Date of Award
2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Complex Systems and Data Science
First Advisor
Peter S. Dodds
Second Advisor
Christopher M. Danforth
Abstract
Identifying temporal linguistic patterns and tracing social amplification across communities has always been vital to understanding modern sociotechnical systems. Now, well into the age of information technology, the growing digitization of text archives powered by machine learning systems has enabled an enormous number of interdisciplinary studies to examine the coevolution of language and culture. However, most research in that domain investigates formal textual records, such as books and newspapers. In this work, I argue that the study of conversational text derived from social media is just as important. I present four case studies to identify and investigate societal developments in longitudinal social media streams with high temporal resolution spanning over 100 languages. These case studies show how everyday conversations on social media encode a unique perspective that is often complementary to observations derived from more formal texts. This unique perspective improves our understanding of modern sociotechnical systems and enables future research in computational linguistics, social science, and behavioral science.
Language
en
Number of Pages
242 p.
Recommended Citation
Alshaabi, Thayer, "Developing natural language processing instruments to study sociotechnical systems" (2021). Graduate College Dissertations and Theses. 1464.
https://scholarworks.uvm.edu/graddis/1464