“我”的回响：利用人口统计学增强的词嵌入追踪身份认同
The Echoes of the 'I': Tracing Identity with Demographically Enhanced Word Embeddings
AI总结：
本文提出将社会人口统计信息融入词嵌入以研究身份认同，成功复现并扩展了性别化自我看法的发现，并能分析社会群体差异，有望推动社会科学领域更快采用计算方法。
摘要：
身份认同是社会科学中最常被研究的构念之一。然而，尽管关于身份认同的理论研究十分广泛，仍需要更多的实证数据来验证和完善现有理论。本文提出了一种通过将社会人口统计信息增强到词嵌入中来研究身份认同的新方法。作为概念验证，我们证明该方法成功复现并扩展了关于性别化自我看法的既有发现。我们的方法可应用于多种场景，使研究者能够利用大量自然产生的数据，例如社交媒体帖子。与计算机科学中已引入的类似方法不同，我们的方法能够研究社会群体之间的差异。这可能对社会科学家特别有吸引力，并可能促进计算社会科学方法在该领域更快地被采用。
英文摘要：
Identity is one of the most commonly studied constructs in social science. However, despite extensive theoretical work on identity, there remains a need for additional empirical data to validate and refine existing theories. This paper introduces a novel approach to studying identity by enhancing word embeddings with socio-demographic information. As a proof of concept, we demonstrate that our approach successfully reproduces and extends established findings regarding gendered self-views. Our methodology can be applied in a wide variety of settings, allowing researchers to tap into a vast pool of naturally occurring data, such as social media posts. Unlike similar methods already introduced in computer science, our approach allows for the study of differences between social groups. This could be particularly appealing to social scientists and may encourage the faster adoption of computational methods in the field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
