Improving Wikipedia Verifiability with AI
Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick Lewis,, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick,, Pierre-Emmanuel Mazar\'e, Armand Joulin, Edouard Grave, Sebastian Riedel

TL;DR
This paper presents Side, an AI system that identifies potentially unverifiable Wikipedia citations and recommends better sources, aiming to enhance verifiability and trustworthiness of online information.
Contribution
The paper introduces a neural network-based tool that suggests improved citations for Wikipedia, trained on existing references and validated through crowdsourcing and community engagement.
Findings
70% of suggested citations preferred over original for unverifiable claims
Side's recommendations increase citation preferences by over 60% in user tests
System demonstrates potential to assist human editors in improving Wikipedia's verifiability
Abstract
Verifiability is a core content policy of Wikipedia: claims that are likely to be challenged need to be backed by citations. There are millions of articles available online and thousands of new articles are released each month. For this reason, finding relevant sources is a difficult task: many claims do not have any references that support them. Furthermore, even existing citations might not support a given claim or become obsolete once the original source is updated or deleted. Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort. Here, we show that the process of improving references can be tackled with the help of artificial intelligence (AI). We develop a neural network based system, called Side, to identify Wikipedia citations that are unlikely to support their…
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Taxonomy
TopicsWikis in Education and Collaboration · Topic Modeling · Natural Language Processing Techniques
