Understanding the Dynamics of Filter Bubbles in Social Media Communication: A Literature Review
Barra lateral del artículo
Contenido principal del artículo
Resumen
Introduction: This literature review synthesizes current research on filter bubbles in social media communication, exploring how algorithmic personalization shapes user experiences and informational diversity. Methodology: The review examines theoretical frameworks and empirical studies that identify the mechanisms through which filter bubbles form on platforms such as Facebook, Twitter, and YouTube. Results: Algorithms, driven by user behaviour and engagement metrics, select content that often reinforces pre-existing beliefs, potentially leading to ideological homogeneity. Evidence is presented regarding the prevalence and impact of these bubbles on public discourse, political polarization, and democratic participation. Discussion: Mitigation strategies are considered, including algorithmic transparency, digital literacy initiatives, and platform design modifications aimed at promoting exposure to diverse perspectives. Both supporting and critical viewpoints of these dynamics are evaluated, highlighting the nuanced role of filter bubbles in digital communication. Conclusions: The study underscores the broader societal implications of filter bubbles and calls for continued interdisciplinary research to develop effective solutions that foster informational diversity and a healthy democratic dialogue in the digital age.
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Citas
Ackland, R., O’Neil, M., & Park, S. (2019). Engagement with news on Twitter: Insights from Australia and Korea. Asian Journal of Communication, 29(3), 235-251. https://doi.org/10.1080/01292986.2018.1462393 DOI: https://doi.org/10.1080/01292986.2018.1462393
Badawy, A., Addawood, A., Lerman, K., & Abdul-Mageed, M. (2019). Characterizing the 2016 Russian IRA influence campaign. Social Network Analysis and Mining, 9, Article 31. DOI: https://doi.org/10.1007/s13278-019-0578-6
Bechmann, A., & Nielbo, K. L. (2018). Are we exposed to the same “news” in the news feed? An empirical analysis of filter bubbles as information similarity for Danish Facebook users. Digital Journalism, 6(8), 990-1002. https://doi.org/10.1080/21670811.2018.1510741 DOI: https://doi.org/10.1080/21670811.2018.1510741
Bruns, A. (2019). Filter bubble. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1426 DOI: https://doi.org/10.14763/2019.4.1426
Burbach, L., Halbach, P., Ziefle, M., & Valdez, A. C. (2019). Bubble trouble: Strategies against filter bubbles in online social networks. In V. Duffy (Ed.), Digital human modeling and applications in health, safety, ergonomics and risk management: Healthcare applications (DHM 2019, Pt. II) (Lecture Notes in Computer Science, 11582, pp. 441–456). Springer. https://doi.org/10.1007/978-3-030-22219-2_33 DOI: https://doi.org/10.1007/978-3-030-22219-2_33
Dahlgren, P. M. (2021). A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review, 42(1), 15-33. https://doi.org/10.2478/nor-2021-0002 DOI: https://doi.org/10.2478/nor-2021-0002
Dubois, E., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729-745. https://doi.org/10.1080/1369118X.2018.1428656 DOI: https://doi.org/10.1080/1369118X.2018.1428656
Ferro-Santos, S., Cardoso, G., & Santos, S. (2024). Bursting the (filter) bubble: Interactions of members of parliament on Twitter. Media & Jornalismo, 24(44). https://doi.org/10.14195/2183-5462_44_3 DOI: https://doi.org/10.14195/2183-5462_44_3
Fletcher, R., & Nielsen, R. K. (2017, June 20). Using social media appears to diversify your news diet, not narrow it. Nieman Lab. https://www.niemanlab.org/2017/06/using-social-media-appears-to-diversify-your-news-diet-not-narrow-it/
Graham, T., & Ackland, R. (2017). Do socialbots dream of popping the filter bubble? The role of socialbots in promoting deliberative democracy in social media. In R. Gehl & M. Bakardjieva (Eds.), Socialbots and their friends: Digital media and the automation of sociality (pp. 187-206). Routledge.
Haim, M., Graefe, A., & Brosius, H.-B. (2018). Burst of the filter bubble? Digital Journalism, 6(3), 330–343. https://doi.org/10.1080/21670811.2017.1338145 DOI: https://doi.org/10.1080/21670811.2017.1338145
Jacobson, S., Myung, E., & Johnson, S. L. (2016). Open media or echo chamber: The use of links in audience discussions on the Facebook pages of partisan news organizations. Information, Communication & Society, 19(7), 875-891. https://doi.org/10.1080/1369118X.2015.1064461 DOI: https://doi.org/10.1080/1369118X.2015.1064461
Jones-Jang, S. M., & Chung, M. (2024). Can we blame social media for polarization? Counter-evidence against filter bubble claims during the COVID-19 pandemic. New Media & Society, 26(6), 3370–3389. https://doi.org/10.1177/14614448221099591 DOI: https://doi.org/10.1177/14614448221099591
Kaiser, J., & Rauchfleisch, A. (2020). Birds of a feather get recommended together: Algorithmic homophily in YouTube’s channel recommendations in the United States and Germany. Social media + Society, 6(4). https://doi.org/10.1177/2056305120969914 DOI: https://doi.org/10.1177/2056305120969914
Kanai, A., & McGrane, C. (2021). Feminist filter bubbles: Ambivalence, vigilance and labour. Information, Communication & Society, 24(15), 2307–2322. https://doi.org/10.1080/1369118X.2020.1760916 DOI: https://doi.org/10.1080/1369118X.2020.1760916
Knobloch-Westerwick, S., & Westerwick, A. (2023). Algorithmic personalization of source cues in the filter bubble: Self-esteem and self-construal impact information exposure. New Media & Society, 25(8), 2095–2117. https://doi.org/10.1177/14614448211027963 DOI: https://doi.org/10.1177/14614448211027963
Knudsen, E. (2023). Modeling news recommender systems’ conditional effects on selective exposure: Evidence from two online experiments. Journal of Communication, 73(2), 138-149. https://doi.org/10.1093/joc/jqac047 DOI: https://doi.org/10.1093/joc/jqac047
Lin, H., Wang, Y., Lee, J., & Kim, Y. (2023). The effects of disagreement and unfriending on political polarization: A moderated-mediation model of cross-cutting discussion on affective polarization via unfriending contingent upon exposure to incivility. Journal of Computer-Mediated Communication, 28(4). https://doi.org/10.1093/jcmc/zmad022 DOI: https://doi.org/10.1093/jcmc/zmad022
Lopes, D. F., Frogeri, R. F., de Souza, M. A., & Portugal Junior, P. dos S. (2023). Information bubbles and the relevance of information from social networking sites for Brazilian teenagers.
Teknokultura: Revista de Cultura Digital y Movimientos Sociales, 20(2), 229-238. https://doi.org/10.5209/tekn.79698 DOI: https://doi.org/10.5209/tekn.79698
Meineck, S. (2018, May 28). Deshalb ist “Filterblase” die blödeste Metapher des Internets [Why the “filter bubble” is the internet’s dumbest metaphor]. Motherboard. https://motherboard.vice.com/de/article/pam5nz/deshalb-ist-filterblase-dieblodeste-metapher-des-internets
Mueller, S. D., & Saeltzer, M. (2022). Twitter made me do it! Twitter’s tonal platform incentive and its effect on online campaigning. Information, Communication & Society, 25(9), 1247-1272. https://doi.org/10.1080/1369118X.2020.1850841 DOI: https://doi.org/10.1080/1369118X.2020.1850841
Pariser, E. (2011). The filter bubble: How the new personalized Web is changing what we read and how we think. Penguin Press.
Plettenberg, N., Nakayama, J., Belavadi, P., Halbach, P., Burbach, L., Valdez, A. C., & Ziefle, M. (2020). User behavior and awareness of filter bubbles in social media. In V. Duffy (Ed.), Digital human modeling and applications in health, safety, ergonomics and risk management: Human communication, organization and work (DHM 2020, Pt. II) (Lecture Notes in Computer Science, 12199, pp. 81-92). Springer. https://doi.org/10.1007/978-3-030-49907-5_6 DOI: https://doi.org/10.1007/978-3-030-49907-5_6
Postill, J. (2018). Populism and social media: A global perspective. Media, Culture & Society, 40(5), 754–765. https://doi.org/10.1177/0163443718772186 DOI: https://doi.org/10.1177/0163443718772186
Puschmann, C. (2019). Beyond the bubble: Assessing the diversity of political search results. Digital Journalism, 7(6), 824-843. https://doi.org/10.1080/21670811.2018.1539626 DOI: https://doi.org/10.1080/21670811.2018.1539626
Rhodes, S. C. (2022). Filter bubbles, echo chambers, and fake news: How social media conditions individuals to be less critical of political misinformation. Political Communication, 39(1), 1-22. https://doi.org/10.1080/10584609.2021.1910887 DOI: https://doi.org/10.1080/10584609.2021.1910887
Rodríguez-Ferrándiz, R. (2019). Post-truth and fake news in political communication: A brief genealogy. Profesional de la Información, 28(3). https://doi.org/10.3145/epi.2019.may.14 DOI: https://doi.org/10.3145/epi.2019.may.14
Roechert, D., Weitzel, M., & Ross, B. (2020). The homogeneity of right-wing populist and radical content in YouTube recommendations. In Proceedings of the 11th International Conference on Social Media & Society (SMSociety ’20) (pp. 245–254). Association for Computing Machinery. https://doi.org/10.1145/3400806.3400835 DOI: https://doi.org/10.1145/3400806.3400835
Seargeant, P., & Tagg, C. (2019). Social media and the future of open debate: A user-oriented approach to Facebook’s filter bubble conundrum. Discourse, Context & Media, 27, 41-48. https://doi.org/10.1016/j.dcm.2018.03.005 DOI: https://doi.org/10.1016/j.dcm.2018.03.005
Terren, L., & Borge, R. (2021). Echo chambers on social media: A systematic review of literature. Review of Communication Research, 9, 99-118. https://doi.org/10.12840/ISSN.2255-4165.028 DOI: https://doi.org/10.12840/ISSN.2255-4165.028
Valdez, A. C. (2020). Human and algorithmic contributions to misinformation online: Identifying the culprit. In C. Grimme, M. Preuss, F. Takes, & A. Waldherr (Eds.), Disinformation in open online media (Lecture Notes in Computer Science, 12021, pp. 3-15). Springer. https://doi.org/10.1007/978-3-030-39627-5_1 DOI: https://doi.org/10.1007/978-3-030-39627-5_1
Whittaker, J., Looney, S., Reed, A., & Votta, F. (2021). Recommender systems and the amplification of extremist content. Internet Policy Review, 10(2). https://doi.org/10.14763/2021.2.1565 DOI: https://doi.org/10.14763/2021.2.1565
Wiard, V., Lits, B., & Dufrasne, M. (2022). “The spy who loved me”: A qualitative exploratory analysis of the relationship between youth and algorithms. Frontiers in Communication, 7. https://doi.org/10.3389/fcomm.2022.778273 DOI: https://doi.org/10.3389/fcomm.2022.778273
Yang, C., Nunes, B. P., dos Santos, J. C., Matsui Siqueira, S. W., & Xu, X. (2021). The BiasChecker: How biased are social media searches? In M. Coscia, A. Cuzzocrea, & K. Shu (Eds.), Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2021) (pp. 305–308). Association for Computing Machinery. https://doi.org/10.1145/3487351.3489482 DOI: https://doi.org/10.1145/3487351.3489482
Zuiderveen Borgesius, F., Trilling, D., Möller, J., Bodó, B., de Vreese, C., & Helberger, N. (2016). Should we worry about filter bubbles? Internet Policy Review, 5(1). https://doi.org/10.14763/2016.1.401 DOI: https://doi.org/10.14763/2016.1.401
Related articles
Ceballos del Cid, Y. (2024). Jóvenes y redes sociales: divergencias y similitudes en el consumo informativo en distintas comunidades autónomas españolas. European Public & Social Innovation Review, 9, 1-19. https://doi.org/10.31637/epsir-2024-605 DOI: https://doi.org/10.31637/epsir-2024-605
Egido Piqueras, M. (2024). Ética, identidad y empleabilidad en la revisión de las redes sociales de los candidatos por parte de los empleadores. Revista de Comunicación de la SEECI, 57, 1-20. https://doi.org/10.15198/seeci.2024.57.e889 DOI: https://doi.org/10.15198/seeci.2024.57.e889
López Julca, R., Julca Guerrero, F., Nivin Vargas, L., Allauca Castillo, W., Robles Trejo, L., & Robles Blacido, E. (2024). El filtro burbuja y el derecho a la información en la web. Desde el Sur, 16(1). http://www.scielo.org.pe/scielo.php?pid=S2415-09592024000100017&script=sci_abstract&tlng=pt DOI: https://doi.org/10.21142/DES-1601-2024-0017
Oscuvilca Tapia, E. C., Albitres Infantes, J. J., Cadenas Calderón, P. C., Aguinaga Mendoza, G. M., Paredes Jiménez, H. R., & Andrade Girón, E. C. (2024). Health and medical informatics research: Identifying international collaboration patterns at the country and institution level. Iberoamerican Journal of Science Measurement and Communication, 4(3), 1-16. http://dx.doi.org/10.47909/ijsmc.137 DOI: https://doi.org/10.47909/ijsmc.137
Pérez Ordóñez, C., & Castro-Martínez, A. (2023). Creadores de contenido especializado en salud en redes sociales. Los micro influencers en Instagram. Revista de Comunicación y Salud, 13, 23-38. https://doi.org/10.35669/rcys.2023.13.e311 DOI: https://doi.org/10.35669/rcys.2023.13.e311