"What is a Newsfluencer?": Conversations About Identity, Social Media Platforms, and Journalism Boundaries
Bibliographic record
Abstract
This thesis examines the journalistic field position and perspectives of U.S. and Canadian “newsfluencers”: a portmanteau coined by Edward Hurcombe (2024) of “news” and “influencer.” Newsfluencers are content creators who regularly post about news across social media platforms and employ social media influencer (SMI) marketing practices, like self-branding, to cultivate engaged and participatory audiences. SMIs are internet personalities with substantial followings that ‘influence’ their audiences’ lifestyle and purchasing decisions. Focusing specifically on non-affiliated newsfluencers—or creators with no formal journalistic training or media background—through semi-structured interviews, this research explores how they: 1) navigate platforms and SMI strategies to gain followers, 2) establish relationships of trust and credibility, and 3) identify as ‘inside’ or ‘outside’ of journalism. Using field theory and boundary work to guide analysis, the findings add to existing literature surrounding newsfluencers and illuminate the role of non-affiliated newsfluencers in connecting with contemporary news audiences.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.035 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".