Agreeing and disagreeing in collaborative knowledge graph construction: An analysis of Wikidata

• Published in: Web semantics Vol. 86; p. 100868
• Main Authors: Koutsiana, Elisavet, Yadav, Tushita, Jain, Nitisha, Meroño-Peñuela, Albert, Simperl, Elena
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2025; Elsevier
• Subjects: Argumentation; Collaborative knowledge base; Collaborative knowledge graph; Community analysis; Controversy; Discussion analysis; Knowledge community; Knowledge community; Collaborative knowledge base; Discussion analysis; Argumentation; Community analysis; Controversy; Collaborative knowledge graph
• ISSN: 1570-8268
• DOI: 10.1016/j.websem.2025.100868

In this work, we study disagreements in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are essential in collaborative work as they can increase contributor performance and encourage the emergence of shared norms and practices. While disagreements can play a productive role in discussions, they can also lead to conflicts and controversies, which impact contributor’ well-being and their motivation to engage. We want to understand if and when such phenomena arise in Wikidata, using a mix of quantitative and qualitative analyses to identify the types of topics people disagree about, the most common patterns of interaction, and roles people play when arguing for or against an issue. We find that decisions to create Wikidata properties are much faster than those to delete properties and that more than half of controversial discussions do not lead to consensus. Our analysis suggests that Wikidata is an inclusive community, considering different opinions when making decisions, and that conflict and vandalism are rare in discussions. At the same time, while one-fourth of the editors participating in controversial discussions contribute legitimate and insightful opinions about Wikidata’s emerging issues, they respond with one or two posts and do not remain engaged in the discussions to reach consensus. Our work contributes to the analysis of collaborative KG construction with insights about communication and decision-making in projects, as well as with methodological directions and open datasets. We hope our findings will help managers and designers support community decision-making and improve discussion tools and practices.