Characterizing Information Diffusion Across Contexts Through Frequency, Diversity, Interaction, and Temporal Dynamics

• Jaqueline Faria de Oliveira PUC Minas / Instituto Kunumi
• Humberto T. Marques-Neto PUC Minas

DOI: https://doi.org/10.5753/webmedia.2026.30775

Resumo

Information adoption in online social networks may be preceded by different patterns of social exposure, yet how these patterns vary across information environments remains insufficiently understood. In this paper, we extend a previously proposed ego-network analytical basis into a unified framework for comparative information diffusion analysis. The framework integrates Frequency, Diversity, Interaction, and Temporal Dynamics. The Interaction Dimension is operationalized through the proposed Reinforced Influence Index (RII), which characterizes exposure organization by relating the observed exposure volume to the number of distinct adopting followees. We evaluate the framework using two large-scale ego-network datasets collected from Twitter (now X) during the 2019 European Parliament election in France and the COVID-19 pandemic in Brazil. The analysis is conducted at the item, user, and topic levels to characterize exposure patterns preceding adoption across both datasets. Our results show that repeated and diversified social exposure exhibit distinct patterns across content types, topics, and datasets. RII further reveals differences in exposure organization, including higher User-level exposure-per-source ratios in the Brazilian dataset, while temporal analyses reveal differences in the length and organization of exposure sequences preceding adoption. Overall, the findings reveal both recurring exposure patterns and substantial heterogeneity across analytical dimensions and empirical contexts. The proposed framework provides a unified and interpretable perspective for studying information diffusion and supports comparative analyses across online social systems.

Palavras-chave: information diffusion, threshold models, repeated influence, user heterogeneity, online social networks, ego networks

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