Notice of Violation of IEEE Publication Principles: A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

Notice of Violation of IEEE Publication Principles

“A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos”
by Lina Zhong, Liyu Yang, Jia Rong, Haoyu Kong,
in IEEE Access, April 2020

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper contains copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“A Big Data Analytics Method for Tourist Behaviour Analysis”
by Shah Jahan Miah, Huy Quan Vu, John Gammack, Michael McGrath,
in Information and Management, 54 (2017), pp. 771-785


Understanding the interests of tourists is a key skill for attraction managers to prepare plans and make strategic decisions in tourism marketing. The rapid growth and spread of social media websites provide an information-rich channel from which tourism researchers and managers can collect a large amount of text-based reviews or comments and photos relating to the past travel experiences of users. The travel photos with geographic information are especially helpful in identifying the geographical location of the destinations. By analyzing these big data in various formats can help to understand the interests of tourists at destinations. In this paper, a framework is proposed to identify the interests of tourists by integrating information carried by the geotagged photos shared on social media websites. Such an approach is expected to provide sustainable tracking on popular places of interest (POIs) updated by tourists and pick the best representative photos taken by them. The performance of this model is evaluated by conducting a case study using the geotagged photos taken in Hong Kong. A case study proved this proposed framework could make a thriving tourism industry more efficient.