Tripadivisor reviews: Users' multiple posts may be tampering with your online reviews research results

Purpose. Online reviews are one of the most common data sources employed in tourism, travel, and hospitality, electronic Word-of-Mouth (eWom) and User Generated Content (UGC) research. One of the more popular review sources is TripAdvisor. However, most studies do not recognize the limitation caused by many users posting multiple reviews for the same place, some of which are duplicate reviews. This limitation can bias research results. Theoretical framework. In probability theory, statistical independence implies that one event does not affect the probability of other event, meaning that one event should not change the belief on another event. Duplicate observations (additional instances of one same observation) or “near duplicates” (observations that share a high number of features with other observations) are a problem in social science studies, because they violate the requirement of statistical independence between observations. Design/methodology/approach. From the analysis of TripAdvisor reviews, collected in three languages (English, Portuguese and Spanish), from twenty attractions in two UNESCO heritage listed cities, we show examples of this multiple/duplicate review publication. Findings. This research shows that the intentional or unintentional publication of multiple reviews, by the same user for the same attraction, is not uncommon. Even though TripAdvisor clearly and publicly advises users against posting more than one review for the same attraction within less than a 90-day interval, this study demonstrates that TripAdvisor’s guidelines are not being adequately enforced. Research, Practical & Social implications. We hypothesize on the different types of reasons that seem to be behind these users’ behavior. We also present suggestions on 205 what researchers who rely on TripAdvisor data should do to avoid multiple posts/duplicate reviews from influencing their results. Originality/Value. We hope this work will contribute to raise awareness regarding samples with duplicate/near-duplicate reviews.