Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure
Bibliographic record
Abstract
Casual online forums such as Reddit, Slashdot and Digg, are continuing to in-crease in popularity as a means of com-munication. Detecting disagreement in this domain is a considerable challenge. Many topics are unique to the conversa-tion on the forum, and the appearance of disagreement may be much more sub-tle than on political blogs or social me-dia sites such as twitter. In this analy-sis we present a crowd-sourced annotated corpus for topic level disagreement detec-tion in Slashdot, showing that disagree-ment detection in this domain is difficult even for humans. We then proceed to show that a new set of features determined from the rhetorical structure of the con-versation significantly improves the per-formance on disagreement detection over a baseline consisting of unigram/bigram features, discourse markers, structural fea-tures and meta-post features. 1
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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 | 0.000 |
| 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 | 0.000 |
| 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".