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On social media, the boundaries between people's private and public lives often blur. The need to navigate both roles, which are governed by distinct norms, impacts how individuals conduct themselves online, and presents methodological challenges for researchers. We conduct a systematic exploration on how an organization's official Twitter accounts and its members' personal accounts differ. Using a climate change Twitter data set as our case, we find substantial differences in activity and connectivity across the organizational levels we examined. The levels differed considerably in their overall retweet network structures, and accounts within each level were more likely to have similar connections than accounts at different levels. We illustrate the implications of these differences for applied research by showing that the levels closer to the core of the organization display more sectoral homophily but less triadic closure, and how each level consists of very different group structures. Our results show that the common practice of solely analyzing accounts from a single organizational level, grouping together all levels, or excluding certain levels can lead to a skewed understanding of how organizations are represented on social media., Comment: 21 pages, 10 figures

The related concepts of partisan belief systems, issue alignment, and partisan sorting are central to our understanding of politics. These phenomena have been studied using measures of alignment between pairs of topics, or how much individuals' attitudes toward a topic reveal about their attitudes toward another topic. We introduce a higher-order measure that extends the assessment of alignment beyond pairs of topics by quantifying the amount of information individuals' opinions on one topic reveal about a set of topics simultaneously. Our multiway alignment measure indicates how much individuals' opinions on multiple topics align into a single ideological divide. Applying this approach to legislative voting behavior reveals that parliamentary systems typically exhibit similar multiway alignment characteristics, but can change in response to shifting intergroup dynamics. In American National Election Studies surveys, our approach reveals a growing significance of party identification together with a consistent rise in multiway alignment over time. Similarly, the growing multiway alignment among topical issues in Finnish online discussions suggests a trend towards a more ideologically driven political landscape. Our case studies demonstrate that the multiway alignment measure is a versatile tool for understanding societal polarization and partisan belief systems across diverse domains.

Existing methods for quantifying polarization in social networks typically report a single value describing the amount of polarization in a social system. While this approach can be used to confirm the observation that many societies have witnessed an increase in political polarization in recent years, it misses the complexities that could be used to understand the reasons behind this phenomenon. Notably, opposing groups can have unequal impact on polarization, and the elites are often understood to be more divided than the masses, making it critical to differentiate their roles in polarized systems. We propose a method to characterize these distinct hierarchies in polarized networks, enabling separate polarization measurements for these groups within a single social system. Applied to polarized topics in the Finnish Twittersphere surrounding the 2019 and 2023 parliamentary elections, our analysis reveals valuable insights: 1) The impact of opposing groups on observed polarization is rarely balanced, and 2) while the elite strongly contributes to structural polarization and consistently display greater alignment across various topics, the masses have also recently experienced a surge in issue alignment, a special form of polarization. Our findings suggest that the masses may not be as immune to an increasingly polarized environment as previously thought.

Coalitions are central to politics, including government formation, international relations, and public policy. Coalitions emerge when actors engage one another across multiple relational contexts, but existing literature often approaches coalitions in singular contexts. We introduce complex coalitions, a theoretical-methodological framework that emphasises the relevance of multiple contexts and cross-context dependencies in coalition politics. We also implement tools to statistically infer such coalition structures using multilayer networks. To demonstrate the usefulness of our approach, we compare coalitions among Finnish organisations engaging in climate politics across three con-texts: resource coordination, legacy media discourse, and social media communication. We show that considering coalitions as complex and accounting for cross-context dependencies improves the empirical validity of coalition studies. In our case study, the three contexts represent complementary, but not congruent, channels for enacting coalitions. In conclusion, we argue that the complex coalitions approach is useful for advancing understanding of coalitions in different political realms.

We propose a hybrid heuristic algorithm for solving the Heaviest k-Subgraph Problem in online social networks -- a combinatorial graph optimization problem central to many important applications in weighted social networks, including detection of coordinated behavior, maximizing diversity of a group of users, and detecting social groups. Our approach builds upon an existing metaheuristic framework known as Variable Neighborhood Search and takes advantage of empirical insights about social network structures to derive an improved optimization heuristic. We conduct benchmarks in both real life social networks as well as synthetic networks and demonstrate that the proposed modifications match and in the majority of cases supersede those of the current state-of-the-art approaches., Comment: 10 pages, 2 figures, implementation available at https://github.com/Decitizen/OVNS

How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling with Elo scoring, and demonstrate that it outperforms the ubiquitous majority-voting method in reducing both types of measurement error. To assess the performance of the labelling approaches, we constructed an agent-based model of crowdsourced labelling that lets us introduce different types of subjectivity into the tasks. We find that under most conditions with task subjectivity, the comparison approach produced higher $f_1$ scores. Further, the comparison approach is less susceptible to inflating bias, which majority voting tends to do. To facilitate applications, we show with simulated and real-world data that the number of required random comparisons for the same classification accuracy scales log-linearly $O(N \log N)$ with the number of labelled items. We also implemented the Elo system as an open-source Python package., Comment: Accepted for publication at ACM CSCW 2023

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, attitude, or language. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we first introduce an effective implementation of the inference method that can achieve higher accuracy than existing implementations in comparable runtime. Then, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the method, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our findings include that the inference accuracy varies extremely with network density, and that the method fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the method before running the inference on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation supports further testing under personalized settings., Comment: 10 pages, 4 figures, submitted to TheWebConf 2022

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 different methods for measuring structural polarization.

The significance and influence of US Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology -- the citation exponential random graph model, for which we provide user-friendly software -- enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations., Comment: 34 pages, 10 figures

Characterising the spreading of ideas within echo chambers is essential for understanding polarisation. In this paper, we explore the characteristics of popular and viral content in climate change discussions on Twitter around the 2019 announcement of the Nobel Peace Prize, where we find the retweet network of users to be polarised into two well-separated groups of activists and sceptics. Operationalising popularity as the number of retweets and virality as the spreading probability inferred using an independent cascade model, we find that the viral themes echo and differ from the popular themes in interesting ways. Most importantly, we find that the most viral themes in the two groups reflect different types of bonds that tie the community together, yet both function to enhance ingroup connections while repulsing outgroup engagement. With this, our study sheds light, from an information spreading perspective, on the formation and upkeep of echo chambers in climate discussions.

Prior research shows that public opinion on climate politics sorts along partisan lines. However, they leave open the question of whether climate politics and other politically salient issues exhibit tendencies for issue alignment, which the political polarization literature identifies as among the most deleterious aspects of polarization. Using a network approach and social media data from the Twitter platform, we study polarization of public opinion toward climate politics and ten other politically salient topics during the 2019 Finnish elections as the emergence of opposing groups in a public forum. We find that while climate politics is not particularly polarized compared to the other topics, it is subject to partisan sorting and issue alignment within the universalist-communitarian dimension of European politics that arose following the growth of right-wing populism. Notably, climate politics is consistently aligned with the immigration issue, and temporal trends indicate that this phenomenon will likely persist., Comment: Working paper, 30 pages, 6 figures