For example, user discussions around particular topics in a social media application may help disseminate ideas and foster social movements even in the physical world. In turn, those patterns drive and favor an underlying (collective) phenomenon. Multiple collective behavioral patterns may emerge from such interactions without a predefined social structure that explains them. These loosely organized people interact with each other driven by common interests and goals or hidden factors (e.g., coordinated actions). For example, groups of users may contribute to disseminating opinions and pieces of information as they produce content in social media applications. Collective behavior emerges in several contexts in both online and physical worlds, and it may drive social, cultural, economic, and political phenomena. A possible definition of collective behavior relates it to “the kinds of activities engaged in sizable but loosely organized groups of people”. The notion of collective behavior has been widely studied in domains such as Sociology and Psychology. įunding: This work has been financed by the Compagnia di San Paolo in the context of the Polito’s Joint Projects for the Internationalization of Research, Center at Politecnico di Torino, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG).Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data and codes that are not from third parties underlying the results presented in the study are available on the following link. Received: FebruAccepted: AugPublished: September 15, 2022Ĭopyright: © 2022 Gomes Ferreira et al. (2022) On network backbone extraction for modeling online collective behavior. 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.Ĭitation: Gomes Ferreira CH, Murai F, Silva APC, Trevisan M, Vassio L, Drago I, et al. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. 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. 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. 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. Each technique has its specific assumptions and procedure to extract the backbone. 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. 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. However, only a fraction of edges contribute to the actual investigation. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information.
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