Citation proximus: the role of social and semantic ties in citing behaviour

2025-02-19

AI Summary

This study investigates the drivers of citation behavior in economics, challenging the conventional prestige-based explanation (e.g., the Matthew effect). Using a large-scale empirical analysis of 43,467 disambiguated authors and 264,436 citation links, we integrate network science with topic modeling (LDA) and semantic embedding similarity to quantify the relative influence of social proximity (especially coauthorship ties), semantic proximity (topical and conceptual alignment), and author prestige. Results show that social and semantic proximity jointly dominate routine citation formation, significantly outperforming prestige in predictive power; prestige exerts influence only in extreme high-citation cases. These findings recast citation behavior as fundamentally “proximity-driven” rather than “prestige-driven,” offering a novel paradigm for research impact assessment and evidence-based science policy design.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Citations are a key indicator of research impact but are shaped by factors beyond intrinsic research quality, including prestige, social networks, and thematic similarity. While the Matthew Effect explains how prestige accumulates and influences citation distributions, our study contextualizes this by showing that other mechanisms also play a crucial role. Analyzing a large dataset of disambiguated authors (N=43,467) and citation linkages (N=264,436) in U.S. economics, we find that close ties in the collaboration network are the strongest predictor of citation, closely followed by thematic similarity between papers. This reinforces the idea that citations are not only a matter of prestige but mostly of social networks and intellectual proximity. Prestige remains important for understanding highly cited papers, but for the majority of citations, proximity--both social and semantic--plays a more significant role. These findings shift attention from extreme cases of highly cited research toward the broader distribution of citations, which shapes career trajectories and the production of knowledge. Recognizing the diverse factors influencing citations is critical for science policy, as this work highlights inequalities that are not based on preferential attachment, but on the role of self-citations, collaborations, and mainstream versus no mainstream research subjects.

Problem

• Examining how social collaboration networks strongly predict citation behavior
• Analyzing thematic similarity between papers as a key factor in citation likelihood
• Comparing prestige versus social/semantic proximity in explaining citation distributions

Innovation

• Leveraging collaboration network ties for citation prediction
• Prioritizing thematic similarity over prestige in citation analysis
• Emphasizing social-semantic proximity in mainstream citation dynamics