The Journalist on Social Media: Mapping the Promoter, Celebrity and Joker Roles on Twitter and Instagram
Bibliographic record
Abstract
This study takes an empirical approach to analyze how journalists perform the roles of promoter, celebrity, and joker on social media. These roles already play out in print and broadcast, but much less is known about how they are performed outside of traditional media contexts. This study addresses this gap in the literature through a content analysis of 4,100 posts by 23 Chilean journalists in 2020 on Twitter and Instagram. The analysis draws on key variables derived from the literature, including frontstage and backstage performance, personal context, platform, follower count, gender, and type of parent media organization. Results suggest that Twitter tends to serve as a space for professional performance bounded by established norms and practices, while Instagram tends to offer a space for a more fluid performance beyond the institutional boundaries of the news media. Findings indicate that professional social media contexts are more suited spaces to perform the promoter role, while personal or backstage contexts are more suited for the celebrity and joker roles. Results indicate how journalists take on specific roles on Twitter and Instagram, considering the affordances of these platforms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".