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In this paper, we investigate underlying factors that influence users when reporting feedback. In particular, we study both reporting incentives and reporting biases observed in a widely used review forum, the Tripadvisor Web site. We consider three sources of information: first, the numerical ratings left by the user for different aspects of quality; second, the textual comment accompanying a review; third, the patterns in the time sequence of reports. We first show that groups of users who discuss a certain feature at length are more likely to agree in their ratings. Second, we show that users are more motivated to give feedback when they perceive a greater risk involved in a transaction. Third, a user's rating partly reflects the difference between true quality and prior expectation of quality, as inferred from previous reviews. We finally observe that because of these biases, when averaging review scores there are strong differences between the mean and the median. We speculate that the median may be a better way to summarize the ratings.<\/jats:p>","DOI":"10.1145\/1734200.1734202","type":"journal-article","created":{"date-parts":[[2010,4,27]],"date-time":"2010-04-27T12:45:25Z","timestamp":1272372325000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Reporting incentives and biases in online review forums"],"prefix":"10.1145","volume":"4","author":[{"given":"Radu","family":"Jurca","sequence":"first","affiliation":[{"name":"Google Switzerland"}]},{"given":"Florent","family":"Garcin","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}]},{"given":"Arjun","family":"Talwar","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}]},{"given":"Boi","family":"Faltings","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2010,4,29]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Admati A. and Pfleiderer P. 2000. Noisytalk.com: Broadcasting opinions in a noisy environment. Working Paper 1670R Stanford University.  Admati A. and Pfleiderer P. 2000. Noisytalk.com: Broadcasting opinions in a noisy environment. Working Paper 1670R Stanford University.","key":"e_1_2_1_1_1","DOI":"10.2139\/ssrn.255318"},{"volume-title":"Proceedings of the National Conference on Artificial Intelligence.","author":"Cui H.","key":"e_1_2_1_2_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.1145\/775152.775226"},{"doi-asserted-by":"crossref","unstructured":"Dellarocas C. Awad N. and Zhang X. 2006. Exploring the value of online product ratings in revenue forecasting: The case of motion pictures. Working paper.  Dellarocas C. Awad N. and Zhang X. 2006. Exploring the value of online product ratings in revenue forecasting: The case of motion pictures. Working paper.","key":"e_1_2_1_4_1","DOI":"10.1002\/dir.20087"},{"unstructured":"Forman C. Ghose A. and Wiesenfeld B. 2006. 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