Responsible processing of crowdsourced tourism data

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Leal, F (Author) Other; Malheiro, B (Author) Other; Veloso, B (Author) FEP; Burguillo, JC (Author) Other

Journal

Title: JOURNAL OF SUSTAINABLE TOURISM

Vol. 29

Pages: 774-794

ISSN: 0966-9582

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-00S-E8B

DOI: 10.1080/09669582.2020.1778011

Abstract (EN): Online tourism crowdsourcing platforms, such as AirBnB, Expedia or TripAdvisor, rely on the continuous data sharing by tourists and businesses to provide free or paid value-added services. When adequately processed, these data streams can be used to explain and support businesses in the early identification of trends as well as prospective tourists in obtaining tailored recommendations, increasing the confidence in the platform and empowering further end-users. However, existing platforms still do not embrace the desired accountability, responsibility and transparency (ART) design principles, underlying to the concept of sustainable tourism. The objective of this work is to study this problem, identify the most promising techniques which follow these principles and design a novel ART-compliant processing pipeline. To this end, this work surveys: (i) real-time data stream mining techniques for recommendation and trend identification; (ii) trust and reputation (T&R) modelling of data contributors; (iii) chained-based storage of trust models as smart contracts for traceability and authenticity; and (iv) trust- and reputation-based explanations for a transparent and satisfying user experience. The proposed pipeline redesign has implications both to digital and to sustainable tourism since it advances the current processing of tourism crowdsourcing platforms and impacts on the three pillars of sustainable tourism.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21