The Dynamics of Textual Content on Social Media Öffentlichkeit
Zhong, Ning (Spring 2019)
Published
Abstract
While social media have emerged as open sources of insights for both marketing
researchers and practitioners, much of the work on the dynamics in social media activity has
focused on numeric metrics such as volume and valence. Exiting literature on the usergenerated
content (UGC) on social media has begun to explore its potential to yield
marketing insights, but little has been done to consider how the textual content on social
media may shift over time. The goal of this dissertation work is to find out how the textbased
UGC on social media evolves over time by extending the topic modeling framework of
latent Dirichlet allocation (LDA) in three empirical scenarios. In the first essay, a discretestate
dynamic topic model that incorporates multiple latent changepoints is developed to
capture the underlying shifts of textual content that relates to a brand on social media
around an event, such as a brand crisis, a new product release, or breaking news. This
model may be used by marketers to actively monitor online conversations surrounding their
own brands by detecting changes in the topics discussed on social media. In the second
essay, a continuous-state dynamic topic model is proposed to examine the evolution of topic
prevalence and evaluations in customer reviews on a multi-generational product. The
findings show that the concerns of the review contributors at the early stage of product
lifecycle are different from those of the review contributors at the later stage.
Table of Contents
INTRODUCTION......................................................................................................................... 1
ESSAY 1
Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model ........ 6
Abstract ....................................................................................................................................... 6
Introduction ................................................................................................................................. 7
Related Literature ....................................................................................................................... 9
Model Development ................................................................................................................. 13
Empirical Applications ............................................................................................................. 20
Discussion ................................................................................................................................. 32
References ................................................................................................................................. 36
Appendix A.1. MCMC Algorithm for LDA-LC ....................................................................... 55
Appendix A.2. Simulation Study .............................................................................................. 59
Appendix A.3. Most Relevant Words and Prevalence of Topics .............................................. 62
ESSAY 2
The Evolution of Online Reviews: A Dynamic Topic Model for Multiple Text Streams ............. 66
Abstract ..................................................................................................................................... 66
Introduction ............................................................................................................................... 67
Related Literature ..................................................................................................................... 70
Data ........................................................................................................................................... 72
Model Development ................................................................................................................. 75
Results ....................................................................................................................................... 81
Conclusion ................................................................................................................................ 88
Reference .................................................................................................................................. 92
Appendix A.1. Collapsed Gibbs Sampler for SLDA .............................................................. 106
Appendix A.2. MCMC Algorithm for SLDA-MS .................................................................. 108
Appendix A.3. In-sample Model Fit ........................................................................................112
Appendix A.4. Results and Discussions of the Remaining Categories ...................................113
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