🤖 AI Summary
This study investigates disagreement dynamics and their implications in collaborative Wikidata knowledge graph construction. Methodologically, it employs a mixed-methods approach: quantitative analysis—including temporal modeling and participation metrics—to characterize controversy evolution, and qualitative coding—encompassing thematic classification, role annotation, and discourse analysis—to identify disagreement types, interaction patterns, and participant roles. Key contributions include the first systematic quantification of Wikidata discussion practices: attribute deletion decisions take significantly longer than creation; over 50% of controversies remain unresolved; rational edits yield high-value insights yet exhibit low retention, with 25% of deeply engaged editors departing shortly thereafter; and overt conflict or vandalism is rare, reflecting an inclusive, consensus-oriented community culture. The findings reveal critical governance challenges—inefficient consensus formation and severe editor attrition—providing empirical grounding for improving collaborative mechanisms and discussion tools in knowledge graph curation.
📝 Abstract
In this work, we study disagreement in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are important 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 with legit and insightful opinions about Wikidata's emerging issues, they do not remain engaged in the discussions. We hope our findings will help Wikidata support community decision making, and improve discussion tools and practices.
