# Interpretable classification of wiki-review streams

arXiv: https://arxiv.org/abs/2405.18335 · HTML: https://mlsanity.com/paper/2405.18335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 79 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [VEWS: A Wikipedia Vandal Early Warning System](https://mlsanity.com/paper/1507.01272/) (2015)
- [SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features](https://mlsanity.com/paper/2103.07612/) (2021)
- [A Machine Learning Approach to Comment Toxicity Classification](https://mlsanity.com/paper/1903.06765/) (2019)
- [Wiki-Reliability: A Large Scale Dataset for Content Reliability on Wikipedia](https://mlsanity.com/paper/2105.04117/) (2021)
- [StRE: Self Attentive Edit Quality Prediction in Wikipedia](https://mlsanity.com/paper/1906.04678/) (2019)
- [Automatically Labeling Low Quality Content on Wikipedia by Leveraging Patterns in Editing Behaviors](https://mlsanity.com/paper/2108.02252/) (2021)
- [Building automated vandalism detection tools for Wikidata](https://mlsanity.com/paper/1703.03861/) (2017)
- [LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information](https://mlsanity.com/paper/2104.06057/) (2021)
- [Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017](https://mlsanity.com/paper/1712.05956/) (2017)

## Built on by (most cited first, 1 of 1)

- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)

## Abstract (arXiv)

Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90 % values for all evaluation metrics (accuracy, precision, recall, and F-measure).