Title Page

Detecting Algorithmic Bias in Turkish E-Commerce Reviews: A Systematic Comparison of Supervised, Lexicon-Based, and Unsupervised Polarity Analysis Methods

Author Information:

Betul Kan-Kilinc
Department of Statistics
Eskisehir Technical University
Yunus Emre Campus, 26470, Eskisehir, Turkiye
Email_1: bkan@eskisehir.edu.tr
Email_2: betul.kankilinc@duke.edu
Phone: +90 222 321 7943
ORCID:https://orcid.org/0000-0002-3746-2327
ResearcherID: J-4078-2017
Scopus Author ID: 56728560600



Acknowledgments:

This work was financially supported by the Tubitak-BIDEB-2219 International Postdoctoral Research Scholarship Programme, and additional support, including server resources, was provided by the Department of Statistical Science at Duke University in the U.S., which greatly facilitated this research. I would also like to express my sincere gratitude to Prof. Dr. Mine Çetinkaya-Rundel for her valuable comments, insightful suggestions, and generous guidance throughout the development of this paper.

Statements and Declarations:

Funding: This research was supported by the Tubitak-BIDEB-2219 International Postdoctoral Research Scholarship Programme.

Conflicts of interest/Competing interests: The author declares no conflicts of interest.

Ethics approval: Not applicable.

Consent to participate: Not applicable.

Consent for publication: The author gives consent for publication.

Availability of data and materials: The datasets generated during and/or analyzed during the current study are available in the shoppingwords R package on CRAN.

Code availability: The code for data collection and analysis is available in the shoppingwords R package on CRAN and in the supplementary materials.

Authors' contributions: Betul Kan-Kilinc: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization.