Browsing by Author "Almeida, Jussara Marques de"

Now showing 1 - 6 of 6
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    Modeling dynamic ideological behavior in political networks.
    (2019) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Souza, Breno Matos de; Almeida, Jussara Marques de
    In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. While the former is an example of a highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose a temporal-ideological space model, based on temporal vertex embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members’ voting patterns. Our results unveil very distinct patterns across the two case studies, both in terms of structural and dynamic properties.
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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.
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    SyGAR – A synthetic data generator for evaluating name disambiguation methods.
    (2009) Ferreira, Anderson Almeida; Gonçalves, Marcos André; Almeida, Jussara Marques de; Laender, Alberto Henrique Frade; Veloso, Adriano Alonso
    Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the digital library community. Several methods have been proposed in the literature, but none of them provides the perfect solution for the problem. More importantly, basically all of these methods were tested in limited and restricted scenarios , which raises concerns about their practical applicability. In this work, we deal with these limitation s by proposing a synthetic generator of ambiguous authors hip records called SyGAR . The generator was validated against a gold standard collection of d is ambiguated records , and aplied to evaluate three d is ambiguation method s in a relevant scenario.
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    A tool for generating synthetic authorship records for evaluating author name disambiguation methods.
    (2012) Ferreira, Anderson Almeida; Gonçalves, Marcos André; Almeida, Jussara Marques de; Laender, Alberto Henrique Frade; Veloso, Adriano Alonso
    The author name disambiguation task has to deal with uncertainties related to the possible many-to-many correspondences between ambiguous names and unique authors. Despite the variety of name disambiguation methods available in the literature to solve the problem, most of them are rarely compared against each other. Moreover, they are often evaluated without considering a time evolving digital library, susceptible to dynamic (and therefore challenging) patterns such as the introduction of new authors and the change of research-ers’ interests over time. In order to facilitate the evaluation of name disambiguation meth-ods in various realistic scenarios and under controlled conditions, in this article we propose SyGAR, a new Synthetic Generator of Authorship Records that generates citation records based on author profiles. SyGAR can be used to generate successive loads of citation records simulating a living digital library that evolves according to various publication pat-terns. We validate SyGAR by comparing the results produced by three representative name disambiguation methods on real as well as synthetically generated collections of citation records. We also demonstrate its applicability by evaluating those methods on a time evolving digital library collection generated with the tool, considering several dynamic and realistic scenarios.
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    Understanding mobility in networks : a node embedding approach.
    (2021) Barros, Matheus Fellipe do Carmo; Ferreira, Carlos Henrique Gomes; Santos, Bruno Pereira dos; Pereira Júnior, Lourenço Alves; Mellia, Marco; Almeida, Jussara Marques de
    Motivated by the growing number of mobile devices capable of connecting and exchanging messages, we propose a methodology aiming to model and analyze node mobility in networks. We note that many existing solutions in the literature rely on topological measurements calculated directly on the graph of node contacts, aiming to capture the notion of the node’s importance in terms of connectivity and obility patterns beneficial for prototyping, design, and deployment of mobile networks. However, each measure has its specificity and fails to generalize the node importance notions that ultimately change over time. Unlike previous approaches, our methodology is based on a node embedding method that models and unveils the nodes’ importance in mobility and connectivity patterns while preserving their spatial and temporal characteristics. We focus on a case study based on a trace of group meetings. The results show that our methodology provides a rich representation for extracting different mobility and connectivity patterns, which can be helpful for various applications and services in mobile networks.