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.

摘要

基于先前关于评论排名算法的文献和流行意见的作用，我们提出了一种数据挖掘方法来监控不断变化的消费者对社会问题活动的反应。特别是，所提出的方法可以（1）在动态社交媒体环境中识别对社会问题运动的排名最高的评论，然后（2）从纵向角度从排名最高的评论中检索流行意见。为了说明如何使用该方法，我们跟踪了关于五个采用不同框架（即员工赞赏、捐赠、行动号召、一线员工赞赏和品牌推广）。结果表明，民意与捐赠、一线工人赞赏和品牌推广框架产生共鸣，而流行的观点颠覆了员工的赞赏和号召性用语框架。该研究对广告学者和从业者具有重要的方法学意义。