Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89 of vandalism while reducing patrollers' workload by 98 , by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit.
Regarding vandalism detection have studied on the demography of vandalism in Wikidata @cite_2 showing interesting dynamics in how and who vandalizes Wikidata. For example, most of the vandals in Wikidata had previously vandalized Wikipedia. @cite_2 As far as we can tell, our work is the first published about a vandalism detection classifier for Wikidata.
@cite_2
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We report on the construction of the Wikidata Vandalism Corpus WDVC-2015, the first corpus for vandalism in knowledge bases. Our corpus is based on the entire revision history of Wikidata, the knowledge base underlying Wikipedia. Among Wikidata's 24 million manual revisions, we have identified more than 100,000 cases of vandalism. An in-depth corpus analysis lays the groundwork for research and development on automatic vandalism detection in public knowledge bases. Our analysis shows that 58 of the vandalism revisions can be found in the textual portions of Wikidata, and the remainder in structural content, e.g., subject-predicate-object triples. Moreover, we find that some vandals also target Wikidata content whose manipulation may impact content displayed on Wikipedia, revealing potential vulnerabilities. Given today's importance of knowledge bases for information systems, this shows that public knowledge bases must be used with caution.