arXiv subjects

Mikko Kivelä

Publications and source records attributed to Mikko Kivelä.

At least 19 recordsLinked to original sources

arXiv2026

Competing for a Finite Pool of Attention in Social Media? How a New Geopolitical Conflict Reshapes Engagement in Bluesky

Major geopolitical crises can rapidly reshape online public attention. Yet population-level increases in discussion volume about a new crisis reveal little about how users accommodate this new demand for attention. We study the onset of the Iran-US-Israel conflict, triggered on 28 February 2026, using longitudinal repost activity from Bluesky across four consecutive approximately three-month windows spanning the period before and after its onset; the data comprise 91.0 million unique posts and 645.5 million repost observations. We find that the new conflict reorganized participation through both reallocation among existing conflict participants and substantial activation of previously low-conflict-active users, while some previously active users reduced their conflict-related participation. Attention redistribution differed substantially across pre-existing interests: Iran-US-Israel and Israel-Palestine attention showed strong positive co-movement with little systematic relative replacement, whereas Other Political and Non-Political content more consistently lost attention share, and Russia-Ukraine exhibited weaker, heterogeneous replacement. Finally, disruption of users' broader attention allocation was substantially more prevalent among users with established attention to geopolitical conflicts than in the overall or Non-Political populations. Together, these findings show that a newly emerging conflict reorganizes online attention through turnover in who participates, selective co-attendance or replacement across topics, and disruption of broader attention patterns concentrated among users already engaged with geopolitical conflicts.

cs.SI↗

arXiv2026

The Structure of Spreading on Temporal Networks

The physics of spreading in static networks is well understood through mappings to percolation. We show that spreading dynamics on temporal networks can analogously be mapped to reachability in temporal event graphs. This provides a theoretical and computational framework for a class of processes, such as variants of the susceptible-infected-susceptible model. Without explicit simulations, through the component analysis of event graphs, we obtain epidemic prevalence and derive epidemic thresholds for temporal networks with arbitrary degree and inter-event time distributions, with significant computational advantages as compared to explicit simulations.

physics.soc-ph↗

arXiv2026

Echoes in the Sky: Computational Thematic Analysis of Online Public Discourse on Bluesky Across Trump's Reelection

As political disruption intensifies online discourse, Bluesky has become an important platform for political discussion and public reaction. In this study, we examine large-scale discourse on Bluesky related to U.S. policy developments associated with the Trump administration. Using the historical retrieval API, we collected all available posts matching Trump and related keywords from 2019 to 2026, yielding 38.5 million posts. We leverage a large language model (LLM)-assisted clustering pipeline, combined with human validation, to identify 14 interpretable thematic domains in English-language posts and 19 thematic categories across 258 executive orders (EOs) signed between January 20, 2025, and May 1, 2026. Our findings identify several dominant themes in Bluesky discourse, including executive governance, political identity, and national security, as well as recurring themes in EOs, including executive task forces, border enforcement, and foreign policy. We also find substantial variation in the persistence and volatility of issue attention, accompanied by an increasing proportion of negative sentiment over time. The dataset and resources are publicly available at https://github.com/Sensify-Lab/Echoes-in-the-Sky

cs.HC↗

arXiv2026

Climate Policy Elites' Twitter Interactions across Nine Countries

Social media is an important space for interactions between climate policy actors, with a burgeoning literature recognizing them as critical platforms of political contestation. We identified Twitter accounts associated with 904 climate change policy actors across nine countries, and collected their activities from 2017--2022, totalling 40 million activities from 16,086 accounts at different organizational levels. We studied these actors and their interactions as a polycentric governance system, emphasizing how boundary blurring between the public and private on social media platforms uniquely shapes the online policy process. Initial results show there is considerable temporal and cross-national variation in how prominent climate-related activities were, but all national policy systems generally responded to climate-related events, such as climate protests, in a similar manner. Examining patterns of interaction within and across countries, we find that these national policy systems rarely directly interact with one another, but are connected through consistently engaging with the same content produced by accounts of international organizations, climate activists, and researchers.

cs.SI↗

arXiv2026

Social Media Data Toolkit: Standardization and Anonymization of Social Network Datasets

The rapid diversification of social media platforms and the increasing restrictions on official APIs have significantly complicated cross-platform analysis. Researchers are often forced to rely on heterogeneous datasets obtained through web scraping and historical archives; however they often lack structural consistency. Prior to conducting cross-platform social media analyses, one needs to answer three critical questions: (1) What makes platforms different and similar? (2) How were the datasets collected? (3) How can we align the datasets of different platforms to conduct fair analyses? To address these questions, we introduce the Social Media Data Toolkit (\projectname{}), a comprehensive Python framework designed for the standardization, anonymization, and enrichment of social network datasets. \projectname{} unifies diverse data structures into a generic schema comprising Communities, Accounts, Posts, Actions, and Entities to facilitate multi-platform research. The framework features a configurable anonymization module to secure Personally Identifiable Information (PII) and an extendable enrichment layer that integrates Large Language Models (LLMs) and network analysis tools for downstream tasks such as stance detection and toxicity scoring without creating codebase for different datasets. We demonstrate the versatility of \projectname{} through four case studies spanning from textual analysis of the content to network analysis across platforms. To offer reproducible social media research, \projectname{} is released as an open-source tool featuring detailed documentation and practical guides for researchers at any skill-level. It can be accessed at github.com/ViralLab/SMDT and varollab.com/SMDT.

cs.SI↗

arXiv2026

Beyond Disinformation: Strategic Misrepresentation across Content, Actors, Processes, and Covertness

This article revisits the widely studied problem of disinformation and related phenomena in online social networks (OSNs) by reframing it as a broader problem of misrepresentation. While disinformation is commonly understood as the intentional spread of false content, its meaning is applied inconsistently and often remains narrowly content-focused. This obscures other forms of manipulation, such as coordinated behavior that distorts the visibility, popularity or perceived legitimacy of actors and discourses without altering content itself. We argue that such limitations hinder a coherent and operational understanding of information campaigning in OSNs. To address this, we introduce strategic misrepresentation as a unifying concept capturing the interplay between content, actors and processes in shaping collective sensemaking. We formalize this concept through a four-dimensional framework encompassing content distortion, actor distortion, process distortion and covertness, reflecting how information campaigns unfold in practice and emphasizing observable behavioral signals. Building on this conceptualization, we conduct an integrative survey of state-of-the-art detection techniques across machine learning, network science and visual analytics. By synthesizing these approaches, we demonstrate how they jointly operationalize strategic misrepresentation in a data-driven manner. Our work provides a novel pragmatic foundation for detecting, classifying, and evaluating legitimate and illegitimate information campaigns within and across OSNs.

cs.SI↗

arXiv2025

Detecting Coordinated Activities Through Temporal, Multiplex, and Collaborative Analysis

In the era of widespread online content consumption, effective detection of coordinated efforts is crucial for mitigating potential threats arising from information manipulation. Despite advances in isolating inauthentic and automated actors, the actions of individual accounts involved in influence campaigns may not stand out as anomalous if analyzed independently of the coordinated group. Given the collaborative nature of information operations, coordinated campaigns are better characterized by evidence of similar temporal behavioral patterns that extend beyond coincidental synchronicity across a group of accounts. We propose a framework to model complex coordination patterns across multiple online modalities. This framework utilizes multiplex networks to first decompose online activities into different interaction layers, and subsequently aggregate evidence of online coordination across the layers. In addition, we propose a time-aware collaboration model to capture patterns of online coordination for each modality. The proposed time-aware model builds upon the node-normalized collaboration model and accounts for repetitions of coordinated actions over different time intervals by employing an exponential decay temporal kernel. We validate our approach on multiple datasets featuring different coordinated activities. Our results demonstrate that a multiplex time-aware model excels in the identification of coordinating groups, outperforming previously proposed methods in coordinated activity detection.

cs.SI↗

arXiv2025

Homophily Within and Across Groups

Homophily -- the tendency of individuals to interact with similar others -- shapes how networks form and function. Yet existing approaches typically collapse homophily to a single scale, either one parameter for the whole network or one per community, thereby detaching it from other structural features. Here, we introduce a maximum-entropy random graph model that moves beyond these limits, capturing homophily across all social scales in the network, with parameters for each group size. The framework decomposes homophily into within- and across-group contributions, recovering the stochastic block model as a special case. As an exponential-family model, it fits empirical data and enables inference of group-level variation of homophily that aggregate metrics miss. The group-dependence of homophily substantially impacts network percolation thresholds, altering predictions for epidemic spread, information diffusion, and the effectiveness of interventions. Ignoring such heterogeneity risks systematically misjudging connectivity and dynamics in complex systems.

physics.soc-ph↗

arXiv2025

Block-corrected Modularity for Community Detection

Unknown node attributes in complex networks may introduce community structures that are important to distinguish from those driven by known attributes. We propose a block-corrected modularity that discounts given block structures present in the network to reveal communities masked by them. We show analytically how the proposed modularity finds the community structure driven by an unknown attribute in a simple network model. Further, we observe that the block-corrected modularity finds the underlying community structure on a number of simple synthetic network models while methods using different null models fail. We develop an efficient spectral method as well as two Louvain-inspired fine-tuning algorithms to maximize the proposed modularity and demonstrate their performance on several synthetic network models. Finally, we assess our methodology on various real-world citation networks built using the OpenAlex data by correcting for the temporal citation patterns.

physics.soc-ph↗

arXiv2025

Politics and polarization on Bluesky

Online political discourse is increasingly shaped not by a few dominant platforms but by a fragmented ecosystem of social media spaces, each with its own user base, target audience, and algorithmic mediation of discussion. Such fragmentation may fundamentally change how polarization manifests online. In this study, we investigate the characteristics of political discourse and polarization on the emerging social media site Bluesky. We collect all activity on the platform between December 2024 and May 2025 to map out the platform's political topic landscape and detect distinct polarization patterns. Our comprehensive data collection allows us to employ a data-driven methodology for identifying political themes, classifying user stances, and measuring both structural and content-based polarization across key topics raised in English-language discussions. Our analysis reveals that approximately 13% of Bluesky posts engage with political content, with prominent topics including international conflicts, U.S. politics, and socio-technological debates. We find high levels of structural polarization across several salient political topics. However, the most polarized topics are also highly imbalanced in the numbers of users on opposing sides, with the smaller group consisting of only 1-2% of the users. While discussions in Bluesky echo familiar political narratives and polarization trends, the platform exhibits a more politically homogeneous user base than was typical prior to the current wave of platform fragmentation.

cs.SI↗

arXiv2025

Strength and weakness of disease-induced herd immunity in networks

When a fraction of a population becomes immune to an infectious disease, the population-wide infection risk decreases nonlinearly due to collective protection, known as herd immunity. Some studies based on mean-field models suggest that natural infection in a heterogeneous population may induce herd immunity more efficiently than homogeneous immunization. However, we theoretically show that this is not necessarily the case when the population is modeled as a network instead of using the mean-field approach. We identify two competing mechanisms driving disease-induced herd immunity in networks: the biased distribution of immunity toward socially active individuals enhances herd immunity, while the topological localization of immune individuals weakens it. The effect of localization is stronger in networks embedded in a low-dimensional space, which can make disease-induced immunity less effective than random immunization. Our results highlight the role of networks in shaping herd immunity and call for a careful examination of model predictions that inform public health policies.

physics.soc-ph↗

arXiv2025

Node-reconfiguring multilayer networks of human brain function

Functional brain network properties are heavily influenced by how the the network nodes are defined. A common approach uses Regions of Interest (ROIs), i.e., predetermined collections of functional magnetic resonance imaging (fMRI) measurement voxels, as nodes. Their definition is always a compromise, as static ROIs cannot capture the dynamics and temporal reconfigurations of the brain areas. Consequently, the ROIs do not align with the functionally homogeneous regions, which can explain the low functional homogeneity values observed for the ROIs. This is in violation of the underlying homogeneity assumption in functional brain network analysis pipelines, which can cause serious problems such as spurious network structure. We introduce the node-reconfiguring multilayer network model, where nodes represent ROIs with boundaries optimized for high functional homogeneity in each time window. In this representation, network layers correspond to time windows, intralayer links depict functional connectivity between ROIs, and interlayer links quantify the overlap between ROIs on different layers. The ROI optimization approach increases functional homogeneity notably, yielding an over 10-fold increase in the fraction of ROIs with high homogeneity compared to static ROIs from the Brainnetome atlas. The optimized ROIs reorganize non-trivially at short time scales of consecutive time windows and across several windows. The amount of reorganization across time windows is connected to intralayer hubness: ROIs with intermediate levels of reorganization have stronger intralayer links than extremely stable or unstable ROIs. Our results demonstrate that reconfiguring parcellations yield more accurate network models of brain function. This supports the ongoing paradigm shift towards the chronnectome that sees the brain as a set of sources with continuously reconfiguring spatial and connectivity profiles.

q-bio.NC↗

arXiv2025

Multiway Alignment of Political Attitudes

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. Applying this approach to legislative voting behavior shows 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.

cs.SI↗

arXiv2024

Anatomy of Elite and Mass Polarization in Social Networks

In the political arena of social platforms, opposing factions of varying sizes show asymmetrical patterns, and elites and masses within these groups have divergent motivations and influence,challenging simplistic views of polarization. Yet, existing methods for quantifying polarization reduce division to a single value, assuming uniform distribution of polarization online. While this approach can confirm the observed increase in political polarization in many societies, it overlooks complexities that could explain this phenomenon. Notably, opposing groups can have unequal impacts on polarization, and the literature shows division between elites and the masses is a critical factor to consider. We propose a method to decompose existing polarization measures in order to quantify the role of groups, determined by these distinct hierarchies, in the total polarization value. We applied this method to polarized topics in the Finnish Twittersphere surrounding the 2019 and 2023parliamentary elections. Our analysis reveals two key insights: 1) The impact of opposing groups on observed polarization is rarely balanced, and 2) while elites strongly contribute to structural polarization and consistently display greater alignment across various topics, the masses have also recently experienced a surge in issue alignment, a stronger form of polarization. Our findings suggest that the masses may not be as immune to an increasingly polarized environment as previously thought. This research provides a more nuanced understanding of polarization dynamics, offering potential insights into its underlying mechanisms and evolution

cs.SI↗

arXiv2024

Integrated or Segregated? User Behavior Change after Cross-Party Interactions on Reddit

It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring activity in out-party communities, which is likely part of a more general dynamic of feedback-boosted engagement.

cs.SI↗

arXiv2024

Limits of Multilayer Diffusion Network Inference in Social Media Research

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, language, or attitude. 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 conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, 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 results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it 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 inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.

cs.SI↗

arXiv2024

My Views Do Not Reflect Those of My Employer: Differences in Behavior of Organizations' Official and Personal Social Media Accounts

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.

cs.SI↗

arXiv2024

Multiplexity is temporal: effects of social times on network structure

Large-scale social networks constructed using contact metadata have been invaluable tools for understanding and testing social theories of society-wide social structures. However, multiplex relationships explaining different social contexts have been out of reach of this methodology, limiting our ability to understand this crucial aspect of social systems. We propose a method that infers latent social times from the weekly activity of large-scale contact metadata, and reconstruct multilayer networks where layers correspond to social times. We then analyze the temporal multiplexity of ties in a society-wide communication network of millions of individuals. This allows us to test the propositions of Feld's social focus theory across a society-wide network: We show that ties favour their own social times regardless of contact intensity, suggesting they reflect underlying social foci. We present a result on strength of monoplex ties, which indicates that monoplex ties are bridging and even more important for global network connectivity than the weak, low-contact ties. Finally, we show that social times are transitive, so that when egos use a social time for a small subset of alters, the alters use the social time among themselves as well. Our framework opens up a way to analyse large-scale communication as multiplex networks and uncovers society-level patterns of multiplex connectivity.

physics.soc-ph↗