@Article{Redi2019,
author = {Redi, Miriam and Fetahu, Besnik and Morgan, Jonathan and Taraborelli, Dario}, 
title = {Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia\'s Verifiability}, 
journal = {}, 
volume = {}, 
number = {}, 
pages = {}, 
year = {2019}, 
abstract = {Wikipedia is playing an increasingly central role on the web,and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate and fact-check Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required by collecting labeled data from editors of multiple Wikipedia language editions. We then collect a large-scale crowdsourced dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design and evaluate algorithmic models to determine if a statement requires a citation, and to predict the citation reason based on our taxonomy. We evaluate the robustness of such models across different classes of Wikipedia articles of varying quality, as well as on an additional dataset of claims annotated for fact-checking purposes.}, 
location = {}, 
keywords = {}}

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Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate and fact-check Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required by collecting labeled data from editors of multiple Wikipedia language editions. We then collect a large-scale crowdsourced dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design and evaluate algorithmic models to determine if a statement requires a citation, and to predict the citation reason based on our taxonomy. We evaluate the robustness of such models across different classes of Wikipedia articles of varying quality, as well as on an additional dataset of claims annotated for fact-checking purposes.","location":"","keywords":"","bibtex":"@Article{Redi2019,\nauthor = {Redi, Miriam and Fetahu, Besnik and Morgan, Jonathan and Taraborelli, Dario}, \ntitle = {Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia\\'s Verifiability}, \njournal = {}, \nvolume = {}, \nnumber = {}, \npages = {}, \nyear = {2019}, \nabstract = {Wikipedia is playing an increasingly central role on the web,and the policies its contributors follow when sourcing and fact-checking content affect million of readers. 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We then collect a large-scale crowdsourced dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design and evaluate algorithmic models to determine if a statement requires a citation, and to predict the citation reason based on our taxonomy. 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