Summary
A new arXiv paper (2606.27347) by Kirill Solovev and Jana Lasser, published June 27, 2026, presents a multilingual joint entity-relation extraction pipeline for mapping political-elite networks across Europe. The work addresses a central question in comparative politics: whether political elites organize into rent-seeking coalitions that capture public resources, or civic networks that sustain governance. Historically, observing these complex, informal, and adversarial ties at scale required intensive manual coding, while automated text-as-data methods were limited to simple co-occurrence analysis. The proposed pipeline leverages open-source large language models (LLMs) to automatically extract political-elite networks from multilingual news text, avoiding proprietary APIs and improving cross-lingual scalability. The approach offers computational social scientists a reproducible, transparent alternative for studying elite networks at scale.
Paper Overview
Field: NLP
Authors: Kirill Solovev, Jana Lasser
Published: 2026-06-27
arXiv:
2606.27347Abstract
Whether political elites organise into rent-seeking coalitions that capture public resources or civic networks that sustain governance is a central question in comparative politics. Yet observing these complex, informal, and adversarial ties at scale has historically required intensive manual coding, while automated text-as-data methods have largely been limited to simple co-occurrence. Recent large language model (LLM) approaches offer a path forward but often rely on proprietary APIs and lack cross-lingual robustness.
This paper proposes a multilingual joint entity-relation extraction pipeline that uses open-source LLMs to automatically construct political-elite networks from multilingual news text — without any proprietary API dependence.
*Auto-collected on 2026-06-27.*
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