Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere

Chen, Ted Hsuan Yun; Salloum, A.; Gronow, Antti; Ylä‐Anttila, Tuomas; Kivelä, Mikko

doi:10.48550/arxiv.2007.02706

Cited by 3 publications

(7 citation statements)

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“…In addition to networks simulated with models, we use real-world data from Twitter from three sources for a total of 203 networks. First, from Chen et al, we used 150 networks constructed from the most popular hashtags during the 2019 Finnish Parliamentary Elections [9]. These were constructed based on single hashtags (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…1: (1) social network construction, (2) finding the two groups via graph partitioning, and (3) quantifying the separation between the two groups. Kernel for density estimation Boundary Polarization (BP) [30] [-0.5, 0.5] -Dipole polarization (DP) [50] [0, 1] % of influencers in each group E-I Index (EI) [38] [-1, 1] -Adaptive E-I Index (AEI) [9] [-1, 1] -Modularity (Q) [65] [-0.5, 1] -When measuring polarization in a social system, constructing a network representation of that system that allows us to identify the existence of polarized group structures apart from other types of interactions is a crucial step. It defines the appropriate bounds of the system, both in terms of which units are included and which interactions are measured.…”

Section: Construct Graphmentioning

confidence: 99%

“…A social network is suitable for polarization analysis with the methods described here if the link between two nodes indicates a positive relationship, such as friendship, preference similarity, endorsement, or agreement [1,11,25,27]. Bounding the network to the appropriate set of interactions is key, as it has been observed that simultaneously measuring unrelated topics will yield lower structural polarization scores regardless of how polarized each of the topics are [9].…”

Section: Construct Graphmentioning

confidence: 99%

“…They are typically motivated by characterizing the difference between interaction patterns that occur within groups and those that cross groups. At its simplest, polarization has been measured [8,32] by comparing the difference in frequency of external to internal links using the EI-index [38] and similar density-based scores [9], disregarding the internal structure of the groups and how the links between them are placed.…”

Section: Construct Graphmentioning

confidence: 99%

“…Applications of structural polarization scores to real networks have spanned many domains, including the studies of political party dynamics [4,37,49,52,65], cohesion and voting behavior [2,9,14,65], political events [12,15,50,58,66], and online echo chambers [12,13,26]. Despite their widespread application, systematic assessments of how well these structural polarization measures perform are rare.…”

Section: Introductionmentioning

confidence: 99%

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Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Salloum,

Chen,

Kivelä

2021

Preprint

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Quantifying the amount of polarization is crucial for understanding and studying political polarization in political and social systems. Several methods are used commonly to measure polarization in social networks by purely inspecting their structure. We analyse eight of such methods and show that all of them yield high polarization scores even for random networks with similar density and degree distributions to typical real-world networks. Further, some of the methods are sensitive to degree distributions and relative sizes of the polarized groups. We propose normalization to the existing scores and a minimal set of tests that a score should pass in order for it to be suitable for separating polarized networks from random noise. The performance of the scores increased by 38%-220% after normalization in a classification task of 203 networks. Further, we find that the choice of method is not as important as normalization, after which most of the methods have better performance than the best-performing method before normalization. This work opens up the possibility to critically assess and compare the features and performance of structural polarization methods.

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Section: Methodsmentioning

confidence: 99%

Section: Construct Graphmentioning

confidence: 99%

Section: Construct Graphmentioning

confidence: 99%

Section: Construct Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Salloum,

Chen,

Kivelä

2021

Preprint

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Privacy and uniqueness of neighborhoods in social networks

Romanini

Lehmann

Kivelä

2021

Sci Rep

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The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such data, however, can lead to serious privacy issues, because individuals could be re-identified, not only based on possible nodes’ attributes, but also from the structure of the network around them. The risk associated with re-identification can be measured and it is more serious in some networks than in others. While various optimization algorithms have been proposed to anonymize networks, there is still only a limited theoretical understanding of which network features are important for the privacy problem. Using network models and real data, we show that the average degree of networks is a crucial parameter for the severity of re-identification risk from nodes’ neighborhoods. Dense networks are more at risk, and, apart from a small band of average degree values, either almost all nodes are uniquely re-identifiable or they are all safe. Our results allow researchers to assess the privacy risk based on a small number of network statistics which are available even before the data is collected. As a rule-of-thumb, the privacy risks are high if the average degree is above 10. Guided by these results, we explore sampling of edges as a strategy to mitigate the re-identification risk of nodes. This approach can be implemented during the data collection phase, and its effect on various network measures can be estimated and corrected using sampling theory. The new understanding of the uniqueness of neighborhoods in networks presented in this work can support the development of privacy-aware ways of designing network data collection procedures, anonymization methods, and sharing network data.

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Argumentative practices and patterns in debating climate change on Twitter

Foderaro

Lorentzen

2022

AJIM

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PurposeThe purpose of this paper is to investigate practices of argumentation on Twitter discussions about climate change.Design/methodology/approachConversational threads were collected from the Twitter API. Fundamental concepts from argumentation theory and linking practices were operationalised through a coding schema for content analysis. Tweets were analysed in the context of the discussions and coded according to their argumentative approach, interaction type and argumentation stage. Linked and embedded sources were analysed in order to find how they were used in arguments, the plausibility and soundness of the message, the consistency and trustworthiness of the linked source and its adequacy with the target audience.FindingsAmong the interactions between arguers, this study found five typical practices and several patterns involving the dynamics of the conversations, the strategy of the argumentation and the linking practices. Although the rhetorical approach was prominent, the agreement was rarely achieved. The arguers used a variety of sources to justify or support their positions, often embedding non-textual content. These linking practices, together with the strategy adopted and the topics discussed, suggest the involvement of a multiple audience engaged in discussing ad lib scientific artefacts, topics and outputs.Originality/valueWhile Twitter has been the focus for many research papers, the conversational threads have been given little attention so far. With the Twitter API making conversations more accessible for research, this paper does not only give insight into multiple audience group argumentation dynamics but also provides a method to study the conversations from an argumentation theory perspective.

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Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere

Cited by 3 publications

References 0 publications

Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Privacy and uniqueness of neighborhoods in social networks

Argumentative practices and patterns in debating climate change on Twitter

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