Understanding and tracking societal discourse around essential governance challenges of our times is crucial. One possible heuristic is to conceptualize discourse as a network of actors and policy beliefs.

Here, we present an exemplary and widely applicable automated approach to extract discourse networks from large volumes of media data, as a bipartite graph of organizations and beliefs connected by stance edges. Our approach leverages various natural language processing techniques, alongside qualitative content analysis. We combine named entity recognition, named entity linking, supervised text classification informed by close reading, and a novel stance detection procedure based on large language models.

We demonstrate our approach in an empirical application tracing urban sustainable transport discourse networks in the Swiss urban area of Zürich over 12 years, based on more than one million paragraphs extracted from slightly less than two million newspaper articles.

We test the internal validity of our approach. Based on evaluations against manually automated data, we find support for what we call the window validity hypothesis of automated discourse network data gathering. The internal validity of automated discourse network data gathering increases if inferences are combined over sliding time windows.

Our results show that when leveraging data redundancy and stance inertia through windowed aggregation, automated methods can recover basic structure and higher-level structurally descriptive metrics of discourse networks well. Our results also demonstrate the necessity of creating high-quality test sets and close reading and that efforts invested in automation should be carefully considered.

We introduce the exponentially preferential recursive tree and study some properties related to the degree profile of nodes in the tree. The definition of the tree involves a radix $a\gt 0$. In a tree of size $n$ (nodes), the nodes are labeled with the numbers $1,2, \ldots ,n$. The node labeled $i$ attracts the future entrant $n+1$ with probability proportional to $a^i$.

We dedicate an early section for algorithms to generate and visualize the trees in different regimes. We study the asymptotic distribution of the outdegree of node $i$, as $n\to \infty$, and find three regimes according to whether $0 \lt a \lt 1$ (subcritical regime), $a=1$ (critical regime), or $a\gt 1$ (supercritical regime). Within any regime, there are also phases depending on a delicate interplay between $i$ and $n$, ramifying the asymptotic distribution within the regime into “early,” “intermediate” and “late” phases. In certain phases of certain regimes, we find asymptotic Gaussian laws. In certain phases of some other regimes, small oscillations in the asymototic laws are detected by the Poisson approximation techniques.

Inequality is a critical global issue, particularly in the United States, where economic disparities are among the most pronounced. Social justice research traditionally studies attitudes towards inequality—perceptions, beliefs, and judgments—using latent variable approaches. Recent scholarship adopts a network perspective, showing that these attitudes are interconnected within inequality belief systems. However, scholars often compare belief systems using split-sample approaches without examining how emotions, such as anger, shape these systems. Moreover, they rarely investigate Converse’s seminal idea that changes in central attitudes can lead to broader shifts in belief systems. Addressing these gaps, we applied a tripartite analytical strategy using U.S. data from the 2019 ISSP Social Inequality module. First, we used a mixed graphical model to demonstrate that inequality belief systems form cohesive small-world networks, with perception of large income inequality and belief in public redistribution as central nodes. Second, a moderated network model revealed that anger towards inequality moderates nearly one-third of network edges, consolidating the belief system by polarizing associations. Third, Ising model simulations showed that changes to central attitudes produce broader shifts across the belief system. This study advances belief system research by introducing innovative methods for comparing structures and testing dynamics of attitude change. It also contributes to social justice research by integrating emotional dynamics and highlighting anger’s role in structuring inequality belief systems.