Language-Agnostic Modeling of Source Reliability on Wikipedia
This addresses the need for verifying source credibility on Wikipedia to assist editors and potentially other user-generated content communities, though it is incremental as it adapts existing approaches to a language-agnostic context.
The paper tackles the problem of assessing web domain reliability across multiple language editions of Wikipedia to combat disinformation, achieving an F1 Macro score of about 0.80 for high-resource languages and 0.65 for mid-resource languages.
Over the last few years, verifying the credibility of information sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of web domains as sources in references across multiple language editions of Wikipedia. Utilizing editing activity data, the model evaluates domain reliability within different articles of varying controversiality, such as Climate Change, COVID-19, History, Media, and Biology topics. Crafting features that express domain usage across articles, the model effectively predicts domain reliability, achieving an F1 Macro score of approximately 0.80 for English and other high-resource languages. For mid-resource languages, we achieve 0.65, while the performance of low-resource languages varies. In all cases, the time the domain remains present in the articles (which we dub as permanence) is one of the most predictive features. We highlight the challenge of maintaining consistent model performance across languages of varying resource levels and demonstrate that adapting models from higher-resource languages can improve performance. We believe these findings can assist Wikipedia editors in their ongoing efforts to verify citations and may offer useful insights for other user-generated content communities.