What Sells on the Fake News Market? Examining the Impact of Contextualized Rhetorical Features on the Popularity of Fake Tweets
Bibliographic record
Abstract
A fake news ecosystem is akin to a marketplace where content generators and users exchange content like sellers and buyers. The popularity of a product in this marketplace is influenced by rhetorical features (ethos, pathos, and logos), topic categories (hard news, soft news, and general news), and design motivations (political intent, fun, etc.). Therefore, the determinants of the popularity of fake news should be contextualized better to understand the spreading patterns. First, we obtained a sample from a fact-checking organization (n=439). Then, we categorized tweets based on their topics and design motivation by using biaxial content analysis. Next, we proposed a rhetorical framework to organize the tweet-related indicators to develop the content’s systematic characterization. Finally, we examined both the primary and interaction effects of topics, design motivations, and organized rhetorical features of tweets on popularity through a negative binomial regression. The main results revealed a positive relationship between logos-related features (i.e., the number of followers) and the popularity of the fake tweets. In addition, an exciting interaction effect indicated that fake tweets designed with political intent are nearly five times less retweeted when they contain hashtags. The research and practical implications and future directions were also discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".