Browsing by Author "Silva, Ana Paula Couto da"

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    Caracterização e análise de uma rede de ingredientes e receitas.
    (2014) Ferreira, Willyan Michel; Souza, Fabrício Benevenuto de; Merschmann, Luiz Henrique de Campos; Silva, Ana Paula Couto da; Santos, Haroldo Gambini
    A troca de receitas é um hábito de muitas pessoas. Um meio online e colaborativo de compartilhar esse tipo de informação é através de websites especializados que permitem que usuários postem receitas, comentem e avaliem receitas existentes. Apesar de extremamente populares, pouco se sabe sobre esses sistemas e os padrões de interações que eles permitem. Visando preencher essa lacuna, esse trabalho apresenta uma extensa caracterização do site Tudo Gostoso, um importante site brasileiro de compartilhamento de receitas. Para isso, nós coletamos todas as receitas existentes no site juntamente com informações associadas aos comentários e avaliações. Além de explorar as interações existentes entre os usuários do site, nosso trabalho analisa uma rede formada por ingredientes que co-ocorrem em receitas e investiga a viabilidade de se extrair possíveis alterações nas receitas a partir de comentários dos usuários do site. Nossas análises revelam padrões de uso de ingredientes fundamentais da culinária brasileira e podem ser úteis para inspirar a construção de diversas novas aplicações, como ferramentas de recomendação de receitas.
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    On network backbone extraction for modeling online collective behavior.
    (2022) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Silva, Ana Paula Couto da; Trevisan, Martino; Vassio, Luca; Drago, Idilio; Mellia, Marco; Almeida, Jussara Marques de
    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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    On the dynamics of political discussions on Instagram : a network perspective.
    (2021) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Silva, Ana Paula Couto da; Almeida, Jussara Marques de; Trevisan, Martino; Vassio, Luca; Mellia, Marco; Drago, Idilio
    Instagram has been increasingly used as a source of information especially among the youth. As a result, political figures now leverage the platform to spread opinions and political agenda. We here analyze online discussions on Instagram, notably in political topics, from a network perspective. Specifically, we investigate the emergence of communities of co-commenters, that is, groups of users who often interact by commenting on the same posts and may be driving the ongoing online discussions. In particular, we are interested in salient co-interactions, i.e., interactions of co-commenters that occur more often than expected by chance and under independent behavior. Unlike casual and accidental co-interactions which normally happen in large volumes, salient co-interactions are key elements driving the online discussions and, ultimately, the information dissemination. We base our study on the analysis of 10 weeks of data centered around major elections in Brazil and Italy, following both politicians and other celebrities. We extract and characterize the communities of co-commenters in terms of topological structure, properties of the discussions carried out by community members, and how some community properties, notably community membership and topics, evolve over time. We show that communities discussing political topics tend to be more engaged in the debate by writing longer comments, using more emojis, hashtags and negative words than in other subjects. Also, communities built around political discussions tend to be more dynamic, although top commenters remain active and preserve community membership over time. Moreover, we observe a great diversity in discussed topics over time: whereas some topics attract attention only momentarily, others, centered around more fundamental political discussions, remain consistently active over time.