On network backbone extraction for modeling online collective behavior.
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2022
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Abstract
Collective user behavior in social media applications often drives several important online
and offline phenomena linked to the spread of opinions and information. Several studies
have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual
investigation. Even worse, the often large number of non-relevant edges may obfuscate the
salient interactions, blurring the underlying structures and user communities that capture
the collective behavior patterns driving the target phenomenon. To solve this issue,
researchers have proposed several network backbone extraction techniques to obtain a
reduced and representative version of the network that better explains the phenomenon of
interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios
where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given
a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their
assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target
phenomenon. We validate our approach using two case studies with different requirements:
online discussions on Instagram and coordinated behavior in WhatsApp groups. We show
that each method can produce very different backbones, underlying that the choice of an
adequate method is of utmost importance to reveal valuable knowledge about the particular
phenomenon under investigation.
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FERREIRA, C. H. G. et al. On network backbone extraction for modeling online collective behavior. PLoS One, v. 17, set. 2022. Disponível em: <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274218>. Acesso em: 03 maio 2023.