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

Abstract

Abstract Based on previous literature on comment-ranking algorithms and the role of popular opinion, we propose a data-mining approach to monitor evolving consumer responses to social issue campaigns. In particular, the proposed approach can (1) identify top-ranked comments on a social issue campaign in the dynamic social media environment and then (2) retrieve popular opinion from the top-ranked comments from a longitudinal perspective. To illustrate how to use the approach, we tracked the development of popular opinion contained in top-ranked comments posted about five COVID-19 brand videos that adopted different frames (i.e., employee appreciation, donation, call to action, frontline worker appreciation, and brand promotion). Results indicated that popular opinion resonates with the donation, frontline worker appreciation, and brand promotion frames, whereas popular opinion subverts the employee appreciation and call-to-action frames. The study has important methodological implications for advertising scholars and practitioners.