Analyzing cross-platform academic networking behavior: Methods and insights on institutional affiliations and user clustering
Weiwei Yan, Yanyan Wang, Jeongyong Song, Yin Zhang · Information Processing & Management · 2025
This study investigates cross-platform behavior on academic social networking sites (ASNSs), focusing on differences among users from academic, government, and corporate institutions. Users often engage with multiple ASNSs due to differing platform features and contexts, leading to distinct behavioral patterns. Drawing on data from Academia.edu (ACA) and ResearchGate (RG), this study analyzes user profiles from 15 institutions to identify cross-platform users and compare behaviors. It proposes an approach for identifying such users and develops a cross-platform user behavior indicator system to support the analysis. A clustering analysis further explores behavior patterns and provides additional insights into cross-platform engagement. Findings show that cross-platform users tend to disclose more information, maintain broader networks, and engage more actively on RG than on ACA. Government-affiliated users are the most active, with high levels of disclosure, publication, and interaction. Corporate users exhibit varied strengths and weaknesses, while academic users demonstrate moderate activity. Most academic cross-platform users fall into a “civilian-type” category, sharing fewer publications and presenting inconsistent profile information. In contrast, many government and corporate users are ”star-type,” showing greater consistency and visibility across platforms. This study advances understanding of cross-platform ASNS behavior and reveals sector-based differences that may inform platform design and user strategies.