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Social Media Misinformation: Spread, Impact, and Fact-Checking With Large Language Models
Social Media Misinformation: Spread, Impact, and Fact-Checking With Large Language Models
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103615
- ISBN
- 9798286433490
- DDC
- 020
- 서명/저자
- Social Media Misinformation: Spread, Impact, and Fact-Checking With Large Language Models
- 발행사항
- [Sl] : Indiana University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 353 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Menczer, Filippo.
- 학위논문주기
- Thesis (Ph.D.)--Indiana University, 2025.
- 초록/해제
- 요약The digital age has profoundly reshaped how information is created, disseminated, and consumed, raising significant concerns about the spread and impact of misinformation. This dissertation examines misinformation from three interconnected perspectives: its dissemination, its societal consequences, and potential interventions to mitigate its harms. The first part focuses on the spread of misinformation on social media platforms, introducing metrics to identify "superspreaders" of low-credibility content and revealing gaps in platform moderation. It also introduces a novel method for inferring information diffusion cascades, enabling a reexamination of a landmark misinformation dataset and challenging prevailing assumptions about how information spreads. The second part explores the impact of misinformation, demonstrating its relationship to vaccine hesitancy during the COVID-19 pandemic and modeling the broader public health consequences of a heavily misinformed population. These studies employ a combination of large-scale correlational analyses and agent-based simulations to quantify the societal effects of misinformation. The final section explores interventions with artificial intelligence, particularly the application of large language models (LLMs) for fact-checking. A randomized controlled experiment finds that while LLM-generated fact-checking information often accurately identified false content, this information did not consistently improve users' ability to discern headline accuracy and, in some cases, even reduces discernment. Through these investigations, this dissertation contributes to the ongoing debate about the social significance of misinformation by exploring its spread, consequences, and possible solutions.
- 일반주제명
- Information science
- 일반주제명
- Web studies
- 일반주제명
- Computer science
- 키워드
- Social media
- 키워드
- Misinformation
- 키워드
- Superspreaders
- 키워드
- Fact-checking
- 기타저자
- Indiana University Informatics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798286433490
■035 ▼a(MiAaPQ)AAI32044349
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a020
■1001 ▼aDeVerna, Matthew R.▼0(orcid)0000-0003-3578-8339
■24510▼aSocial Media Misinformation: Spread, Impact, and Fact-Checking With Large Language Models
■260 ▼a[Sl]▼bIndiana University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a353 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Menczer, Filippo.
■5021 ▼aThesis (Ph.D.)--Indiana University, 2025.
■520 ▼aThe digital age has profoundly reshaped how information is created, disseminated, and consumed, raising significant concerns about the spread and impact of misinformation. This dissertation examines misinformation from three interconnected perspectives: its dissemination, its societal consequences, and potential interventions to mitigate its harms. The first part focuses on the spread of misinformation on social media platforms, introducing metrics to identify "superspreaders" of low-credibility content and revealing gaps in platform moderation. It also introduces a novel method for inferring information diffusion cascades, enabling a reexamination of a landmark misinformation dataset and challenging prevailing assumptions about how information spreads. The second part explores the impact of misinformation, demonstrating its relationship to vaccine hesitancy during the COVID-19 pandemic and modeling the broader public health consequences of a heavily misinformed population. These studies employ a combination of large-scale correlational analyses and agent-based simulations to quantify the societal effects of misinformation. The final section explores interventions with artificial intelligence, particularly the application of large language models (LLMs) for fact-checking. A randomized controlled experiment finds that while LLM-generated fact-checking information often accurately identified false content, this information did not consistently improve users' ability to discern headline accuracy and, in some cases, even reduces discernment. Through these investigations, this dissertation contributes to the ongoing debate about the social significance of misinformation by exploring its spread, consequences, and possible solutions.
■590 ▼aSchool code: 0093.
■650 4▼aInformation science
■650 4▼aWeb studies
■650 4▼aComputer science
■653 ▼aSocial media
■653 ▼aMisinformation
■653 ▼aLarge language models
■653 ▼aSuperspreaders
■653 ▼aFact-checking
■690 ▼a0723
■690 ▼a0984
■690 ▼a0800
■690 ▼a0646
■71020▼aIndiana University▼bInformatics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0093
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357900▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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