 False advertising or slander? Using location based tweets to assess online rating-reliabilityAmit Poddar, Syagnik Banerjee and Karthik Sridhar Journal of Business Research, 2019, vol. 99, issue C, 390-397 Abstract: Online reviews have diffused into a large variety of businesses, ranging from physical goods to services. The ubiquity of the reviews and the importance given to them by potential customers makes examining their validity extremely important. While good reviews can boost companies' business, bad reviews can spell their doom. Since online reviews are anonymous, there are cases of both false advertising and slander that can create conflict. In this paper authors provide reasons for online rating bias and demonstrate a way to measure it. Authors mine consumer's location aware tweets from business locations to capture a location's pleasure score and compare the pleasure scores to Yelp ratings to determine how overrated or underrated the venue is. Foursquare and Twitter are mined to extract an emotion score from the location aware tweets using a dictionary called the Affective Norms for English Words (ANEW). Rating biases are found across cities and different types of restaurants and managerial and policy implications discussed. Keywords: Big data; Location aware tweets; Data mining; Public policy; Electronic WOM; Online ratings (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:99:y:2019:i:c:p:390-397 DOI: 10.1016/j.jbusres.2017.08.030 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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