Identifying Controversial Wikipedia Articles Using Editor Collaboration Networks
Bibliographic record
Abstract
Wikipedia is probably the most commonly used knowledge reference nowadays, and the high quality of its articles is widely acknowledged. Nevertheless, disagreement among editors often causes some articles to become controversial over time. These articles span thousands of popular topics, including religion, history, and politics, to name a few, and are manually tagged as controversial by the editors, which is clearly suboptimal. Moreover, disagreement, bias, and conflict are expressed quite differently in Wikipedia compared to other social media, rendering previous approaches ineffective. On the other hand, the social process of editing Wikipedia is partially captured in the edit history of the articles, opening the door for novel approaches. This article describes a novel controversy model that builds on the interaction history of the editors and not only predicts controversy but also sheds light on the process that leads to controversy. The model considers the collaboration history of pairs of editors to predict their attitude toward one another. This is done in a supervised way, where the votes of Wikipedia administrator elections are used as labels indicating agreement (i.e., support vote) or disagreement (i.e., oppose vote). From each article, a collaboration network is built, capturing the pairwise attitude among editors, allowing the accurate detection of controversy. Extensive experimental results establish the superiority of this approach compared to previous work and very competitive baselines on a wide range of settings.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".