Comparing Audience Appreciation to Fact-Checking Across Political Communities on Reddit
Bibliographic record
Abstract
As a countermeasure to disinformation, many fact-checking websites, such as Snopes.com, provide valuable resources to verify news stories or claims. In this paper, we study how such fact-checking resources are used in online political discussions on Reddit, and how audiences or readers respond to their use in the context of the 2016 US Presidential Election. We first characterize the role of fact-checking resources by developing a typology for labeling instances in which they are employed in three political subreddits, r/politics, r/The_Donald and r/hillaryclinton. We find that fact-checking, when used as a correction to false information, is more prevalent on r/politics than on r/The_Donald or r/hillaryclinton. Next, we quantify audience responses to fact-checking by using comment score as a measure of popularity and find that the correction of facts is also more appreciated in r/politics than the other subreddits. Finally, we estimate the impact of corrections, and other uses of fact-checks, on the sustainability of a conversational thread and find that presence of corrections in r/politics appear to be correlated with short conversations. Overall, these findings indicate that the use of fact-checking resources within r/politics is distinct from more partisan subreddits.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 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".