A Network-Driven Framework for Bidimensional Analysis of Information Dissemination on Social Media Platforms

Network modeling has become a foundational approach for analyzing information dissemination on social media platforms. Moreover, backbone extraction techniques, designed to isolate the most relevant structural patterns in noisy networks, have been widely employed to identify salient interactions, especially in the context of coordinated behavior and campaign detection. However, most of these approaches rely on a one-dimensional perspective, focusing primarily on interaction volume while neglecting temporal dynamics that are crucial to understanding how content spreads in real time. This limited view can obscure important distinctions between dissemination strategies that are fast but sparse or slow but voluminous. To address this gap, we introduce a bidimensional framework that integrates both interaction volume and speed, enabling a more comprehensive modeling of dissemination dynamics. Our approach applies state-of-the-art backbone extraction techniques independently to each dimension, classifying edges into four distinct dissemination profiles. This classification provides new analytical affordances for exploring both structural and textual patterns of information flow across social platforms. Applied to two case studies on Twitter/X and Telegram, the framework reveals contrasting dissemination strategies across platforms and shows how different edge classes contribute to the amplification of specific narratives. These findings advance the study of information dynamics by offering a finer-grained, multidimensional perspective on user interactions in complex social networks.