How we do things with words: Analyzing text as social and cultural data
Alan Turing Institute（艾伦·图灵研究所）；Utrecht University（乌得勒支大学）；University of Warwick（华威大学）；Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）；Cornell University（康奈尔大学）；Leiden University（莱顿大学）；University of London（伦敦大学）
Dong Nguyen, Maria Liakata, Simon DeDeo, Jacob Eisenstein, David Mimno, Rebekah Tromble, Jane Winters
In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thick social and cultural concepts. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many. And this leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be.
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
Wangxuan Institute of Computer Technology, Peking University（北京大学计算机技术研究院）；Information Management Department, Peking University（北京大学信息管理系）；Renmin University of China（中国人民大学）
