Understanding Online Political Networks: The Case of the Far-Right and Far-Left in Greece

Agathangelou, Pantelis; Katakis, Ioannis; Rori, Lamprini; Gunopulos, Dimitrios; Richards, Ben

doi:10.1007/978-3-319-67217-5_11

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…Empirical evidence suggests that, if a heterogeneous group containing users with two opposing views has a non-zero cross-view retweet rate, it will end up as two polarized communities (Conover et al, 2011). Following this logic, the studies of political polarization result in predefined binary descriptions of polarized communitiessee Morales, Borondo, Losada, and Benito (2015) for Venezuela; Agathangelou, Katakis, Rori, Gunopulos, and Richards (2017) for Greece; or Weber, Garimella, and Batayneh (2013) for Egypt.…”

Section: Political Polarization Studies and The Current Research Gapsmentioning

confidence: 99%

Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts

Bodrunova¹,

Blekanov

Smoliarova³

et al. 2019

MaC

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Studies of political polarization in social media demonstrate mixed evidence for whether discussions necessarily evolve into left and right ideological echo chambers. Recent research shows that, for political and issue-based discussions, patterns of user clusterization may differ significantly, but that cross-cultural evidence of the polarization of users on certain issues is close to non-existent. Furthermore, most of the studies developed network proxies to detect users’ grouping, rarely taking into account the content of the Tweets themselves. Our contribution to this scholarly discussion is founded upon the detection of polarization based on attitudes towards political actors expressed by users in Germany, the USA and Russia within discussions on inter-ethnic conflicts. For this exploratory study, we develop a mixed-method approach to detecting user grouping that includes: crawling for data collection; expert coding of Tweets; user clusterization based on user attitudes; construction of word frequency vocabularies; and graph visualization. Our results show that, in all the three cases, the groups detected are far from being conventionally left or right, but rather that their views combine anti-institutionalism, nationalism, and pro- and anti-minority views in varying degrees. In addition to this, more than two threads of political debate may co-exist in the same discussion. Thus, we show that the debate that sees Twitter as either a platform of ‘echo chambering’ or ‘opinion crossroads’ may be misleading. In our opinion, the role of local political context in shaping (and explaining) user clusterization should not be under-estimated.

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Section: Political Polarization Studies and The Current Research Gapsmentioning

confidence: 99%

Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts

Bodrunova¹,

Blekanov

Smoliarova³

et al. 2019

MaC

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“…as overall more unstable. This result is particularly relevant also in light of the many studies that specifically focus on strongly polarized communities, such as those on political polarization, fringe and extreme behaviors, and far-right online groups (Agathangelou et al 2017;Zannettou et al 2018;Morstatter et al 2018).…”

Section: Rq3: Archetypes and Drivers Of User Behaviormentioning

confidence: 87%

Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Tardelli¹,

Nizzoli²,

Tesconi³

et al. 2023

Preprint

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Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence.

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“…Since 2012, the values of NMI fall around 0.6, reflecting greater stability in community membership. For the US, on the other hand, both persistence and NMI are very large, approaching the maximum (1). Almost all members persist in their polarized communities over the years.…”

Section: Temporal Analysismentioning

confidence: 98%

“…The results revealed inefficient coalitions with the government as parties that make such coalitions have members distributed in different ideological communities over time. Orthogonally, others have investigated the ideology of political members and users through profiles of social networks [1,12,34].…”

Section: Related Workmentioning

confidence: 99%

Analyzing Dynamic Ideological Communities in Congressional Voting Networks

Ferreira

Matos

Almeira

2018

Lecture Notes in Computer Science

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We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.

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Understanding Online Political Networks: The Case of the Far-Right and Far-Left in Greece

Cited by 9 publications

References 20 publications

Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts

Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts

Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Analyzing Dynamic Ideological Communities in Congressional Voting Networks

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