Language-Agnostic Modeling of Source Reliability on Wikipedia
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912719771271168 |
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| author | D'Ignazi, Jacopo Kaltenbrunner, Andreas Mejova, Yelena Tizzani, Michele Kalimeri, Kyriaki Beiró, Mariano Aragón, Pablo |
| author_facet | D'Ignazi, Jacopo Kaltenbrunner, Andreas Mejova, Yelena Tizzani, Michele Kalimeri, Kyriaki Beiró, Mariano Aragón, Pablo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language-Agnostic Modeling of Source Reliability on Wikipedia D'Ignazi, Jacopo Kaltenbrunner, Andreas Mejova, Yelena Tizzani, Michele Kalimeri, Kyriaki Beiró, Mariano Aragón, Pablo Social and Information Networks Machine Learning 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. |
| title | Language-Agnostic Modeling of Source Reliability on Wikipedia |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2410.18803 |