ICEA - Instituto de Ciências Exatas e Aplicada
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Navegando ICEA - Instituto de Ciências Exatas e Aplicada por Autor "Almeida, Jussara Marques de"
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Item A network-driven study of hyperprolific authors in computer science.(2024) Vieira, Vinicius da Fonseca; Ferreira, Carlos Henrique Gomes; Almeida, Jussara Marques de; Moreira, Edré; Laender, Alberto Henrique Frade; Meira Júnior, Wagner; Gonçalves, Marcos AndréScientific authors’ collaborations are influenced by various factors, such as their field, geographic region, and institutional role. Here we focus on a group of authors whose patterns of publications greatly deviate from the average, previously referred as hyperprolific authors. Prior studies have investigated the emergence of hyperprolific authors and their productivity. In this article, we focus on the role of coauthorships in the hyperprolific authors’ publication profiles. Based on a network model that represents researchers as nodes and weighted edges as the number of collaborations between a pair of researchers, we argue that not all network edges have the same importance to characterize the existence of hyperprolific authors. As such, we filter out “sporadic” coauthorships, revealing an underlying structure composed only of edges representing consistent and repetitive collaborations, named as the network backbone. Our network-oriented methodology was applied to a dataset of Computer Science publications extracted from DBLP, covering an 11-year period from 2010 to 2020. Our experiments reveal significant topological differences between the full coauthorship networks and backbones, concerning only authors with very off-the-pattern profiles. We also show that hyperprolific authors are consistently more likely to exhibit off-the-pattern coauthorships and that an author’s probability of being present in the backbone substantially increases with her topological proximity to a hyperprolific author. Finally, we investigate how authors’ hyperprolific profiles correlate to their presence in the backbone.Item Análise da percepção do uso de cigarros eletrônicos no Brasil por meio de comentários no YouTube.(2024) Dias , Aline Martins; Tanure, Richardy Rodrigues; Almeida, Jussara Marques de; Lima, Helen de Cássia Sousa da Costa; Ferreira, Carlos Henrique Gomes; LanaThe rise of video platforms such as YouTube has revolutionized information sharing and influenced social habits and product consumption. At the same time, the diverse global regulatory landscape and ongoing studies on the health effects of e-cigarettes have led to intense and controversial debates. Amidst this, the production of content about e-cigarettes on the internet is rapidly increasing, particularly on YouTube, one of the most popular video platforms globally and the most popular in Brazil. Despite the growing body of research focused on understanding online interactions with ecigarettes, there is a lack of comprehensive and detailed temporal analyses that capture the dynamics of the debate and people’s stance towards these products, especially in the Brazilian context. To address this gap, our study aims to investigate the popularity and acceptance of e-cigarettes in Brazil by analyzing YouTube videos and their associated comments. We collected an extensive dataset of videos, channels, comments and their metadata from 2018 to 2023. Our methodology involved analyzing production and engagement metrics, and developing a deep learning-based stance detection model to estimate people’s stance (approval or disapproval) based on comments and quantify the temporal dynamics of these attitudes over the years. Our findings reveal a significant increase in content production and user engagement, indicating a growing public interest, with a notable increase in approving comments on the product. This study fills previous research gaps by offering a comprehensive and pioneering overview of e-cigarette use and public perception in Brazil, emphasizing the need for a more informed discussion among society and regulatory bodies.Item Modeling dynamic ideological behavior in political networks.(2019) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Souza, Breno Matos de; Almeida, Jussara Marques deIn 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.Item 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 deCollective 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.Item 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, IdilioInstagram 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.Item 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 deMotivated 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.