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False advertising or slander? Using location based tweets to assess online rating-reliability

Author

Listed:
  • Poddar, Amit
  • Banerjee, Syagnik
  • Sridhar, Karthik

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.

Suggested Citation

  • Poddar, Amit & Banerjee, Syagnik & Sridhar, Karthik, 2019. "False advertising or slander? Using location based tweets to assess online rating-reliability," Journal of Business Research, Elsevier, vol. 99(C), pages 390-397.
    • Handle: RePEc:eee:jbrese:v:99:y:2019:i:c:p:390-397
    DOI: 10.1016/j.jbusres.2017.08.030
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    References listed on IDEAS

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    1. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    2. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
    3. Justin Malbon, 2013. "Taking Fake Online Consumer Reviews Seriously," Journal of Consumer Policy, Springer, vol. 36(2), pages 139-157, June.
    4. Sarah G. Moore, 2015. "Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 42(1), pages 30-44.
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    Cited by:

    1. Hajek, Petr & Sahut, Jean-Michel, 2022. "Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Wang, Ruipeng & Tai, Yuhong, 2025. "How does blockchain mitigate false advertising in live streaming E-commerce? A tripartite stochastic evolutionary game approach," Journal of Retailing and Consumer Services, Elsevier, vol. 85(C).
    3. Ying, Shiyi & Huang, Youlin & Qian, Lixian & Song, Jinzhu, 2023. "Privacy paradox for location tracking in mobile social networking apps: The perspectives of behavioral reasoning and regulatory focus," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

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