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

2024-07-24

arXiv.org

AI Summary

Wikidata suffers from low core contributor retention—only 0.8% of users perform 80% of edits—and insufficient long-term participation. Method: We conduct a systematic analysis of the platform’s entire discussion corpus, introducing the first small-world network model of Wikidata discussions. Integrating social network analysis, BERT-based text embeddings, and Node2Vec graph embeddings—validated via rigorous statistical testing—we examine how conversational continuity, account age, and interaction depth jointly predict long-term retention. Contribution/Results: We identify that post semantics significantly modulate conversational continuity, and uncover synergistic predictive effects among these factors. Our analysis pinpoints actionable behavioral signals driving sustained contribution, providing empirically grounded, implementable insights for refining community governance mechanisms and designing targeted incentive strategies—thereby enhancing core contributor retention.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Community question answering
• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We study collaboration patterns of Wikidata, one of the world's largest collaborative knowledge graph communities. Wikidata lacks long-term engagement with a small group of priceless members, 0.8%, to be responsible for 80% of contributions. Therefore, it is essential to investigate their behavioural patterns and find ways to enhance their contributions and participation. Previous studies have highlighted the importance of discussions among contributors in understanding these patterns. To investigate this, we analyzed all the discussions on Wikidata and used a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our research showed that the interactions between Wikidata editors form a small world network where the content of a post influences the continuity of conversations. We also found that the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our findings can benefit the Wikidata community by helping them improve their practices to increase contributions and enhance long-term participation.

Problem

• Analyze communication patterns in Wikidata
• Impact of discussions on community engagement
• Factors influencing long-term member participation

Innovation

• mixed methods analysis
• network topology study
• text and graph embedding