Large-Scale Social Feedback and Language Usage: a Distributional Analysis
Bibliographic record
Abstract
A recent trend in computational cognitive science is to use large-scale naturalistic data to evaluate cognitive theories (Griffiths, 2015; Jones, 2017). This article extends this work by examining the connection between social feedback and language usage. Social feedback, operationalized here as the difference between the number of upvotes and downvotes on comments on the social media forum Reddit, provides a unique opportunity to investigate how social approval interacts with linguistic behavior at a large scale. To accomplish this, social feedback ratings from over 400 billion words of comments from the social media forum Reddit were mined. Two types of corpora were formed: 1) large (approximately 1.7 billion words) combined corpora of positive, negative, and neutral comments, and 2) individual corpora from over 1,500 high-level commenters on Reddit (following recent work by Johns, 2021a, 2023, 2024a,b using individualized language models). Both were used to examine social feedback and language usage across multiple sets of lexical organization and lexical semantic data with distributional modeling techniques. It was found that comments that receive positive social feedback tended to align more closely with normative language usage, suggesting that the acceptability of a communicative message may lie in the distributional form of that utterance and not just the content of it. Additionally, this trend was replicated at the individual level, where commenters who had more normative (or typical) usage of language generally received more positive ratings on their comments. Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-025-00247-7.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".