Tracking group identity through natural language within groups

Abstract

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.

Locally Ensconced and Globally Integrated: How Positions in Network Structure Relate to a Language-Based Model of Organizational Identification

“…As shown in Appendix Tables A-1, A-2, A-3, and A-4, our main findings hold when controlling for these variables. Taken together, these analyses reinforce the notion that our measure tracks the semantic meanings people assign to "we" and "I" instead of simply measuring pronoun frequencies, as recent work focusing on natural language and group identification has sought to do (Ashokkumar and Pennebaker 2022).…”

The Influence of Cognitive and Emotional Factors on Social Media Users’ Information-Sharing Behaviours during Crises: The Moderating Role of the Construal Level and the Mediating Role of the Emotional Response

“…This study analysed the text content generated by social media from the perspective of psychological language, which can better reveal the intrinsic nature of people’s information-sharing behaviours. LIWC (Linguistic Inquiry and Word Count) [ 63 ] is an effective tool for textual psychoanalysis through word metrics, using pre-validated dictionaries to capture themes and psychological states from texts. In the network context, LIWC has also been effectively verified.…”

Three families of automated text analysis

“…Researchers have also thought of creative ways to use LIWC to measure more nuanced theoretical constructs. Ashokkumar and Pennebaker (2022), for instance, validate a measure of group identity strength that is equal to the ratio of the frequencies of "we" words to "cognition" words occurring in text. Brady et al (2017) use the intersection of the moral foundations dictionary and "emotion" words from LIWC to measure the moral-emotional strength of social media posts, showing that this is correlated with how many times they are shared (but see Burton et al 2021).…”