personalization factors on tiktok. In: Proceedings of the ACM Web Conference 2022, pp. 2298–2309 (2022)

    Google Scholar 

  33. 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)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. 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)

    Article  Google Scholar 

  36. Bakshy, E., Messing, S., Adamic. L.A.: Exposure to ideologically diverse news and opinion on facebook. Science 348(6239), 1130–1132 (2015)

    Google Scholar 

  37. Galton, F.: Vox populi (the wisdom of crowds). Nature 75(7), 450–451 (1907)

    Article  MATH  Google Scholar 

  38. 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)

    Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. 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)

    Google Scholar 

  41. Mavrodiev, P., Schweitzer, F.: The ambigous role of social influence on the wisdom of crowds: an analytic approach. Physica A 567, 125624 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  42. Kunda, Z.: The case for motivated reasoning. Psychol. Bull. 108(3), 480 (1990)

    Article  Google Scholar 

  43. Becker, J., Porter, E., Centola, D.: The wisdom of partisan crowds. Proc. Natl. Acad. Sci. 116(22), 10717–10722 (2019)

    Article  Google Scholar 

  44. 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)

    Google Scholar 

  45. 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

  46. 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)

    Google Scholar 

  47. 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)

    Google Scholar 

  48. 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)

    Google Scholar 

Download references

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

Authors

Corresponding authors

Correspondence to Davide Taibi or Dimitri Ognibene .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics