Talking Wikidata: Communication patterns and their impact on community engagement in collaborative knowledge graphs

TL;DR

This study analyzes Wikidata's communication patterns, revealing how network structure and member activity influence long-term engagement, with implications for improving collaborative knowledge graph communities.

Contribution

It provides a detailed analysis of Wikidata's discussion networks and identifies key factors affecting contributor retention and participation.

Findings

Wikidata's interaction network is a resilient small world network.

Account age and discussion content significantly impact long-term engagement.

Network topology and discussion content influence conversation continuity.

Abstract

We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.