Identifying Controversial Wikipedia Articles Using Editor Collaboration Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it