Investigating information framing with natural language processing
- Words can influence both who and what we believe. Consider these headlines, which report on the same vaccine: "Moderna Vaccine Proves 96\% Effective in Teens," "Moderna Vaccine Highly Effective in Adolescents, Company Says." The first presents the efficacy as an established fact, while the second presents the efficacy as an assertion made by its manufacturer. Word choice regarding how assertions are made also matters: a company's "findings" (with semi-factive "find") strengthens the validity of its assertions, while a politician's "admission" carries negative connotations about their trustworthiness. Prior work studying information framing, or discourse acts in which a writer or speaker signals their stance with respect to a proposition from some source entity, has been restricted to a relatively narrow set of framing strategies over small-scale datasets. As a result, we know little about systematic patterns in information framing within real world contexts, and even less about its downstream effects on people's beliefs. In my dissertation I combine methods from linguistics, social psychology, and natural language processing (NLP) to study information framing in a data-driven way. In my first series of studies, I use my tools to discover linguistic strategies for conveying trust and doubt and I expose partisan patterns in how journalists exploit information framing to cover scientific issues. Next, I quantitatively measure the persuasiveness of framing strategies in social media arguments and reveal an interaction between epistemic commitment and politeness: high commitment is viewed as face-threatening and less persuasive when framing personal opinions, but has no effect on persuasion when framing scientific evidence. Finally, I conduct behavioral experiments to study the effect of information framing on attitude formation. I show that presenting information as opinions using non-factive verbs (e.g., "Scientists believe climate change is a serious concern") can affect what readers think is true, and to a greater extent, whether they think a ground truth exists in the first place. Together, this work contributes to our understanding of the role of language in how people form, update, and spread their beliefs, and contributes tools and datasets that may be applied to public interest messaging, conflict resolution and negotiation, and bias and misinformation detection systems.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Degree committee member
|Stanford University, School of Humanities and Sciences
|Stanford University, Department of Linguistics
|Statement of responsibility
|Submitted to the Department of Linguistics.
|Thesis Ph.D. Stanford University 2023.
- © 2023 by Yiwei Luo
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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