Selection biases in crowdsourced big data applied to tourism research: An interpretive framework

被引:13
作者
Zheng, Yunhao [1 ]
Zhang, Yi [1 ,5 ]
Mou, Naixia [2 ]
Makkonen, Teemu [3 ]
Li, Mimi [4 ]
Liu, Yu [1 ,5 ]
机构
[1] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Sch Earth & Space Sci, Beijing, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao, Peoples R China
[3] Univ Eastern Finland, Karelian Inst, Joensuu, Finland
[4] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Hong Kong, Peoples R China
[5] Southwest United Grad Sch, Kunming, Peoples R China
基金
中国国家自然科学基金;
关键词
Crowdsourcing; Data quality; Selection biases; Big data; Interpretive framework; SOCIAL MEDIA; CHINESE TOURISTS; DESTINATION IMAGE; TRAVEL BEHAVIOR; PATTERNS; DIVIDE; TRENDS; FLOWS;
D O I
10.1016/j.tourman.2023.104874
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Crowdsourced big data has faced growing criticism due to its quality issues, particularly selection biases. We propose an interpretive framework for understanding selection biases in crowdsourced big data applied to tourism research. Inspired by medical terminology, the framework was structured according to external manifestations, internal causes, and potential influencing factors. Using illustrative case data from six websites, the framework demonstrates the emergence and impact of selection biases. Specifically, crowdsourcing-based tourism analysis can be notably affected by online platforms and destination contexts. Crowdsourced samples may not provide a perfect representation of actual travelers due to skewness in gender, age, origin, etc. Tourism researchers and stakeholders are urged to acknowledge selection biases and respond judiciously in their academic and practical efforts. Our research addresses a timely data science issue and offers insights for advancing knowledge innovation and technological improvements in tourism.
引用
收藏
页数:17
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