The Journalist on Social Media: Mapping the Promoter, Celebrity and Joker Roles on Twitter and Instagram
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it