Open Access
Issue
SHS Web Conf.
Volume 202, 2024
The 1st International Conference on Environment and Smart Education (ICEnSE 2024)
Article Number 05001
Number of page(s) 8
Section Communication and Technology Adoption
DOI https://doi.org/10.1051/shsconf/202420205001
Published online 14 November 2024
  1. Baer, T., & Baer, T. (2019). Algorithmic Biases and Social Media. Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists, 95–106. [Google Scholar]
  2. Bandy, J., & Diakopoulos, N. (2021). More accounts, fewer links: How algorithmic curation impacts media exposure in Twitter timelines. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–28. [CrossRef] [Google Scholar]
  3. Bansal, G. (2024). Reprogramming the Software of the Mind: A New Framework for Cultural Homogenization. Journal of Global Information Technology Management, 27(1), 1–7. https://doi.org/10.1080/1097198X.2023.229802. [CrossRef] [Google Scholar]
  4. Bruns, A. (2017). Echo chamber? What echo chamber? Reviewing the evidence. 6th Biennial Future of Journalism Conference (FOJ17). [Google Scholar]
  5. Caled, D., & Silva, M. J. (2022). Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation. Journal of Computational Social Science, 5(1), 123–159. [CrossRef] [Google Scholar]
  6. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. [CrossRef] [Google Scholar]
  7. Dahlgren, P. M. (2021). A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review, 42(1), 15–33. [CrossRef] [Google Scholar]
  8. Eg, R., Tønnesen, Ö. D., & Tennfjord, M. K. (2023). A scoping review of personalized user experiences on social media: The interplay between algorithms and human factors. Computers in Human Behavior Reports, 9, 100253. [CrossRef] [Google Scholar]
  9. Franziska, Z., Katrin, S., & Mechtild, S. (2019). Fake news in social media: Bad algorithms or biased users? Journal of Information Science Theory and Practice, 7(2), 40–53. [Google Scholar]
  10. Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018). Me, my echo chamber, and I: introspection on social media polarization. Proceedings of the 2018 World Wide Web Conference, 823–831. [Google Scholar]
  11. Harambam, J., Helberger, N., & Van Hoboken, J. (2018). Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180088. [CrossRef] [Google Scholar]
  12. Karizat, N., Delmonaco, D., Eslami, M., & Andalibi, N. (2021). Algorithmic folk theories and identity: How TikTok users co-produce Knowledge of identity and engage in algorithmic resistance. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–44. [CrossRef] [Google Scholar]
  13. Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS Quarterly, 44(4). [Google Scholar]
  14. Kuang, L., Huang, N., Hong, Y., & Yan, Z. (2019). Spillover effects of financial incentives on non-incentivized user engagement: Evidence from an online knowledge exchange platform. Journal of Management Information Systems, 36(1), 289–320. [CrossRef] [Google Scholar]
  15. Lu, S. (2020). Taming the news feed on Facebook: understanding consumptive news feed curation through a social cognitive perspective. Digital Journalism, 8(9), 1163–1180. [CrossRef] [Google Scholar]
  16. Luzsa, R. (2019). A Psychological and Empirical Investigation of the Online Echo Chamber Phenomenon. Universität Passau. [Google Scholar]
  17. McBrayer, J. P. (2020). Beyond fake news: Finding the truth in a world of misinformation. Routledge. [Google Scholar]
  18. Moon, Y. E., & Lewis, S. C. (n.d.). Social Media as Commodifier or Homogenizer? Journalists’ Social Media Use in Individualistic and Collectivist Cultures and Its Implications for Epistemologies of News Production. Digital Journalism, 1–20. https://doi.org/10.1080/21670811.2024.230398. [Google Scholar]
  19. Nechushtai, E., Zamith, R., & Lewis, S. C. (n.d.). More of the Same? Homogenization in News Recommendations When Users Search on Google, YouTube, Facebook, and Twitter. Mass Communication and Society, 1–27. https://doi.org/10.1080/15205436.2023.217360. [Google Scholar]
  20. Neubaum, G., Cargnino, M., & Maleszka, J. (2021). How Facebook users experience political disagreements and make decisions about the political homogenization of their online network. International Journal of Communication, 15, 20. [Google Scholar]
  21. Nguyen, C. T. (2020). Echo chambers and epistemic bubbles. Episteme, 17(2), 141–161. [CrossRef] [Google Scholar]
  22. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. penguin UK. [Google Scholar]
  23. Pettis, B. T. (2022). Know Your Meme and the homogenization of web history. Internet Histories, 6(3), 263–279. https://doi.org/10.1080/24701475.2021.196865. [CrossRef] [Google Scholar]
  24. Pizolati, A. (2024). Digital Media, Social Bubbles, Extremism and Challenges Implicated in the Construction of Identity and Respect for Diversity and Cultural Pluralism. [Google Scholar]
  25. Purnomo, E. P., Loilatu, M. J., Nurmandi, A., Qodir, Z., Sihidi, I. T., & Lutfi, M. (2021). How Public Transportation Use Social Media Platform during Covid-19: Study on Jakarta Public Transportations’ Twitter Accounts? Webology, 18(1). [Google Scholar]
  26. Qureshi, I., Bhatt, B., Gupta, S., & Tiwari, A. A. (2022). Introduction to the role of information and communication technologies in polarization. In Causes and Symptoms of Socio-Cultural Polarization: Role of Information and Communication Technologies (pp. 1–23). Springer. [Google Scholar]
  27. Ranalli, C., & Malcom, F. (2023). What’s so bad about echo chambers? Inquiry, 1–43. [Google Scholar]
  28. Reed, T. V. (2018). Digitized lives: Culture, power and social change in the internet era. Routledge. [Google Scholar]
  29. Reviglio della Venaria, U. (2020). Personalization in Social Media: Challenges and Opportunities for Democratic Societies. [Google Scholar]
  30. Sapountzi, A., & Psannis, K. E. (2018). Social networking data analysis tools & challenges. Future Generation Computer Systems, 86, 893–913. [CrossRef] [Google Scholar]
  31. Shin, D., Hameleers, M., Park, Y. J., Kim, J. N., Trielli, D., Diakopoulos, N., Helberger, N., Lewis, S. C., Westlund, O., & Baumann, S. (2022). Countering algorithmic bias and disinformation and effectively harnessing the power of AI in media. Journalism & Mass Communication Quarterly, 99(4), 887–907. [CrossRef] [Google Scholar]
  32. Silva, S., & Kenney, M. (2018). Algorithms, platforms, and ethnic bias: An integrative essay. Phylon (1960-), 55(1 & 2), 9–37. [Google Scholar]
  33. Spohr, D. (2017). Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Business Information Review, 34(3), 150–160. [CrossRef] [Google Scholar]
  34. Srisermwongse, V. (2022). New Media and Identity. Pratt Institute. [Google Scholar]
  35. Tucker, J. A., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., & Nyhan, B. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Political Polarization, and Political Disinformation: A Review of the Scientific Literature (March 19, 2018). [Google Scholar]
  36. Van Dijck, J., Poell, T., & De Waal, M. (2018). The platform society: Public values in a connective world. Oxford university press. [Google Scholar]
  37. Wahyuni, H., Purnomo, E. P., & Fathani, A. T. (2021). Social media supports tourism development in the COVID-19 normal era in Bandung. Jurnal Studi Komunikasi, 5(3), 600–616. [Google Scholar]
  38. Wang, R., Bush-Evans, R., Arden-Close, E., Bolat, E., McAlaney, J., Hodge, S., Thomas, S., & Phalp, K. (2023). Transparency in persuasive technology, immersive technology, and online marketing: Facilitating users’ informed decision making and practical implications. Computers in Human Behavior, 139, 107545. [CrossRef] [Google Scholar]
  39. Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public discourse and social network echo chambers driven by socio-cognitive biases. Physical Review X, 10(4), 41042. [CrossRef] [Google Scholar]
  40. Williams, B. A., Brooks, C. F., & Shmargad, Y. (2018). How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. Journal of Information Policy, 8, 78–115. [CrossRef] [Google Scholar]

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