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Assessing reliability of social media data: lessons from mining TripAdvisor hotel reviews

Author

Listed:
  • Zheng Xiang

    (Virginia Tech
    Beijing Union University)

  • Qianzhou Du

    (Virginia Tech)

  • Yufeng Ma

    (Virginia Tech)

  • Weiguo Fan

    (Virginia Tech)

Abstract

As an emerging research paradigm, big data analytics has been gaining currency in various fields. However, in existing hospitality and tourism literature there is scarcity of discussions on the quality of data which may impact the validity and generalizability of research findings. This study examines the reliability of online hotel reviews in TripAdvisor by developing a text classifier to predict travel purpose (i.e., business vs. leisure) based upon review textual contents. The classifier is tested over a range of cities and data sizes to examine its sensitivity to data samples. The findings show that, while the classifier’s performance is consistent across different cities, there are variations in response to data sizes and sampling methods. More importantly, a considerable amount of noise is found in the data, which leads to misclassification. Furthermore, a novel approach is developed to address the misclassification problem resulting from data noise. This study reveals important data quality issues and contributes to the theoretical development of social media analytics in hospitality and tourism.

Suggested Citation

  • Zheng Xiang & Qianzhou Du & Yufeng Ma & Weiguo Fan, 2018. "Assessing reliability of social media data: lessons from mining TripAdvisor hotel reviews," Information Technology & Tourism, Springer, vol. 18(1), pages 43-59, April.
    • Handle: RePEc:spr:infott:v:18:y:2018:i:1:d:10.1007_s40558-017-0098-z
    DOI: 10.1007/s40558-017-0098-z
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    References listed on IDEAS

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    1. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    2. Hamid Ekbia & Michael Mattioli & Inna Kouper & G. Arave & Ali Ghazinejad & Timothy Bowman & Venkata Ratandeep Suri & Andrew Tsou & Scott Weingart & Cassidy R. Sugimoto, 2015. "Big data, bigger dilemmas: A critical review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(8), pages 1523-1545, August.
    3. Xiang, Zheng & Pan, Bing, 2011. "Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations," Tourism Management, Elsevier, vol. 32(1), pages 88-97.
    4. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    5. Alan S. Abrahams & Weiguo Fan & G. Alan Wang & Zhongju (John) Zhang & Jian Jiao, 2015. "An Integrated Text Analytic Framework for Product Defect Discovery," Production and Operations Management, Production and Operations Management Society, vol. 24(6), pages 975-990, June.
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    Cited by:

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    4. Lukas Hägele & Mathias Klier & Lars Moestue & Andreas Obermeier, 2025. "Aspect-based currency of customer reviews: A novel probability-based metric to pave the way for data quality-aware decision-making," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-18, December.
    5. Zachlod, Cécile & Samuel, Olga & Ochsner, Andrea & Werthmüller, Sarah, 2022. "Analytics of social media data – State of characteristics and application," Journal of Business Research, Elsevier, vol. 144(C), pages 1064-1076.
    6. Madelene Blaer, 2023. "Interactive webcam travel: supporting wildlife tourism and conservation during COVID-19 lockdowns," Information Technology & Tourism, Springer, vol. 25(1), pages 47-69, March.
    7. Yang, Yang & Mao, Zhenxing & Zhang, Xiaowei, 2021. "Better sleep, better trip: The effect of sleep quality on tourists' experiences," Annals of Tourism Research, Elsevier, vol. 87(C).

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