# NwQM: A neural quality assessment framework for Wikipedia

arXiv: https://arxiv.org/abs/2010.06969 · HTML: https://mlsanity.com/paper/2010.06969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction](https://mlsanity.com/paper/2308.07971/) (2023)
- [Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review](https://mlsanity.com/paper/2310.02235/) (2023)
- [A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia](https://mlsanity.com/paper/2410.19150/) (2024)

## Abstract (arXiv)

Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia QualityMonitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.