Should We Feed the Trolls? Using Marketer-Generated Content to Explain Average Toxicity and Product Usage
我们应该喂食巨魔吗？使用市场生成的内容解释平均毒性和产品使用
Marcelo Vinhal Nepomuceno 1Hooman Rahemi 2Tolga Cenesizoglu 1Laurent Charlin1
作者信息
- 1. HEC Montreal, Canada
- 2. Concordia University, Canada
- 折叠
摘要
营销人员和研究人员认识到社交媒体渠道中营销人员生成的内容(MGC)的重要性和对消费者行为的影响。在这项研究中，作者提出了一种结合无监督和有监督机器学习的MGC分类方法。他们从Facebook、Instagram和Twitter收集了大量帖子，并使用时间序列模型（面板数据向量自动回归）来演示如何使用MGC来解释用户的平均毒性。他们通过研究哪些类型的MGC导致有毒评论以及这些有毒评论如何影响产品使用，为该领域做出了贡献。作者发现，证明产品质量的MGC和旨在创造归属感的MGC更有可能增加平均毒性。此外，作者发现，社交媒体社区中较高的平均毒性导致焦点产品使用量的增加。最后，这些结果为文献做出了贡献，为MGC对产品使用的影响提供了见解。
Abstract
Marketers and researchers recognize the importance and impact on consumer behavior of marketer-generated content (MGC) in social media channels. In this study, the authors present a method to classify MGC using a combination of unsupervised and supervised machine learning. They gather a large data set of posts from Facebook, Instagram, and Twitter and use a time-series model (panel-data vector autoregression) to demonstrate how MGC can be used to explain average toxicity on the part of users. They contribute to the field by examining what types of MGC lead to toxic comments and how these toxic comments impact product usage. The authors find that MGC that demonstrates the quality of products and MGC that is aimed at creating a sense of belonging to a group are more likely to increase average toxicity. Furthermore, the authors find that higher average toxicity in social media communities leads to an increase in usage of the focal product. Finally, the results contribute to the literature by providing insights on the impact of MGC on product usage.
Key words
marketer-generated content/product usage/panel-data vector autoregression/content analysis/toxic comments引用本文复制引用
出版年
2023
NETL
NSTL
Sage