This study demonstrates the use of GPT-4 and variants, advanced language models readily accessible to many social scientists, in extracting political networks from text. This approach showcases the novel integration of GPT-4’s capabilities in entity recognition, relation extraction, entity linking, and sentiment analysis into a single cohesive process. Based on a corpus of 1009 Chilean political news articles, the study validates the graph extraction method using ‘legislative agreement’, i.e., the proportion of times two politicians vote the same way. It finds that sentiments identified by GPT-4 align with how frequently parliamentarians vote together in roll calls. Comprising two parts, the first involves a linear regression analysis indicating that negative relationships predicted by GPT-4 correspond with reduced legislative agreement between two parliamentarians. The second part employs node embeddings to analyze the impact of network distance, considering both with and without sentiment, on legislative agreements. This analysis reveals a notably stronger predictive power when sentiments are included. The findings underscore GPT-4’s versatility in political network analysis.

This study examines the relationship between ethnic endogamy and socioeconomic status (SES) within the socioeconomically divergent Jewish and Native-Chilean Mapuche communities of Santiago, Chile. By leveraging the Hispanic naming convention to analyze dual ethnic surnames, we trace endogamy patterns across comprehensive datasets that go back to 1884 up to the present. Our quantile regression analysis reveals that individuals from the lower SES brackets of the Jewish community and the higher brackets of the Mapuche community are more likely to have mixed ethnic backgrounds. This finding shows a nuanced interplay between socioeconomic standing and marital choices, suggesting that these factors significantly influence the persistence and transformation of SES within minority groups. The study introduces the Ecological Model of Ethnic Disaffiliation, providing a theoretical framework that explains how socioeconomic outliers within ethnic groups could lead to a narrowing of their socioeconomic range over generations.

This paper introduces SpatialCluster, a Python library developed for clustering urban areas using geolocated data. The library integrates a range of methods for urban clustering, including Deep Modularity Networks, Gaussian Mixtures, K-Nearest Neighbours, Self Organized Maps, and Information-Theoretic Clustering, providing a comprehensive framework. These methods are evaluated using indices such as the Adjusted Rand Index and Adjusted Mutual Information, and the library includes features for detailed map visualization. SpatialCluster’s online documentation offers examples, making the library accessible to researchers and urban planners. The library aims to facilitate urban data analysis and contribute to the field of urban studies.

Urban clustering detects geographical units that are internally homogeneous and distinct from their surroundings. It has applications in urban planning, but few studies compare the effectiveness of different methods. We study two techniques that represent two families of urban clustering algorithms: Gaussian Mixture Models (GMMs), which operate on spatially distributed data, and Deep Modularity Networks (DMONs), which work on attributed graphs of proximal nodes. To explore the strengths and limitations of these techniques, we studied their parametric sensitivity under different conditions, considering the spatial resolution, granularity of representation, and the number of descriptive attributes, among other relevant factors. To validate the methods, we asked residents of Santiago, Chile, to respond to a survey comparing city clustering solutions produced using the different methods. Our study shows that DMON is slightly preferred over GMM and that social features seem to be the most important ones to cluster urban areas.

This article develops a model to explain the incorporation of new groups into the political elites in oligarchic societies. In this model, factions within the traditional power-holding group compete, and as their conflict escalates, they recruit the support of groups traditionally excluded from politics. This mechanism changes the ruling class’s social composition without the need for a substantial push from lower-status groups. I apply this model to analyze sequential changes in the social composition of the Chilean Congress from 1834 to 1894. To identify old versus new elites, I use an original database of kinship ties among all Chilean ministers and Congress members. By combining social network analysis and historical evidence, I show that, in times of increased intra-oligarchic conflict, groups traditionally excluded from the inner circles of power – the bourgeoisie and the bureaucrats initially – made breakthroughs in their political representation.

From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.

How do political candidates combine social media campaign tools with on-the-ground political campaigns to pursue segmented electoral strategies? We argue that online campaigns can reproduce and reinforce segmented electoral appeals. Furthermore, our study suggests that electoral segmentation remains a broader phenomenon that includes social media as but one of many instruments by which to appeal to voters. To test our argument, we analyze the case of the 2017 legislative elections in Chile. We combine an analysis of Facebook and online electoral campaign data from 80 congressional campaigns that competed in three districts with ethnographic sources (i.e., campaigns observed on the ground and in-depth interviews with candidates). The results of this novel study suggest that intensive online campaigning mirrors offline segmentation.

Based on a geocoded registry of more than four million residents of Santiago, Chile, we build two surname-based networks that reveal the city's population structure. The first network is formed from paternal and maternal surname pairs. The second network is formed from the isonymic distances between the city's neighborhoods. These networks uncover the city's main ethnic groups and their spatial distribution. We match the networks to a socioeconomic index, and find that surnames of high socioeconomic status tend to cluster, be more diverse, and occupy a well-defined quarter of the city. The results are suggestive of a high degree of urban segregation in Santiago.