AI 中文总结
探讨将大语言模型整合进定性和混合方法社交网络分析领域，强调增强定性分析深度与严谨性，介绍其可辅助数据收集等工作，指出使用局限与伦理挑战，并为相关研究者提供研究设计与实用建议。
AI 中文摘要
本文探讨了将大语言模型（LLMs）整合到定性和混合方法社交网络分析（SNA）领域。我们认为这种整合应主要增强定性SNA的深度和严谨性，而非用自动化系统取代人类研究者。首先概述了定性和混合方法SNA的核心原则，强调理解关系意义、叙事作用和关系身份的重要性。接着讨论LLMs如何作为强大工具辅助此项工作，包括数据收集、编码、理论构建和溯因推理等。还提及使用LLMs的局限和伦理挑战，如偏差、幻觉等问题。最后为想以深思熟虑且负责任方式将LLMs整合到工作中的研究者提供了一系列研究设计和实用建议。
英文摘要
This manuscript explores the integration of Large Language Models (LLMs) into the field of qualitative and mixed-methods social network analysis (SNA). We argue that the primary focus of this integration should be on enhancing the depth and rigor of qualitative SNA, rather than on replacing human researchers with automated systems. We begin by outlining the core principles of qualitative and mixed-methods SNA, emphasizing the importance of understanding the meaning of ties, the role of narratives, and the significance of relational identities. We then discuss how LLMs can be used as powerful tools to augment this work, from assisting with data collection and coding to supporting theory-building and abductive reasoning. We also address the limitations and ethical challenges of using LLMs in this context, including issues of bias, hallucination, and the need for reflexivity. We conclude with a series of research designs and practical recommendations for researchers who want to integrate LLMs into their work in a thoughtful and responsible way.
