자료유형  

 학위논문

Control Number  

0017162860

International Standard Book Number  

9798382740676

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Kazemi, Ashkan.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

General Note  

Advisor: Mihalcea, Rada;Perez-Rosas, Veronica.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2024.

Summary, Etc.  

요약As curbing the spread of online misinformation has proven to be challenging, we look to artificial intelligence (AI) and natural language technology for helping individuals and society counter and limit it. Despite current advances, state-of-the-art natural language processing (NLP) and AI still struggle to automatically identify and understand misinformation. Humans exposed to harmful content may experience lasting negative consequences in real life, and it is often difficult to change one's mind once they form wrong beliefs. Addressing these interwoven technical and social challenges requires research and understanding into the core mechanisms that drive the phenomena of misinformation. This thesis introduces human-centered NLP tasks and methods that can help prioritize human welfare in countering misinformation. We present findings on the differences in how people of different backgrounds perceive misinformation, and how misinformation unfolds in different conditions such as end-to-end encrypted social media in India. We build on this understanding to create models and datasets for identifying misinformation at scale that put humans in the decision making seat, through claim matching, matching claims with fact-check reports, and query rewriting that scale the efforts of fact-checkers. Our work highlights the global impact of misinformation, and contributes to advancing the equitability of available language technologies through models and datasets in a variety of high and low resources and languages. We also make fundamental contributions to data, algorithms, and models through: multilingual and low-resource embeddings and retrieval for better claim matching, reinforcement learning for reformulating queries for better misinformation discovery, unsupervised and graph-based focused content extraction through introducing the Biased TextRank algorithm, and explanation generation through extractive (Biased TextRank) and abstractive (GPT-2) summarization. Through this thesis, we aim to promote individual and social wellbeing by creating language technologies built on a deeper understanding of misinformation, and provide tools to help journalists as well as internet users to identify and navigate around it.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Communication.

Index Term-Uncontrolled  

Natural language processing

Index Term-Uncontrolled  

Misinformation

Index Term-Uncontrolled  

Human-centered NLP

Index Term-Uncontrolled  

Biased TextRank

Added Entry-Corporate Name  

University of Michigan Computer Science & Engineering

Host Item Entry  

Dissertations Abstracts International. 85-12A.

Electronic Location and Access  

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Control Number  

joongbu:655603