Research Article

How Media Influences Public Perception of AI Applications in Education-A Research on YouTube Comments

Xinyan YaoNanjing Normal University* Chenkai HuShanghai Qibaodwight High School Jiachenan YuZhejiang NO.2 High School Chuqiao LiJianping High School

* Corresponding author: [email protected]

Abstract

How do media influence public perception? In the growing field of AI applications in education, the audience’s opinions and sentiments are becoming increasingly important, as they are closely related to the media agenda-setting process. Nowadays, as social media penetrates people's lives, the influence of media on audience cognition is becoming more extensive and in-depth. Using qualitative and quantitative methods, the article will research YouTube comments on related videos released by different types of channels: the official channels and personal channels. In this work, data science technology will assist in analyzing the themes and sentiments within public perception. The agenda-setting characteristics and effects of different types of channels will be described, and the framing theory will be integrated to aid the analysis process, providing a more comprehensive view of the interaction between the media agenda and the public agenda, helping education industry insiders or professionals involved in the situation of “Artificial Intelligence in Education" understand the audience's views.

Keywords: Agenda-Setting; Frame; YOUTUBE; Public Perception; Education
Published: May 30, 2025
DOI: 10.54254/2753-7064/2024.23418
Volume: CHR Vol.68
pp. 97-123
Download PDF

References

  1. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
  2. Chaudhry, M. A., & Kazim, E. (2022). Artificial Intelligence in Education (AIEd): A high-level academic and industry note 2021. AI and Ethics, 2(1), 157–165. https://doi.org/10.1007/s43681-021-00074-z
  3. Zhai, X., & Lu, M. (2023). Editorial: Machine learning applications in educational studies. Frontiers in Education, 8, 1225802. https://doi.org/10.3389/feduc.2023.1225802
  4. Huang, L. (2023). Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection. Science Insights Education Frontiers, 16(2), 2577–2587. https://doi.org/10.15354/sief.23.re202
  5. Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
  6. Grover, P., Kar, A. K., & Dwivedi, Y. (2022). The evolution of social media influence—A literature review and research agenda. International Journal of Information Management Data Insights, 2(2), 100116. https://doi.org/10.1016/j.jjimei.2022.100116
  7. Zhou, S., Yang, X., Wang, Y., Zheng, X., & Zhang, Z. (2023). Affective agenda dynamics on social media: Interactions of emotional content posted by the public, government, and media during the COVID-19 pandemic. Humanities and Social Sciences Communications, 10(1), 1–10. https://doi.org/10.1057/s41599-023-02265-x
  8. Liu, Yuwei. (2022). A study of attributional bias in the construction of professional media and self-media frames(Master's dissertation, South Central University for Nationalities). Master's degree. https://link.cnki.net/doi/10.27710/d.cnki.gznmc.2022.000543 doi:10.27710/d.cnki.gznmc.2022.000543.
  9. Zhu Chen. (2022). Media Functions in Major Public Crises (Master's thesis, Shanghai Jiao Tong University).Masterhttps://link.cnki.net/doi/10.27307/d.cnki.gsjtu.2022.000297 doi:10.27307/d.cnki.gsjtu.2022.000297.
  10. Wang, Hanxiao. (2020). A Study on the Relationship between Explicit and Implicit Agenda Networks among Media in Vaccine Safety Issues (Doctoral dissertation, Nanjing Normal University). PhD. https://link.cnki.net/doi/10.27245/d.cnki.gnjsu.2020.000060 doi:10.27245/d.cnki.gnjsu.2020.000060.
  11. Dong, Z. (2021). A Study on the Agenda Setting of “Comfort Women” in Taiwan Media (Master's thesis, Shanghai Normal University). Master's degree. https://link.cnki.net/doi/10.27312/d.cnki.gshsu.2021.000251 doi:10.27312/d.cnki.gshsu.2021.000251.
  12. Liang, W., Huang, Y., Tang, X., & Lin, X. (2022). Agenda-Setting of the Public Health Emergencies (COVID-19) Based on Audience’s Perspective. 2022 7th International Conference on Multimedia Systems and Signal Processing (ICMSSP), 80–84. https://doi.org/10.1145/3545822.3545838
  13. McCosker, A. (2014). Negotiating Illness Bloggers’ Expressive Worlds: Adapting Digital Ethnography. SAGE Publications, Ltd. https://doi.org/10.4135/978144627305014529501
  14. González Canché, M. S. (2023). Machine driven classification of open-ended responses (MDCOR): An analytic framework and no-code, free software application to classify longitudinal and cross-sectional text responses in survey and social media research. Expert Systems with Applications, 215, 119265. https://doi.org/10.1016/j.eswa.2022.119265
  15. González Canché, M. S. (2023). Latent Code Identification (LACOID): A Machine Learning-Based Integrative Framework [and Open-Source Software] to Classify Big Textual Data, Rebuild Contextualized/Unaltered Meanings, and Avoid Aggregation Bias. International Journal of Qualitative Methods, 22, 160940692211449. https://doi.org/10.1177/16094069221144940
  16. Mao, H., Yuan, Z., Xu, H., Yu, W., Liu, Y., & Gao, K. (2022). M-SENA: An Integrated Platform for Multimodal Sentiment Analysis (arXiv:2203.12441). arXiv. http://arxiv.org/abs/2203.12441
  17. Nandwani, P. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining.
  18. Braun,V., Clarke,V.,(2006).Using thematic analysis in psychology, Qualitative Research in Psychology, 10.1191/1478088706qp063oa
  19. Nowell L. S., Norris J. M., White D. E., & Moules N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16(1), 160940691773384. https://doi.org/10.1177/1609406917733847
  20. King,(2004),Epistemic trespass: Qualitative research from a quantitative perspective,10.1093/ijpp/riab026
  21. Bai, Q., Dan, Q., Mu, Z., & Yang, M. (2019). A Systematic Review of Emoji: Current Research and Future Perspectives. Frontiers in Psychology, 10, 2221. https://doi.org/10.3389/fpsyg.2019.02221
  22. Rinker, T. W. (2018). lexicon: Lexicon Data version 1.2.1. http://github.com/trinker/lexicon(Rinker, 2016/2024)
  23. Wickham, H. (2009). stringr: Simple, Consistent Wrappers for Common String Operations (p. 1.5.1) [Dataset]. https://doi.org/10.32614/CRAN.package.stringr
  24. Wilbur, W., & Sirotkin, K. (1992). The automatic identification of stop words. Journal of Information Science, 18, 45–55. https://doi.org/10.1177/016555159201800106
  25. Feinerer, I., & Hornik, K. (2007). tm: Text Mining Package (p. 0.7-14) [Dataset]. https://doi.org/10.32614/CRAN.package.tm
  26. Dplyr.pdf. (n.d.). Retrieved August 23, 2024, from https://cran.r-project.org/web/packages/dplyr/dplyr.pdf
  27. So, H.-J., Jang, H., Kim, M., & Choi, J. (2024). Exploring public perceptions of generative AI and education: Topic modelling of YouTube comments in Korea. Asia Pacific Journal of Education, 44(1), 61–80. https://doi.org/10.1080/02188791.2023.2294699
  28. Zhang, Yujue. (2023). Framework Construction and Audience Acceptance of Climate Change Short Videos on Jieyin Platform (Master's Dissertation, Shanghai International Studies University).M.A. https://link.cnki.net/doi/10.27316/d.cnki.gswyu.2023.000548 doi:10.27316/d.cnki.gswyu.2023.000548.
  29. Rodriguez,L. & Dimitrova,V., (2011),The levels of visual framing,10.1080/23796529.2011.11674684
  30. Gilardi, F., Gessler, T., Kubli, M., & Müller, S. (2022). Social Media and Political Agenda Setting. Political Communication, 39(1), 39–60. https://doi.org/10.1080/10584609.2021.1910390
  31. Wang, Y.. (2021). Self-media Reporting in Public Emergencies (Master's thesis, Sichuan University). Master https://link.cnki.net/doi/10.27342/d.cnki.gscdu.2021.007109 doi:10.27342/d.cnki.gscdu.2021.007109.
  32. Xia, Huiyi. (2014). A study of microblogging discourse game during public emergencies. News Knowledge (11), 27-29.
  33. Peng, Shu. (2016). Analysis of political communication effect and limitations of microblog in emergencies. Legal Expo(18), 310-311.
  34. Jones-Jang et al., (2020).Diversifying or Reinforcing Science Communication? Examining the Flow of Frame Contagion Across Media Platforms,10.1177/1077699019874731
  35. Wang, Hanxiao. (2020). A Study on the Relationship between Explicit and Implicit Agenda Networks among Media in Vaccine Safety Issues (Doctoral dissertation, Nanjing Normal University). PhD. https://link.cnki.net/doi/10.27245/d.cnki.gnjsu.2020.000060 doi:10.27245/d.cnki.gnjsu.2020.000060.
  36. Guo, L et al.,(2023), Do News Frames Really Have Some Influence in the Real World? A Computational Analysis of Cumulative Framing Effects on Emotions and Opinions About Immigration, 10.1177/19401612231204535