Eliciting and Retrieving the Feedback-Loop. Exploring Elicitation Interview Techniques for Detecting Algorithmic Feedback on Social Media and Cultural Consumption

This article introduces elicitative interviewing techniques in the context of algorithmic feedback detection on social media about cultural consumption. This article presents elicitation interviewing methods to identify algorithmic feedback concerning cultural consumption on social media. The initial section will clarify the notion of influence in algorithm-driven consumption decisions on these platforms. The second part will underscore the necessity for finely nuanced qualitative methodologies to dissect the conceptual facets essential for analysis within such contexts of influence and dynamics. The main interviewing techniques for finalizing data collection with this intent will then be reviewed. The third part will present an example of a survey instrument that uses the elicitation component to achieve the essence of the feedback-loop between algorithms and cultural consumption choices that underlie the PRIN ALGOFEED survey. Finally, this detection phase's placement within the project and its role as an enhancer of the preceding collection and analysis stages will be elucidated, emphasizing the benefits of this decision and the potential pitfalls that necessitate proper attention and scrutiny.