The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors

Dinesh Puranam (), Vishal Narayan () and Vrinda Kadiyali ()
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Dinesh Puranam: Marshall School of Business, University of Southern California, Los Angeles, California 90089
Vishal Narayan: NUS Business School, National University of Singapore, Singapore 119245
Vrinda Kadiyali: Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853

Marketing Science, 2017, vol. 36, issue 5, 726-746

Abstract: In 2008, New York City mandated that all chain restaurants post calorie information on their menus. For managers of chain and standalone restaurants, as well as for policy makers, a pertinent goal might be to monitor the impact of this regulation on consumer conversations. We propose a scalable Bayesian topic model to measure and understand changes in consumer opinion about health (and other topics). We calibrate the model on 761,962 online reviews of restaurants posted over eight years. Our model allows managers to specify prior topics of interest such as “health” for a calorie posting regulation. It also allows the distribution of topic proportions within a review to be affected by its length, valence, and the experience level of its author. Using a difference-in-differences estimation approach, we isolate the potentially causal effect of the regulation on consumer opinion. Following the regulation, there was a statistically small but significant increase in the proportion of discussion of the health topic. This increase can be attributed largely to authors who did not post reviews before the regulation, suggesting that the regulation prompted several consumers to discuss health in online restaurant reviews.

Keywords: Bayesian estimation; data mining; word-of-mouth (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (33)

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