Evolving Consumer Responses to Social Issue Campaigns: A Data-Mining Case of COVID-19 Ads on YouTube

References

• Bikhchandani, S., Hirshleifer, D., & Welch, I. 1992. “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades.” Journal of Political Economy 100 (5): 992–1026.
   Google Scholar
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3(Jan): 993–1022.
   Google Scholar
• Borah, P. 2011. “Conceptual Issues in Framing Theory: A Systematic Examination of a Decade's Literature.” Journal of Communication 61 (2): 246–63. doi:https://doi.org/10.1111/j.1460-2466.2011.01539.x.
   Web of Science ®Google Scholar
• Champlin, S., Y. Sterbenk, K. Windels, and M. Poteet. 2019. “How Brand-Cause Fit Shapes Real World Advertising Messages: A Qualitative Exploration of ‘Femvertising’.” International Journal of Advertising 38 (8): 1240–63. doi:https://doi.org/10.1080/02650487.2019.1615294.
   Web of Science ®Google Scholar
• Creswell, J. W., and C. N. Poth. 2016. Qualitative Inquiry and Research Design: Choosing among Five Approaches. London: Sage Publications.
   Google Scholar
• Duan, W., B. Gu, and A. B. Whinston. 2009. “Informational Cascades and Software Adoption on the Internet: An Empirical Investigation.” MIS Quarterly 33 (1):23–48.
   Web of Science ®Google Scholar
• Entman, R. M. 1993. “Framing: Towards Clarification of a Fractured Paradigm.” Journal of Communication 43 (4): 51–8. doi:https://doi.org/10.1111/j.1460-2466.1993.tb01304.x.
   Web of Science ®Google Scholar
• Feng, Y., H. Chen, and L. He. 2019. “Consumer Responses to Femvertising: A Data-Mining Case of Dove’s “Campaign for Real Beauty” on YouTube.” Journal of Advertising 48 (3): 292–301. doi:https://doi.org/10.1080/00913367.2019.1602858.
   Web of Science ®Google Scholar
• Feng, Y., H. Chen, and H. Y. A. Ahn. 2021. “How Consumers React to Woke Advertising: Methodological Triangulation Based on Social Media Data and Self-Report Data.” Journal of Research in Interactive Marketing 15 (4): 529–48. doi:https://doi.org/10.1108/JRIM-09-2020-0185.
   Web of Science ®Google Scholar
• Fujita, S., H. Kobayashi, and M. Okumura. 2020. “Unsupervised Ensemble of Ranking Models for News Comments Using Pseudo Answers.” Advances in Information Retrieval 12036:133.
   Google Scholar
• Habibabadi, S. K., and P. D. Haghighi. 2019. “Topic Modelling for Identification of Vaccine Reactions in Twitter.” Proceedings of the Australasian Computer Science Week Multiconference, 1–10. doi:https://doi.org/10.1145/3290688.3290735.
   Google Scholar
• Hayes, R. A., and C. T. Carr. 2021. “Getting Called Out: Effects of Feedback to Social Media Corporate Social Responsibility Statements.” Public Relations Review 47 (1): 101962. doi:https://doi.org/10.1016/j.pubrev.2020.101962.
   Web of Science ®Google Scholar
• Islam, T. 2019. “Yoga-Veganism: Correlation Mining of Twitter Health Data.” arXiv preprint arXiv:1906.07668.
   Google Scholar
• Iyengar, S. 1991. Is Anyone Responsible? How Television Frames Political Issues. University of Chicago Press.
   Google Scholar
• Kavuluru, R., M. Ramos-Morales, T. Holaday, A. G. Williams, L. Haye, and J. Cerel. 2016. “Classification of Helpful Comments on Online Suicide Watch Forums.” Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 32–40.
   Google Scholar
• Kharrat, F. B., A. Elkhleifi, and R. Faiz. 2016. “Recommendation System Based Contextual Analysis of Facebook Comment.” 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 1–6. IEEE.
   Google Scholar
• Larsson, A. O. 2018. “Diversifying Likes: Relating Reactions to Commenting and Sharing on Newspaper Facebook Pages.” Journalism Practice 12 (3): 326–43. doi:https://doi.org/10.1080/17512786.2017.1285244.
   Google Scholar
• Lee, E. J. 2012. “That's Not the Way It Is: How User-Generated Comments on the News Affect Perceived Media Bias.” Journal of Computer-Mediated Communication 18 (1): 32–45. doi:https://doi.org/10.1111/j.1083-6101.2012.01597.x.
   Web of Science ®Google Scholar
• Lou, C., S. S. Tan, and X. Chen. 2019. “Investigating Consumer Engagement with Influencer-vs. Brand-Promoted Ads: The Roles of Source and Disclosure.” Journal of Interactive Advertising 19 (3): 169–86. doi:https://doi.org/10.1080/15252019.2019.1667928.
   Google Scholar
• Mahajan, D. K., R. Rastogi, C. Tiwari, and A. Mitra. 2012. “Logucb: An Explore-Exploit Algorithm for Comments Recommendation.” Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 6–15.
   Google Scholar
• Muntinga, D. G., M. Moorman, and E. G. Smit. 2011. “Introducing COBRAs: Exploring Motivations for Brand-Related Social Media Use.” International Journal of Advertising 30 (1): 13–46. doi:https://doi.org/10.2501/IJA-30-1-013-046.
   Web of Science ®Google Scholar
• Nanda, G., K. A. Douglas, D. R. Waller, H. E. Merzdorf, and D. Goldwasser. 2021. “Analyzing Large Collections of Open-Ended Feedback from MOOC Learners Using LDA Topic Modeling and Qualitative Analysis.” IEEE Transactions on Learning Technologies 14 (2): 146–60. doi:https://doi.org/10.1109/TLT.2021.3064798.
   Google Scholar
• Northcutt, C. G., K. A. Leon, and N. Chen. 2017. “Comment Ranking Diversification in Forum Discussions.” Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 327–330.
   Google Scholar
• Reicher, S. D., R. Spears, and T. Postmes. 1995. “A Social Identity Model of Deindividuation Phenomena.” European Review of Social Psychology 6 (1): 161–98. doi:https://doi.org/10.1080/14792779443000049.
   Google Scholar
• Röder, M., A. Both, and A. Hinneburg. 2015. “Exploring the Space of Topic Coherence Measures.” Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 399–408.
   Google Scholar
• Rundus, D. 1971. “Analysis of Rehearsal Processes in Free Recall.” Journal of Experimental Psychology 89 (1): 63–77. doi:https://doi.org/10.1037/h0031185.
   Google Scholar
• Scheufele, D. A., and D. Tewksbury. 2007. “Framing, Agenda Setting, and Priming: The Evolution of Three Media Effects Models.” Journal of Communication 57 (1): 9–20.
   Web of Science ®Google Scholar
• Seth, P. 1979. “Institutional/Image Advertising and Idea/Issue Advertising as Marketing Tools: Some Public Policy Issues.” Journal of Marketing 43 (1): 68–78.
   Google Scholar
• Shi, R., P. Messaris, and J. N. Cappella. 2014. “Effects of Online Comments on Smokers' Perception of Antismoking Public Service Announcements.” Journal of Computer-Mediated Communication 19 (4): 975–90. doi:https://doi.org/10.1111/jcc4.12057.
   PubMed Web of Science ®Google Scholar
• Syed, Shaheen, and Marco Spruit. 2017. “Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation.” 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 165–174. IEEE. doi:https://doi.org/10.1109/DSAA.2017.61.
   Google Scholar
• Van Gorp, B., and B. Vyncke. 2021. “Deproblematization as an Enrichment of Framing Theory: Enhancing the Effectiveness of an Awareness-Raising Campaign on Child Poverty.” International Journal of Strategic Communication 15 (5): 425–39. doi:https://doi.org/10.1080/1553118X.2021.1988615.
   Google Scholar
• Yoon, G., C. Li, Y. Ji, M. North, C. Hong, and J. Liu. 2018. “Attracting Comments: Digital Engagement Metrics on Facebook and Financial Performance.” Journal of Advertising 47 (1): 24–37. doi:https://doi.org/10.1080/00913367.2017.1405753.
   Web of Science ®Google Scholar
• Zhang, Q., W. Wang, and Y. Chen. 2020. “Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments.” Marketing Science 39 (2): 285–95. doi:https://doi.org/10.1287/mksc.2019.1215.
   Web of Science ®Google Scholar