Gatekeeping Fake News Discourses on Mainstream Media Versus Social Media
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 analyzes mainstream media (MSM) coverage of fake news discourse and compares it with social networking sites (SNS) users who reference the term “fakenews” in their tweets. The study employs computational methods by analyzing over 8 million tweets and 1,350 news stories using topic modeling. Building on the theory of (networked) gatekeeping and Herman and Chomsky’s propaganda model, the results show that SNS users follow networked gatekeeping practices by mostly associating fake news references to the alleged bias of MSM. On the other hand, MSM coverage tends to link fake news to SNS’s negative role in spreading misinformation. I argue here that there is a networked flak activity on Twitter which is defined as a collective negative response to MSM in order to discipline it, change its tone and editorial stance, or undermine the public’s trust in it.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it