Dynamics of User and Platform Recognition
(A2026-134305)

AUTHORS

Keyvan Dehmamy, Goethe University Frankfurt

ABSTRACT

By using Yelp Open data of over 22K active users from 2012-2022, with at least one year of Elite status, their reviews and reactions to their reviews, I built a dynamic model to postulate the dynamics of platform and user endorsements on users’ platform engagements, lingual and emotional word-associations of their review texts, and reactions to their reviews. I find that user endorsements, such as the count of Useful or Funny, have a long-lasting effect on users’ engagement and the length of their reviews (which is usually a measure of review quality). In contrast, platform endorsements result in only short-term engagement by simultaneously reducing the Usefulness of reviews. The effect of platform endorsement is not a direct explanation of the number of reviews; in the short term, user endorsements are not direct explanatory variables, but become so over time.