From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing
Year: 2021
Type: dissertation
Abstract: Recent improvements in information and communication technologies have contributed to an increasingly globalized and connected world.The digital data that are created as the result of people's online activities and interactions consist of different types of personal and social information that can be used to extract and understand people's implicit or explicit beliefs, ideas, and biases.This thesis leverages methods and theories from natural language processing and social sciences to study and analyze the manifestations of various attributes and signals, namely social impacts, personal values, and moral traits, in user-generated texts.This work provides a comprehensive understanding of people's viewpoints, social values, and interactions and makes the following contributions.This Ph.D. was a life-changing experience that has been nothing short of amazing.Throughout this journey, I have received great support and guidance from many remarkable individuals whom I wish to acknowledge.First and foremost, I would like to express my sincere gratitude to my advisor and committee chair, Dr. Jana Diesner, for her continuous support throughout my Ph.D. study.During my tenure, she contributed to a rewarding graduate school experience by motivating me to aim for high-quality research, giving me intellectual freedom in my work to find my own path, and allowing me to grow as a researcher.I am indebted to her for her constant patience, assistance
Author: Rezapour, Rezvaneh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1198325749, G5259331294
OpenAlex ID: W6987961065