The Dynamics of Online Fandom Communities: A Qualitative Study on Engagement and Identity
Bibliographic record
Abstract
This study aims to explore the dynamics of online fandom communities, focusing on how these digital platforms influence engagement patterns, identity formation, community dynamics, motivations for participation, and the challenges and issues faced by members. It seeks to contribute to the existing literature by providing a comprehensive analysis of the multifaceted experiences of individuals within these communities. A qualitative research design was employed, utilizing semi-structured interviews with 33 participants actively involved in various online fandom communities. Thematic analysis was conducted to identify key themes and categories related to engagement, identity, and community dynamics. The study adhered to ethical guidelines, ensuring participant confidentiality and informed consent. The analysis revealed five main themes: Engagement Patterns, Identity Formation, Community Dynamics, Motivations for Participation, and Challenges and Issues. Engagement Patterns highlighted content creation, community interaction, and event participation. Identity Formation encompassed personal identity, group identity, and roles within the community. Community Dynamics focused on inclusion and exclusion, conflict and resolution, and support and solidarity. Motivations for Participation identified escapism, social connection, and skill development as key drivers. Challenges and Issues discussed toxic behaviors, identity safety, and content disputes. Online fandom communities play a significant role in shaping individuals' engagement patterns, identity formation, and community dynamics. These communities provide a supportive environment for creative expression, social interaction, and personal growth. However, challenges such as toxic behaviors and identity safety concerns highlight the need for effective community management and support mechanisms. The findings underscore the complexity of online fandom participation and its impact on individual and collective identity construction.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".