Comparing features of fabricated and legitimate political news in digital environments (2016‐2017)
Bibliographic record
Abstract
ABSTRACT With the problem of ‘fake news’ in the digital media, there are efforts at creation of awareness, automation of ‘fake news’ detection and news literacy. This research is descriptive as it pulls evidence from the content of online fabricated news for the features that distinguish fabrications from the legitimate political news around the time of the U.S. Presidential Elections (276 articles in total, from November 2016 ‐ June 2017). Certain stylistic and psycho‐linguistic features of fabrications may be apparent to the news readers: fewer words and paragraphs but longer paragraphs, more slangs, swear words and affective words in the stories. Such features could be used for educational information literacy campaigns for spotting so‐called ‘fake news’. Other informative features may require specialized analytical tools (or further training) to notice the presence of more words, punctuation marks, demonstratives and emotiveness in fabrications but fewer verifiable facts (or named entities) in their headlines.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".