A Methodological Blueprint for Analysing Online Brand Advocacy

Online Brand Advocacy (OBA) is a powerful force in digital consumer communities, yet its authentic, large-scale expression remains methodologically difficult to analyse. Traditional methods often fail to capture the nuance within the high volume of unstructured user-generated content. This paper addresses this gap by introducing and demonstrating a novel methodological blueprint for analysing OBA with greater depth and scale. Our aim is to showcase how combining a fine-tuned Large Language Model (LLM) with SHapley Additive exPlanations (SHAP) can unlock deep, theory-driven insights from online discourse. We analysed over 24,000 comments from the r/CallofDuty and r/Battlefield subreddits. First, we fine-tuned a gpt-3.5-turbo model, using Wilk et al.'s (2020) sixdimensional OBA framework, to classify comments. Second, we used SHAP to identify the key linguistic terms driving the classifications. The results highlight the method's power to not only identify dominant advocacy dimensions (brand defence, brand positivity, and brand appraisal) but also to reveal distinct community-specific "dialects." This paper's primary contribution is a scalable, interpretable, and theoretically grounded method that enables researchers and practitioners to move beyond simple sentiment analysis and understand the specific vocabularies that shape brand communities.