Who is really in my social circle?

Leão, Jeancarlo Campos; Brandão, Michele A.; Melo, Pedro O. S. Vaz de; Laender, Alberto H. F.

doi:10.1186/s13174-018-0091-6

Cited by 9 publications

(9 citation statements)

References 73 publications

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“…This effort can allow user classification through data mining techniques to identify candidates' advocates, political bots, and other actors. Finally, regarding community detection, Leão et al [12] point out the challenge of adopting different approaches for community detection, consider additional algorithms to explore temporal aspects or identify overlapping communities, and evaluate filtered networks. Moreover, different alternatives to measure the strength of ties should be investigated.…”

Section: Discussionmentioning

confidence: 99%

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Thematic series on Social Network Analysis and Mining

Santos

Lopes

2019

J Internet Serv Appl

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Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.

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Section: Discussionmentioning

confidence: 99%

“…Leão et al [12] propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, the authors observe that social networks converge to a topology with more pure social relationships and better quality community structures.…”

Section: The Papersmentioning

confidence: 99%

Thematic series on Social Network Analysis and Mining

Santos

Lopes

2019

J Internet Serv Appl

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“…Building on prior work [ 33 , 36 , 47 , 64 , 85 , 93 ], we consider metrics of backbone quality in two categories: topological, which are closely related to network and community structure, and contextual, which refers to phenomenon-specific attributes.…”

Section: Selection and Evaluation Of Network Backbone Extraction Methodsmentioning

confidence: 99%

“…In selecting the backbone methods, we consider those that have been proposed for weighted networks and applied to the analysis of collective behavior, often in projected networks [ 21 – 23 , 31 , 33 , 35 , 56 , 64 , 76 , 77 , 79 , 83 , 84 ]. We consider methods that have been explored by prior studies, restricting our focus to those applied in social media applications.…”

Section: Introductionmentioning

confidence: 99%

On network backbone extraction for modeling online collective behavior

et al. 2022

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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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“…Some of the simplest methods of backbone extraction explore topological metrics and global properties of the graph. They typically propose to select the most important edges for the study [65,59,58,56,61]. Examples include the use of Kcore searches for dense subgraphs [66,67] and the removal of edges based on a global threshold τ, either applied directly to edge weights [68,69,70] or to more sophisticated metrics such as the neighborhood overlap of a pair of nodes [71,72].…”

Section: Network Backbone Extractionmentioning

confidence: 99%

On the dynamics of political discussions on Instagram: A network perspective

Ferreira

Murai

Silva

et al. 2021

Online Social Networks and Media

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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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Who is really in my social circle?

Cited by 9 publications

References 73 publications

Thematic series on Social Network Analysis and Mining

Thematic series on Social Network Analysis and Mining

On network backbone extraction for modeling online collective behavior

On the dynamics of political discussions on Instagram: A network perspective

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