Exploring how natural language reflects individual and group social dynamics

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2021-12-02

Authors

Seraj, Sarah

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Abstract

Language can be a window into people’s thoughts, feelings, and life experiences. With the increasing use of online communication platforms, researchers now have more avenues to study people’s life events using real-time and real-world data. My dissertation attempts to identify the most important language markers for understanding people’s cognitive, social, and emotional lives. What are signs in language that predict a distressing life event and how do people cope with it in the months afterward? After identifying a large group of users on Reddit (N = 6,813) who had gone through emotional upheavals such as breakups, divorce, or other distressing life events, we tracked their language in the months before, during and after their upheaval (Chapter 2). In 2020, the world faced a global crisis: the COVID-19 pandemic. In the US, the pandemic was followed by the killing of George Floyd at the hands of police and the Black Lives Matter (BLM) protests of summer 2020, a time of national reckoning on police brutality. These two events naturally led to the question of how people dealt with collective upheavals compared to a personal crisis like a breakup and how the context of the pandemic (social isolation, people living in lockdowns) affected people’s response to the BLM movement. Would the two upheavals interact with each other in any way? A large-scale Reddit dataset (33.7 million posts, 1.37 million users) was used to study the two upheavals (Chapter 3). After identifying important language markers that help us understand people’s psychological state during personal and collective upheavals, we wanted to see if the same markers were important for understanding social dynamics outside of the context of upheavals. A group of individuals who were all part of the same work team were recruited to hold a series of one-on-one chats with everyone else on the team (N = 27; 198 conversations). The language markers that predict successful conversations were identified from the study (Chapter 4). The final chapter puts together the insights from the three different studies and highlights the contribution of each of them.

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