Algorithmic Colonization in Heritage Tourism Research: A Critical Bibliometric Analysis

Algorithmic Colonization in Heritage Tourism Research: A Critical Bibliometric Analysis

Huanhaun Li¹, Shali Wang ^2*

¹School of Geography and Tourism, Luoyang Normal University, Luoyang, 471000, China..  Email: lihh_wyh@163.com

²School of Economics and Management, Guizhou University of Engineering Science, Bijie, Guizhou , 551700, China. Email: teesn235@gues.edu.cn

* denotes the corresponding author

This dataset comprises two components:

1. HSSC_Appendices.docx

2. HSSC_Data and Code

Component 1, HSSC_Appendices.docx, provides a detailed explanation of the data processing procedures and methods employed in the manuscript, together with a reproducibility statement. It contains five sub-appendices (Appendix A–Appendix E), as follows:

Appendix A: This appendix consists of five sections, including operational definitions of core concepts, calculation formulas for critical bibliometric indices (ACI, ESI), algorithmic ethnographic protocols, counterfactual modeling parameter settings, and sensitivity tests. It provides unified, reproducible methodological specifications and technical details for the quantitative analysis, qualitative tracking, and counterfactual network modeling throughout the paper, supporting the rigor of the research method and the reliability of the results.

Appendix B: This appendix contains the literature screening process, spatiotemporal and journal distribution statistics of the sample, and coding rules for key variables. It clearly presents the screening logic, sample characteristics, and coding standards for the 2,417 articles, verifies sample representativeness, and provides the data foundation and quality assurance for the empirical analysis in the main text.

Appendix C: This appendix presents supplementary analytical results, including traditional bibliometric benchmark networks, heatmaps of regional bias in algorithmic visibility, timelines of semantic drift in the “sustainability” concept, and comparative figures of counterfactual networks. It visually corroborates the empirical findings of the four mechanisms of algorithmic colonization in the main text.

Appendix D: This appendix includes the researcher positionality statement, ethical statement on algorithmic research, and reflections on methodological paradoxes. It openly addresses the research stance, ethical norms, and internal contradictions, enhancing reflexivity and academic integrity, and responding to the positional and ethical concerns of critical research.

Appendix E: This appendix provides research reproducibility resources, including data availability statements, open-source code repositories, and interactive visualization links. The data processing and analysis codes are publicly available to ensure research reproducibility and verifiability, and to promote academic transparency and open science practices.

2.HSSC_Data and Code comprises the source data and original data processing codes provided for the analysis of the manuscript data. These include:

The repository structure is as follows:

·01_Data_clean.py: Processes five WOS TXT files (exported from WOS with “full records and cited references”) and generates a table containing document IDs, titles, author names, publication years, first author affiliations, reference lists, and other fields.

·02_extract_ID_reference.py: Extracts document IDs and corresponding reference lists to generate node tables (Nodes.csv) and edge tables (Edges.csv) required for Gephi.

·03_extract_ID_keywords.py: Extracts keywords and generates node files (Keyword_Nodes.csv) and edge files (Keyword_Edges.csv) formatted for Gephi, suitable for high-frequency keyword co-occurrence network analysis.

·04_W_geo_calculation.py: Calculates the core values of W_geo (ln) in the 2024 geographic weight factor (W_geo) table based on per capita GNI in Appendix Table A.2-1, following the formula W_geo = 1 / log(GNI_per_capita + 1).

·05_Sensitivity_testing.py: Systematically shows consistency differences in node rankings of the Algorithmic Centrality Index (ACI) under four GNI transformation methods (natural logarithm, common logarithm, reciprocal, square root), quantifying the impact of different weighting designs on ranking results.

·06_Comparison_Cited_Publications.py: Constructs counterfactual network comparison rules.

·07_Data source from wos.rar: All TXT data files exported from WOS.

Schedule A.2-2_Schedule A.2-2 Table of Geographic Weighting Factors (W_geo) Based on Per Capita GNI (2024).xlsx

Schedule A.2-4_Detailed Ranking Data for 2,417 Nodes.xlsx

The data in these two tables is used to calculate the relevant figures in the Appendix A: Methodological Protocols and Operationalization Details.