NwQM: A neural quality assessment framework for Wikipedia

Abstract

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 Quality Monitor (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.

Mining the online infosphere: A survey

“…[124] proposed a multimodal information fusion approach where embeddings obtained from both article text as well as html rendering of the article webpage is used for final classification. [52] proposed the first approach which incorporates information from three modes for quality assessment, i.e., article text, article image and article talk page. [52] obtains 8% improvement over [125] approach and achieves the SOTA result.…”

“…[52] proposed the first approach which incorporates information from three modes for quality assessment, i.e., article text, article image and article talk page. [52] obtains 8% improvement over [125] approach and achieves the SOTA result. A complementary direction of exploration has been put forward by [84,31] where correlation between article quality and structural properties of co-editor network and editor-article network has been exploited.…”

“…Under the first topic we shall identify the different features of an article like its language, structure and stability as well as their quality [150,53,120]. We shall further summarise attempts that have been made to automatically predict the quality of an article [52,91]. Within the second topic we shall briefly describe various issues related to the community of editors including anomalies, vandalism and edit wars [71,135].…”

Mining the online infosphere: A survey

“…Shen, Salehi, Baldwin, and Qi (2019) proposed a multimodal information fusion approach where embeddings obtained from both article text as well as html rendering of the article webpage is used for final classification. Guda et al (2020) proposed the first approach which incorporates information from three modes for quality assessment, that is, article text, article image, and article talk page. Guda et al (2020) obtains an 8% improvement over Shen, Salehi, Qi, and Baldwin (2019) approach and achieve the SOTA result.…”

“…Under the first topic, we shall identify the different features of an article like its language, structure, and stability as well as their quality (Halfaker & Geiger, 2020; Sarkar et al, 2019; Zhang et al, 2020). We shall further summarize attempts that have been made to automatically predict the quality of an article (Guda et al, 2020; Marrese‐Taylor et al, 2019). Within the second topic, we shall briefly describe various issues related to the community of editors including anomalies, vandalism, and edit wars (Kiesel et al, 2017; Tran & Christen, 2015).…”