Information Reputation(s): Swifties, super fans, and their information seeking behaviours
Bibliographic record
Abstract
This paper explores the information seeking behaviours of super fans, specifically fans of Taylor Swift, and their respective behaviours of knowledge collecting and sharing. While fandom studies are well established in cultural and media scholarship, the information seeking behaviours of fans (how they search for, interpret, and share knowledge) is relatively underexplored in a Library and Information Science (LIS) context. This paper addresses this gap by examining Swifties (fans of Taylor Swift) as a case study to demonstrate how fan-based information practices complicate and expand traditional understandings of Human Information Interaction (HII), particularly where affect, identity, and pleasure are central motivations. This study then examines the way that Swifties both search for and receive information about her as methods of personal fulfillment. Additionally, this study builds on HII models by mobilizing post-modern theory to both highlight the figure of the icon and to study information behaviours that, for the most part, are produced for the sake of it (for joy and pleasure). The methods used in this study are grounded in the sense-making model offered by Brenda Dervin and deploys Information World Mapping (IWM) techniques during the interview process with participants to explore their information practices. Participants illustrated and discussed how they locate, evaluate, and share information about Taylor Swift across multiple platforms and contexts. Findings indicate that social media functions as the primary hub for Swiftie information seeking, facilitating both individual sense-making and community connection. Participants described verifying information through Taylor Swift’s own media outputs, interpreting “Easter eggs” embedded in her work, and linking their fan practices to processes of identity formation and leisure. The results suggest that Swifties’ information behaviours are motivated by joy, fulfillment, and self-expression, and demonstrates how fandom contexts extend HII frameworks to include identity-driven motivations, positioning the pop culture icon not only as subject but also as information system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".