AI-Augmented Netnography: Ethical and Methodological Frameworks for Responsible Digital Research

The increasing integration of Artificial Intelligence (AI) in social sciences research is reshaping qualitative methodologies, particularly in those studies that employ the netnography method. Although AI provides improved data processing abilities, it also introduces ethical and methodological concerns about privacy, transparency, authenticity, and possible bias. This paper proposes ethical and methodological frameworks for AI-augmented netnography that prioritize responsible AI use without compromising the interpretive depth and cultural sensitivity foundational to traditional netnography. The frameworks address the complexities of informed consent, data minimization, bias mitigation, and accountability, providing a structured approach to balancing AI’s efficiency with human-led analysis. Using a case study of online activism, this research illustrates the frameworks’ practical application across diverse digital platforms, such as Twitter and Instagram. By combining AI-driven sentiment and pattern recognition with human interpretive oversight, the study captures cultural nuances essential to understanding online social movements. This dual approach highlights AI-augmented netnography’s potential to deliver rigorous, ethically grounded insights into digital communities, promoting more nuanced and inclusive research outcomes. The study contributes to the evolving landscape of digital research by offering actionable, ethically robust frameworks applicable to a broad spectrum of qualitative studies, emphasizing socially responsible research practices in the digital age.