Digital Social Visibility, Anonymity and User Content Generation: Evidence from Natural Experiments

Ni Huang (), Yili Hong and Gordon Burtch
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Ni Huang: Temple University, Fox School of Business, 1801 Liacouras Walk, Philadelphia, PA 19122

No 15-04, Working Papers from NET Institute

Abstract: This study examines how changes in digital social visibility (or conversely, anonymity) can affect the characteristics of user-generated content (volume and linguistic features). We consider natural experiments at two leading online review websites (Yelp.com and TripAdvisor.com), wherein each was integrated with Facebook. Constructing a unique panel dataset of online reviews for a matched set of restaurants across the two review sites, we estimate a multi-treatment difference-in-differences (DID) model to assess the impact of increased digital social visibility (decreased anonymity). We find that integration with Facebook (and thus greater digital social visibility) increased the volume of user-generated content and consumers’ use of affective language processes, while simultaneously decreasing their use of cognitive language processes and controversial language (e.g., sexually explicit, negative and abrasive words). We discuss the implication of these results as they relate to the creation of a civil, sustainable online social platforms for user generated content.

Keywords: Natural experiment; text analytics; online reviews; linguistic characteristics; digital social visibility; social network integration; anonymity; difference-in-differences (search for similar items in EconPapers)
JEL-codes: D12 D80 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2015-10
New Economics Papers: this item is included in nep-ict and nep-mkt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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