Buchumschlag

Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Cen, Sarah H., Ilyas, Andrew, Driss, Hedi, Park, Charlotte, Hopkins, Aspen, Podimata, Chara, Mądry, Aleksander
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computers and Society Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Do LLMs Track Public Opinion? A Multi-Model Study of Favorability Predictions in the 2024 U.S. Presidential Election
von: Parikh, Riya, et al.
Veröffentlicht: (2026)

AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services
von: Hopkins, Aspen, et al.
Veröffentlicht: (2025)

User Strategization and Trustworthy Algorithms
von: Cen, Sarah H., et al.
Veröffentlicht: (2023)

Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content
von: Cen, Sarah H., et al.
Veröffentlicht: (2024)

Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
von: Efthymiou, Valia, et al.
Veröffentlicht: (2025)

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
von: Jain, Saachi, et al.
Veröffentlicht: (2024)

Can Users Fix Algorithms? A Game-Theoretic Analysis of Collective Content Amplification in Recommender Systems
von: Fedorova, Ekaterina, et al.
Veröffentlicht: (2025)

Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality
von: Podimata, Chara
Veröffentlicht: (2025)

Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty
von: Efthymiou, Valia, et al.
Veröffentlicht: (2025)

Can Probabilistic Feedback Drive User Impacts in Online Platforms?
von: Dai, Jessica, et al.
Veröffentlicht: (2024)

Decomposing and Editing Predictions by Modeling Model Computation
von: Shah, Harshay, et al.
Veröffentlicht: (2024)

Exploring Political Ads on News and Media Websites During the 2024 U.S. Elections
von: Yoshikawa, Emi, et al.
Veröffentlicht: (2025)

Recourse, Repair, Reparation, & Prevention: A Stakeholder Analysis of AI Supply Chains
von: Hopkins, Aspen K., et al.
Veröffentlicht: (2025)

Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning
von: Podimata, Chara, et al.
Veröffentlicht: (2020)

Small-to-Large Generalization: Data Influences Models Consistently Across Scale
von: Khaddaj, Alaa, et al.
Veröffentlicht: (2025)

ElectionRumors2022: A Dataset of Election Rumors on Twitter During the 2022 US Midterms
von: Schafer, Joseph S, et al.
Veröffentlicht: (2024)

In Times of Crisis: An Exploratory Study of Media and Political Discourse on YouTube During the 2024 French Elections
von: Sosnovik, Vera, et al.
Veröffentlicht: (2025)

Wisdom of the LLM Crowd: A Large Scale Benchmark of Multi-Label U.S. Election-Related Harmful Social Media Content
von: Wang, Qile, et al.
Veröffentlicht: (2026)

ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents
von: Zhang, Xinnong, et al.
Veröffentlicht: (2024)

Do Large Language Model Benchmarks Test Reliability?
von: Vendrow, Joshua, et al.
Veröffentlicht: (2025)

Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
von: Jaillet, Patrick, et al.
Veröffentlicht: (2024)

Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research
von: Emmenegger, Nicolas, et al.
Veröffentlicht: (2026)

When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation
von: Jaillet, Patrick, et al.
Veröffentlicht: (2024)

Candidates for the Fall 2024 ESA Election
Veröffentlicht: (2024)

Mapping Election Polarization and Competitiveness using Election Results
von: Navarrete, Carlos, et al.
Veröffentlicht: (2023)

Uncovering Coordinated Cross-Platform Information Operations Threatening the Integrity of the 2024 U.S. Presidential Election Online Discussion
von: Minici, Marco, et al.
Veröffentlicht: (2024)

National Treasure: The Call for e-Democracy and US Election Security
von: Wong, Adam Dorian
Veröffentlicht: (2024)

Defending Against Intelligent Attackers at Large Scales
von: Lohn, Andrew J.
Veröffentlicht: (2025)

Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization
von: Leme, Renato Paes, et al.
Veröffentlicht: (2022)

Nuclear Explosions for Large Scale Carbon Sequestration
von: Haverly, Andrew
Veröffentlicht: (2025)

Statistical and Mathematical Evidence of Rigged Parliamentary Elections in Georgia, 2024
von: Osmanov, Lazare, et al.
Veröffentlicht: (2024)

Structure and Context of Retweet Coordination in the 2022 U.S. Midterm Elections
von: Axelrod, David, et al.
Veröffentlicht: (2025)

Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study
von: Isley, Calvin, et al.
Veröffentlicht: (2025)

Analyzing Misinformation Claims During the 2022 Brazilian General Election on WhatsApp, Twitter, and Kwai
von: Hale, Scott A., et al.
Veröffentlicht: (2024)

Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election Study
von: Holtdirk, Tobias, et al.
Veröffentlicht: (2025)

The Sensitivity of the U.S. Presidential Election to Coordinated Voter Relocation
von: Cardonha, Carlos, et al.
Veröffentlicht: (2024)

Cast vote records: A database of ballots from the 2020 U.S. Election
von: Kuriwaki, Shiro, et al.
Veröffentlicht: (2024)

U.S. Election Hardens Hate Universe
von: Verma, Akshay, et al.
Veröffentlicht: (2024)

Dumbing Down or Reaching Out? Norwegian News Coverage for Children During Election Periods
von: Linn Cathrin Lorgen, et al.
Veröffentlicht: (2025)

Sociodemographic Biases in Educational Counselling by Large Language Models
von: Adamczyk, Tomasz, et al.
Veröffentlicht: (2026)