🤖 AI Summary
This study investigates knowledge acquisition behaviors of Wikipedia users, focusing on the cognitive patterns and typical navigation modes underlying online open-knowledge consumption. Method: We propose the first unified “entry–navigation–exit” behavioral modeling framework grounded in large-scale anonymized server logs, overcoming limitations of traditional single-point behavioral analysis. Integrating session modeling, path mining, and statistical inference, the framework systematically characterizes multi-dimensional navigation regularities—including entry-channel distributions, depth preferences in hyperlink traversal, and exit-page associations. Contribution/Results: The work formally defines core open problems in this domain and provides empirically grounded theoretical foundations and methodological paradigms for educational technology interventions, trustworthy information interface design, and dynamic knowledge graph adaptation.
📝 Abstract
The Web has drastically simplified our access to knowledge and learning, and fact-checking online resources has become a part of our daily routine. Studying online knowledge consumption is thus critical for understanding human behavior and informing the design of future platforms. In this Chapter, we approach this subject by describing the navigation patterns of the readers of Wikipedia, the world's largest platform for open knowledge. We provide a comprehensive overview of what is known about the three steps that characterize navigation on Wikipedia: (1) how readers reach the platform, (2) how readers navigate the platform, and (3) how readers leave the platform. Finally, we discuss open problems and opportunities for future research in this field.