Lobbying is often perceived as a tool for entities with large financial or social capital to influence policymaking, potentially sidelining ordinary citizens’ interests. However, large-scale quantitative studies confirming or rejecting this perception are lacking. Indeed, most existing studies survey the actions and the interactions of small samples of stakeholders or policymakers. This paper leverages on a comprehensive dataset of face-to-face interactions between high level European Commission (EC) representatives and stakeholders to provide a detailed analysis of the EC lobbying network. More specifically, we examine whether there exist a highly interconnected core of actors that could dominate the network.

Indeed, network theory is particularly well-suited for studying power concentration in lobbying contexts. First, topological measures, like rich club coefficients, offers a concrete evidence for the existence of a hierarchical structure within the network. Second, it permits the identification of central actors and of the determinants of centrality. Third, integrating higher-order interactions offers insights into coalition formation and collaborative lobbying strategies.

Accordingly, we model these interactions by constructing a hypergraph where the nodes represent EC officials and stakeholders, and a hyperedge connects entities that participate in the same meetings. This hypergraphic perspective allows us to analyze both the micro and the macro structures of interactions. We apply this approach both to the full network containing stakeholders and policy-makers and to the network of stakeholders only. Our analysis highlights the presence of a robust core-periphery structure, with a few well-connected entities that occupy the center of the network and enjoy a stable integration in the EC policy-making process. Examination of the core composition reveals that companies and trade associations maintain closer relationships with the EC, while NGOs show increased core presence in the stakeholder-only network. This suggests different engagement strategies among stakeholder groups, with NGOs potentially focusing more on coalition-building and shared meetings. A regression analysis of company centrality identifies several determinants. The number of full-time equivalent staff dedicated to lobbying activities and company size are strongly associated with higher centrality. Companies with EU-based headquarters and those with global or European levels of interest also exhibit significantly higher centrality. These findings provide quantitative evidence supporting the perception of lobbying as a tool dominated by well-connected actors, while also revealing heterogeneous lobbying strategies across stakeholder groups. Our findings can be used to assess the performance of the EU consultation process with respect to its objectives of increasing the legitimacy of the European policy process and of maintaining an open, transparent and regular dialogue with stakeholders.

In nascent policy subsystems, political actors have a hard time identifying coalition partners, given that knowledge about subsystem boundaries (both in terms of relevant issue and actors) are unknown, and the belief systems and policy preferences of potential coalitions partners (as well as opponents) are difficult to assess. Actors in such subsystems are known to rely on prior knowledge and deep core beliefs for forming coalitions. With developing coalition structures, actors are further known to rely on the devil shift as a cue to structure the actor constellations and coalitions around them. In this paper, we trace the development of actor constellations and coalitions in two nascent subsystems, that is, AMR policy in Germany and Switzerland. We re-construct actor coalitions based on actors’ shared problem perceptions, actors’ shared blame attribution to others, actors’ shared solution propositions, and actors’ shared responsibility attributions to others. We expect coalitions to start forming based on the first, and continue forming around the four dimensions over time, in the order we discuss. Methodologically, we rely on texts from public media (two newspapers per country / subsystem) that we coded and transform into networks of actors’ similar profiles with respect to problems, blame, solutions, and responsibility. We thus analyse four network layers over four periods, in two subsystems that differ on the institutional setup but share the substantive issue of AMR.

This study examines how the European Union's messages have been disseminated in the United Kingdom over five years using a network-based approach to analyse their representation in three selected newspapers (The Times, The Guardian and Metro). By integrating text mining, natural language processing, and social network analysis, the structure, diffusion, and reception of EU-related discourse in British media were investigated.

It is argued that this representation played a substantial role in fostering anti-EU sentiment, ultimately leading to Brexit. Amid challenges in EU communication and anti-EU feelings on the rise, alongside notable divisions within the bloc, there is an urgent call to gain a more thorough understanding of these developments. This study addresses this urgency by examining the dynamics between EU communication and the perspectives presented in the media.

While existing research has extensively explored the portrayal of the EU in the press and media discourse (Coleman et al., 2022; Krzyżanowski, 2019; Maccaferri, 2019; Simpson & Startin, 2022; Walter, 2019; Zappettini & Krzyżanowski, 2019), very little attention has been paid to the relationship with the EU’s own communication efforts. Previous studies have explored aspects such as framing, discourse, patterns and external drivers of media coverage (Brosius et al., 2019; Gattermann, 2013; Schuck et al., 2014; Vliegenthart et al., 2008), as well as looked into messages sent by the European Commission (Rauh, 2022). While insightful, existing research has predominantly focused on media-centric analyses, overlooking the influence of the EU's communication strategies on media portrayal and vice versa. This study explores this interplay, particularly in the pre- and post-Brexit landscape, to uncover the dynamics of this interrelationship.

Drawing upon previous agenda-setting research and employing a network theory approach, this study examines communication dynamics during the Brexit negotiations. It identifies key actors and recurring narratives that have shaped public perceptions of the EU, while also tracing how messages spread across various news sources. Additionally, the research explores the role of intermedia agenda-setting in shaping media coverage. Through network visualisation and centrality measures, the study reveals clusters within the networks, finds co-occurring themes and ideological alignments, and the impact of the EU’s communication on media framing. The investigation is grounded in the examination of the EU's communication and publications in the British media, spanning the period pre- and post-Brexit, up to the Windsor Framework agreement (2018 – 2023).

The findings contribute to a deeper understanding of how institutional messaging operates within national media ecosystems, shedding light on the mechanisms of information diffusion and public discourse formation, as well as organisations’ agenda-building powers. This study also advances methodological discussions on the integration of network theory and text analysis, demonstrating their potential for examining media influence and information flows. Finally, it uncovers the complex network of political communication dynamics during a period of significant geopolitical change.

In the aftermath of violent intergroup conflicts, this article examines the role of interpersonal discussion networks in shaping intergroup contact and collective action. Taking a cue on discussion networks for political action, the paper underscores the significance of localized, interpersonal communication channels in providing new insights into the conflict-related beliefs of both minority and majority groups.

These networks are thought to foster social embeddedness by influencing individuals' worldviews and sense of agency, while facilitating ongoing social interactions that serve both as conduits and filters for information. Through the exchange of ideas and information, discussion networks create opportunities for action, emphasizing the well-documented impact of political discussions on mobilization.

Using a unique dataset on interpersonal communication (N= 1500) in Sri Lanka (2017–2020), we first map these discussion networks and describe their characteristics—specifically, whom individuals talk to and the issues they discuss. Second, we analyze the features of discussion networks within minority and majority groups to reveal their correlation with beliefs and forms of action. Our preliminary findings suggest that network size negatively influences the attitudinal openness of minority groups, while a growing subset of discussion partners focused on the war increases support for compensatory rights and preferential policies. Ultimately, the study of these networks contributes to building resilience against ethnic opposition in post-conflict environments.

Despite the increasing scholarly attention to relational dynamics concerning the coordination of transboundary crisis responses, we still have too little evidence concerning the evolution of the structures managing these incidents. To address this literature gap, this study examines the humanitarian responses to two emergencies caused by two of the deadliest disasters affecting the Southern Hemisphere in recorded history, namely Cyclones Idai and Kenneth in 2019 and Cyclone Freddy in 2023. More specifically, it compares the policy networks involved in the reactions to these episodes in Mozambique. The analysis will specifically focus on how the EU Member States and supranational organizations coordinated themselves and with other actors during these episodes. While longitudinal crisis network studies are rare due to their demanding data collection requirements, they allow for linking structural changes over time to different policy outcomes. To do so, this paper relies on a combination of Temporal Exponential Random Graph Models (TERGMs) and semi-structured interviews. Data was extracted from a survey distributed among individuals managing these episodes. This study has relevant implications for practitioners and theorists alike, since it provides a greater understanding of the extent to which transboundary crisis network managers replicate successful patterns and apply lessons learned from previous incidents.

This study investigates the causal impact of holding a seat in a representative political body on individuals' social capital, utilizing the unique case of the Chilean Constitutional Convention (2021–2022). Unlike previous processes, this convention adopted an innovative electoral design that lowered entry barriers for independent candidates, many of whom came from social movements and civil society organizations. This institutional shift created a distinctive political setting to analyze how transitioning from activism to institutional politics affects delegates' social networks and political capital.

Using a two-mode bipartite dataset that tracks affiliations with up to six social organizations and political parties before and after the convention, the study evaluates changes in delegates' embeddedness, cohesion, and brokerage. The analysis employs a difference-in-differences (DiD) design, comparing delegates who secured a seat (treatment group) with candidates who narrowly missed election (control group).

The study hypothesizes that institutional participation increases embeddedness and cohesion for all delegates but reduces brokerage for independents due to the loss of their original bridging roles. Conversely, party-affiliated delegates are expected to experience increased brokerage capacity, leveraging their pre-existing partisan networks. Findings from this research contribute to the literature on political socialization, social capital, and network dynamics in emerging institutional contexts.

This manuscript explores resilience in the policy framework designed to govern native forest ecosystems in Argentina, with particular attention to the case of Salta province. Resilience, in this context, refers to the capacity of a policy system to adapt and achieve effective implementation. This would be evidenced in the enforcement of the legal protection of native forest ecosystems. Our analysis encompasses a wide range of legislation including: The latest Forest Law, Law 26160 on Indigenous Land, policies on family farming, and Argentina's international commitments regarding environmental protection and the rights of Indigenous peoples. The case of Salta is particularly significant due to several factors: In this province, there were very high levels of deforestation observed prior to the enactment of the Forest Law, and several efforts to circumvent restrictions, in coordinated actions between the local government and stakeholders interested in achieving the expansion of productive areas. One of the strategies was recategorization, although it was not the only one. They also took advantage of the laxity of controls and the low imposition of penalties, factors which, in combination, would make it possible to circumvent legal restrictions. The contested issue revolves around the definition of the territorial ordinance of native forest (OTBN by its spanish acronym) map. We examine in depth how, in addition to various attempts to expand the area of land that can be deforested (such as through reclassification), there is evidence of systematic responses to counter these efforts, including the intervention of National Forests office (Bosques de Nación), tribunal rulings, and the imposition of fines to penalize violators. We also analysed the debate around the trajectory of redifinition of OTBN along this decade, which has currently a new version looking at major local and national Argentinean newspapers. This paper highlights the role of "oscillating exceptional agents", a diverse group of actors who have contributed to protecting contested areas of forest preservation and economic development (yellow zones), areas most threatened by deforestation and inhabited by Indigenous and criollos family farmers communities. By a sequential mixed methods research design, combining quantitative and qualitative methods including policy network analysis and content analysis of actors' discourse, we explore the complex system of coalitions between family farmers and indigenous organizations, environmental state and non-state agencies, and their collaboration with federal authorities. This paper highlights the role of "oscillating exceptional agents" a diverse group of actors who have contributed to protecting contested areas of forest preservation and economic development (yellow zones), areas most threatened by deforestation and inhabited by Indigenous communities. By a sequential mixed methods research design, combining quantitative and qualitative methods including policy network analysis and content analysis of actors' discourse, we explore the complex system of coalitions between family farmers and indigenous organizations, environmental state and non-state agencies, and their collaboration with federal authorities.

Our findings indicate that besides external pressures such as change in the political administration and the pandemic, the existing coalition were able to be resilient in governing of native forest ecosystem in the province of Salta.

Recent research on political communication highlights how digital platforms facilitate policy discourse, yet much of this work focuses on ideological polarization rather than specific issues. This study builds on social network analysis (SNA) research by examining whether Twitter interactions among U.S. politicians provide information about cluster actors who coalesce around shared policy interests (i.e., policy constellations). While prior studies have investigated partisanship and influencer dynamics, less is known about how politicians form networks around specific issues, particularly in drug policy. Using a dataset of several hundred American politicians and over 7,000 cannabis-related tweets, I construct a two-mode network based on retweets, hashtag co-occurrences, and direct mentions ("@") to analyze patterns of interaction and discourse. The first mode comprises ties between politicians and organizations, while the second mode ties politicians to policies and political issues. Preliminary findings suggest that a small subset of politicians disproportionately drives the conversation, acting as key brokers who amplify and shape policy discussions. At the same time, distinct clusters emerge, with politicians and organizations aligning around specific policy stances, regulatory frameworks, and advocacy efforts. This study extends existing work on digital political networks by offering an analytic model to uncover policy constellations through communication ties. These findings contribute to research on political communication, digital advocacy, and the role of social media in shaping contemporary policy debates.

This paper examines the changing structure of elite conflict in Weimar Germany by analyzing interactions among political parties in the German Reichstag between 1920 and 1932. We introduce a new database derived from digitized parliamentary proceedings, capturing all speeches, interjections, and reactions to interjections recorded by parliamentary stenographers. Each interaction is assigned a politeness score based on the type of interaction (e.g., applause, agreement, laughter, shouting) and modifying descriptors (e.g., “tumultuous” or “lively”). Using these scores, we first employ network analysis to trace changes in the structure of deference. We then construct a party-party structural equivalence matrix based on shared interaction patterns and arrange parties in a two-dimensional space using multidimensional scaling. Our findings reveal that the resulting party structure closely aligns with one derived from roll-call vote similarity, whereas speech content alone fails to reproduce this pattern. By examining relationships formed through repeated interactions on the floor, this study provides novel insights into elite competition and polarization in the Weimar Republic, contributing to broader debates on institutional instability in democratic systems under stress.

Understanding political polarization from a social network perspective is critical for diagnosing how policy preferences diffuse, cluster, and shape opinion dynamics. We present a novel method, Neural Network Nominate (NNN), which extends bipartite network analysis to measure ideological positions from user–proposal ties. Building on Thurstone’s paired comparisons and the NOMINATE framework, NNN embeds both individuals and policy proposals in a low-dimensional latent space, learned by a neural network classifier that predicts the outcome of each user’s preference ties. We examine large-scale datasets from three contexts—Chile, France, and Brazil—collecting over five million user–proposal choices. The resulting bipartite networks are then mapped to ideological spaces where users cluster by political leaning, and proposals align with the left–right dimension.

Our approach consistently attains strong predictive accuracy, including 68–70% for pairwise choice and up to 91% for self-reported left–right classification. We highlight how these embeddings illuminate generational differences in polarization: for instance, younger cohorts in France and Brazil form tighter ideological clusters, whereas young right-wing Chileans align with left-leaning policies. By focusing on revealed preferences, NNN mitigates the social desirability biases often found in surveys and the echo-chamber distortions in social media data. The method offers a scalable, transparent means to identify bridging proposals that might reduce ideological divides. This contribution underscores the value of a network-based approach for analyzing mass political ideology, fostering new directions in understanding—and potentially intervening in—polarized political landscapes. Our findings reveal critical insights, opening pathways for digital democracy interventions.

The undertaking of several studies of political phenomena in social media mandates the operationalization of the notion of political stance of users and contents involved. Relevant examples include the study of segregation and polarization online, or the study of political diversity in content diets in social media. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general design, in which users and content might take stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a dataset of X/Twitter population of users in the French political Twittersphere and web domains embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment. Positions are derived using a method for ideal point estimation on a bipartite network between members of parliament and their followers. We provide several benchmarks validating the positions of these entities (based on both, LLM and human annotations), as well as a discussion of the case studies in which they can be used, including, e.g., AI explainability, political polarization and segregation, and media diets.

The study of social-ecological systems and social-ecological networks allows for the joint analysis of both social and ecological systems. Managing and governing – and even more so achieving social-ecologial fit in – social-ecological systems has been claimed to be complex, because of actors’ lack of knowledge about the complex ecological interdependencies, but also because of the dynamic nature of ecological interdependencies. This paper addresses the question of how actors in a social-ecological system address potentially changing ecological systems and related interdependencies. More specifically, we study a river basin in Switzerland and rely on survey data on actors involved in managing the river basin. We further rely on ecological data about connectedness of river patches, and how this connectedness might change depending on a) climate-change induced future scenarios, and b) the addition or removal of man-made barriers and the improvement or deterioration of water quality. This allows us to work with different scenarios of ecological interdependencies, and assess to what degree actors are able to consider these scenario-based interdependencies into their network structure, and where there might be gaps in the collaborative actor network to address future scenarios. We rely on ERGM analyses of the social-ecological network.

States use sanctions as a means to penalize or influence behavior in their counterparts. Sanctioned states may respond to these material penalties in different ways. On one hand, they might signal conciliatory attitudes to reduce the impact of sanctions. On the other hand, they might demonstrate resolve and willingness to absorb costs to resist external pressure. This paper uses relational event models and language measures to analyze how sanctioned states communicate their responses after being subjected to sanctions, and the network dynamics that govern sanction allocation and response.

This paper introduces a novel field for applying network analysis to political phenomena by conceptualizing transitions in discrete political state spaces as transition networks. By examining transitions in key areas—political regimes, institutional governance structures, and policy regimes—this approach aims to categorize transition paths and identify structural similarities, thereby enhancing our understanding of political development patterns. Larger datasets allow for analysis using relational event models.

First, the paper explores regime transitions in ancient Greece, tracing shifts between democracies, autocracies, and hybrid regimes. By constructing a network of regime types and their transitions, it identifies patterns of political change, including factors that facilitate or hinder transitions. This framework provides insights into regime stability and temporal dynamics of regime evolution, with implications for contemporary democratization and autocratization processes.

Second, the paper examines transitions in institutional governance structures, focusing on infrastructure systems. It analyzes shifts between private markets, state hierarchies, and hybrid systems, highlighting mechanisms driving governance adaptation, convergence, and persistence. Institutional path dependencies and external shocks play a key role in shaping these transformations.

Third, policy regime transitions are analyzed through climate policy, tracing changes in policy instrument configurations over time. By mapping policy trajectories, this approach identifies critical junctures and dominant pathways, offering insights into how policy repertoires adapt to political and economic shifts.

By applying network analysis to political state spaces, this paper advances a relational perspective on political change and transformation, contributing to a deeper understanding of political development dynamics.

This paper advocates thinking of political capital within a social network ecological framework and suggests that although network position forms the structural basis for individual levels of political capital, the political environment within a given political ecosystem determines how and when social ties are put into action. The political network changes little in the short run, but the costs and benefits of political action will influence perceived tie strength and political obligation depending on the political environment (e.g., the political environment created by 9/11). In viewing political capital in such a manner, we engage in an empirical examination of these ideas through the study of a policy network in North Carolina over a 6-year period, examining individual variation in reputed political influence as it relates to various kinds of network centrality, structural holes, cognitive knowledge of the network, and political outcomes at three points in time. At a macro level we examine individual variation in reputed political influence as it relates to various kinds of network centrality, structural holes, cognitive knowledge of the network, and political status at three points in time. Included are legislators, lobbyists, state government officials, and activists involved in passing a piece of environmental legislation. At the micro level, we examine the political capital over time for two political antagonists (lobbyists representing opposing industries), examining how the development of and change in such capital is related to political outcomes.