Although social media has emerged as a popular source of insights for both researchers and practitioners, much of the work on the dynamics in social media has focused on common metrics such as volume and sentiment. In this research, we develop a changepoint model to capture the underlying shifts in social media content. We extend latent Dirichlet allocation (LDA), a topic modeling approach, by incorporating multiple latent changepoints through a Dirichlet process hidden Markov model that allows for the prevalence of topics to differ before and after each changepoint without requiring prior knowledge about the number of changepoints. We demonstrate our modeling framework using social media posts from brand crises (Volkswagen’s 2015 emissions testing scandal and Under Armour’s 2018 data breach) and a new product launch (Burger King’s 2016 launch of the Angriest Whopper). We show that our model identifies shifts in the conversation surrounding each of these events and outperforms both static and other dynamic topic models. We demonstrate how the model may be used by marketers to actively monitor conversations around their brands, including distinguishing between changes in the conversation arising from a shift in the contributor base and underlying changes in the topics discussed by contributors.
|Original language||English (US)|
|Number of pages||20|
|State||Published - Jul 1 2020|
All Science Journal Classification (ASJC) codes
- Business and International Management