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<p>This paper explores how Flickr’s geotagged photos have contributed to the development of new research topics in tourism studies, particularly through the use of large-scale, freely available datasets. A systematic literature review and bibliometric network analysis were conducted using 333 Scopus-indexed papers that included “tourism” and “Flickr” in abstracts or keywords. An additional 519 papers citing this core set were identified. Network analysis using Gephi applied a gravity model and clustering algorithms to detect citation-based communities. Content analysis of highly cited papers helped define key research themes. Seven research clusters emerged, focusing on nature-based tourism, tourist activities by space-time behaviour, destination attractiveness, image development, travel route detection and recommendation, and machine learning for content analysis. These communities reflect the global academic interest enabled by Flickr’s open API, allowing reproducible and comparative analyses across destinations. This study highlights how a unified, open-access social media dataset has catalysed the formation of global research communities in tourism. Unlike newer platforms with restricted access, Flickr’s openness fostered methodological innovation and deepened field-specific knowledge in tourism research. A critical analysis of existing work was provided, highlighting overlooked areas and synthesising insights to propose new directions for research.</p>

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The contribution of accessible social media data to the work of tourism research communities: a bibliometric network analysis of studies using Flickr data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

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  • Bálint Kádár,
  • Márcio Ribeiro Martins

摘要

This paper explores how Flickr’s geotagged photos have contributed to the development of new research topics in tourism studies, particularly through the use of large-scale, freely available datasets. A systematic literature review and bibliometric network analysis were conducted using 333 Scopus-indexed papers that included “tourism” and “Flickr” in abstracts or keywords. An additional 519 papers citing this core set were identified. Network analysis using Gephi applied a gravity model and clustering algorithms to detect citation-based communities. Content analysis of highly cited papers helped define key research themes. Seven research clusters emerged, focusing on nature-based tourism, tourist activities by space-time behaviour, destination attractiveness, image development, travel route detection and recommendation, and machine learning for content analysis. These communities reflect the global academic interest enabled by Flickr’s open API, allowing reproducible and comparative analyses across destinations. This study highlights how a unified, open-access social media dataset has catalysed the formation of global research communities in tourism. Unlike newer platforms with restricted access, Flickr’s openness fostered methodological innovation and deepened field-specific knowledge in tourism research. A critical analysis of existing work was provided, highlighting overlooked areas and synthesising insights to propose new directions for research.