Tracking group identity through natural language within groups.
Tracking group identity through natural language within groups.
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DOI：
10.1093/pnasnexus/pgac022
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发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Pennebaker, James W.
中科院分区：
文献类型：
--
作者：
Ashokkumar, Ashwini;Pennebaker, James W.
我们在多大程度上可以通过人们使用的语言来确定他们与群体的联系？近年来，来自社交媒体社区的大量行为数据档案已被社会科学家所利用，这为跟踪自然发生的群体身份过程提供了可能性。大多数数字群体的一个特点是，他们完全依赖书面文字。在3项研究中，我们开发并验证了一种基于语言的群体身份强度指标，并证明了其在跟踪在线社区身份过程中的潜力。在研究1a-1c中，873人写下了他们与不同群体（国家、大学或宗教）的联系。研究发现，群体认同强度的语言标记共有2个：高隶属度（更多的词，如我们，在一起）和低认知处理或质疑（更少的词，如认为，不确定）。使用这些标记，一个基于语言的无疑问的联系指数被开发出来，并应用于2,161名大学生的课堂意识流论文（研究2）。用语言表达的更高水平的无条件归属感不仅预测了自我报告的大学身份，而且预测了学生一年后继续留在大学的可能性。在研究3中，该指数被应用于2016年总统候选人希拉里·克林顿和唐纳德·特朗普的支持者的两个在线社区中的270，784人的自然主义Reddit对话。该指数预测了人们会在群体中呆多久（3a），并揭示了反映成员加入和离开群体的时间变化（3b）。总之，这些研究突出了一个基于语言的方法来跟踪和研究在线群体的群体身份过程的承诺。
To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a–1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students’ likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates—Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members’ joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.
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影响因子：
6.2
作者：
Gonzales, Amy L.;Hancock, Jeffrey T.;Pennebaker, James W.
通讯作者：
Pennebaker, James W.
DOI：
10.1073/pnas.2026045118
发表时间：
2021-05-04
影响因子：
11.1
作者：
Markowitz, David M.;Shulman, Hillary C.
通讯作者：
Shulman, Hillary C.
影响因子：
7.6
作者：
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通讯作者：
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影响因子：
13.6
作者：
Boyd, Ryan L.;Blackburn, Kate G.;Pennebaker, James W.
通讯作者：
Pennebaker, James W.
影响因子：
2.1
作者：
Boyd RL;Schwartz HA
通讯作者：
Schwartz HA