Interpretive researchers often face challenges in presenting the whole dataset for qualitative interpretation within the constrained space of academic articles, leading them to select only a limited number of extracts ( Mann, 2016 ). However, the process of selecting these extracts is frequently conducted without sufficient rigour due to the lack of a well-defined, principled approach to selection. Without such an approach, interpretive researchers risk introducing bias during selection, and this potentially undermines their ability to identify those with confirmed salience. To address this critical gap, we developed a rigorous methodology for identifying salient extracts. The dataset for this study comprises approximately 650,000 words of Facebook posts discussing a culturally sensitive news topic in Thai society – a trans Thai net idol dressing as the Lord Buddha for Hallowe’en celebrations. Using this large dataset, we employ a corpus-based technique to outline a four-stage methodology for identifying salient extracts: ( 1) keyword identification, ( 2) plot generation and keyword thematisation based on the plots’ main distribution areas, ( 3) investigation of the main communicators of posts linked to these keywords, and ( 4) the actual extract selection. Our findings demonstrate that this four-stage approach not only captures extracts with strong salience but also enhances the trustworthiness, providing a more rigorous framework for producing meaningful analysis and interpretation of interpretivist research findings.
Powichit et al. (Wed,) studied this question.