Language-Agnostic Modeling of Source Reliability on Wikipedia (2410.18803v2)

Abstract: Over the last few years, content verification through reliable sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of sources across multiple language editions of Wikipedia. Utilizing editorial activity data, the model evaluates source 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 source 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. This work contributes not only to Wikipedia's efforts in ensuring content verifiability but in ensuring reliability across diverse user-generated content in various language communities.

• The paper introduces a model that uses editorial activity data to predict Wikipedia source reliability without relying on language-specific processing.
• It demonstrates strong performance metrics, achieving an F1 Macro score of approximately 0.80 for high-resource languages while noting lower scores in mid- and low-resource contexts.
• The findings support enhanced editorial guidelines and offer a pathway for adapting models to improve verifiability and combat misinformation on multilingual platforms.

Language-Agnostic Modeling of Source Reliability on Wikipedia

The paper "Language-Agnostic Modeling of Source Reliability on Wikipedia" presents an innovative approach towards evaluating the reliability of sources cited on Wikipedia, using a model that transcends language barriers. Given the increasing significance of ensuring content verifiability in combatting disinformation, this paper addresses a critical challenge: assessing the reliability of sources across diverse language editions of Wikipedia.

Summary of Methodology and Key Findings

The authors introduce a model leveraging editorial activity data—a unique idea that circumvents the need for language-specific processing—to estimate the reliability of sources cited in various Wikipedia articles, encompassing topics like Climate Change, COVID-19, Biology, History, and Media. Their approach focuses on crafting language-agnostic features, such as the permanence of sources within articles, the number of articles referencing a source, and the number of unique users interacting with the source citations.

Key performance metrics of their model, such as the F1 Macro score, reveal an impressively effective prediction of source reliability in high-resource language settings, with scores reaching approximately 0.80 for English and other high-resource languages. However, performance varies notably in mid-resource (F1 Macro 0.65) and low-resource languages, with the latter witnessing more fluctuation, underscoring the challenge of consistent performance across diverse linguistic contexts.

Strong Numerical Results and Insights

One of the strongest numerical results is the model's capability to allude to source reliability through editorial behavior patterns, achieving significant predictive power with language-agnostic features alone. Notably, permanence, a key feature indicating the duration a source remains cited in articles, emerged as among the most pivotal in predicting reliability.

Furthermore, the model demonstrates adaptability potential; while its performance decreases when applied to cross-language and cross-topic settings, improvements are observed when models trained in high-resource languages are adapted to mid- and low-resource languages. This highlights the model's versatility and potential to be refined for broader applicability.

Implications and Future Work

The paper contributes significantly to the efforts in maintaining Wikipedia's role as a reliable information source by providing a mechanism that can enhance editorial monitoring algorithms, primarily in language editions lacking substantial resources. The authors demonstrate that using a mix of language-derived data can improve models for low-resource languages, suggesting future research should continue to explore cross-language and cross-topic machine learning model adaptations.

A notable implication of the findings is the possibility of using the model to support editorial guidelines and help curate community-maintained reliable and unreliable source lists, crucial for Wikipedia's information integrity. Future advancements could involve augmenting this language-agnostic model with semantic linking or user session data to improve its predictive accuracy and applicability.

Conclusion

In introducing a model that astute editors can use across Wikipedia's wide array of languages, the paper tackles the pressing challenge of source reliability in an increasingly multilingual and multifacetal online ecosystem. The authors successfully provide a foundational approach to discerning reliable sources using a language-neutral framework, setting the stage for further developments in AI models aimed at content reliability and verifiability. These advancements not only bolster the integrity of Wikipedia but have wider implications for combating misinformation across user-generated content platforms.