Racism in tourism reviews. (October 2020)
- Record Type:
- Journal Article
- Title:
- Racism in tourism reviews. (October 2020)
- Main Title:
- Racism in tourism reviews
- Authors:
- Li, Shu
Li, Gang
Law, Rob
Paradies, Yin - Abstract:
- Abstract: Racism is increasingly recognised as a key driver of unfair inequalities in power, resources and opportunities across racial groups. A comprehensive understanding of racism is beneficial to activist groups, policymakers and governments. Traditional approaches, such as surveys and interviews, are usually time-consuming and inefficient in capturing the occurrence of large-scale racism. In this study, we utilise routinely collected data available on tourism websites to assess self-reported racism in the tourism domain. We present a data acquisition procedure that collects racism-related reviews from the Internet at the global scale and then utilise statistics and natural language processing techniques to analyse and explore racism in terms of its tendency, distribution, semantics and characteristics. The effectiveness of the proposed method is demonstrated in a case study, in which we acquire racism-related data at the global scale and validate the impact of racial discrimination on tourists' experience. Highlights: Using tourism data to analyse and assess racism at a global scale. An approach is presented to acquire racism-related data and extract information without directly engaging with tourists. Reviews text processing technique is presented to analyse global racism patterns. The method is demonstrated in a case study and the purpose for racism analysis is achieved. A valuable and reliable data source with a large sample size is provided for related research onAbstract: Racism is increasingly recognised as a key driver of unfair inequalities in power, resources and opportunities across racial groups. A comprehensive understanding of racism is beneficial to activist groups, policymakers and governments. Traditional approaches, such as surveys and interviews, are usually time-consuming and inefficient in capturing the occurrence of large-scale racism. In this study, we utilise routinely collected data available on tourism websites to assess self-reported racism in the tourism domain. We present a data acquisition procedure that collects racism-related reviews from the Internet at the global scale and then utilise statistics and natural language processing techniques to analyse and explore racism in terms of its tendency, distribution, semantics and characteristics. The effectiveness of the proposed method is demonstrated in a case study, in which we acquire racism-related data at the global scale and validate the impact of racial discrimination on tourists' experience. Highlights: Using tourism data to analyse and assess racism at a global scale. An approach is presented to acquire racism-related data and extract information without directly engaging with tourists. Reviews text processing technique is presented to analyse global racism patterns. The method is demonstrated in a case study and the purpose for racism analysis is achieved. A valuable and reliable data source with a large sample size is provided for related research on racism. … (more)
- Is Part Of:
- Tourism management. Volume 80(2020)
- Journal:
- Tourism management
- Issue:
- Volume 80(2020)
- Issue Display:
- Volume 80, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 80
- Issue:
- 2020
- Issue Sort Value:
- 2020-0080-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-10
- Subjects:
- Tourism -- Racism -- Review text processing -- Sentiment analysis
Tourism -- Periodicals
338.4791 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02615177 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tourman.2020.104100 ↗
- Languages:
- English
- ISSNs:
- 0261-5177
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8870.920970
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13381.xml