PA · 2026
When AI models look at a political campaign photo, do they make assumptions about the candidate's ideology based on race or gender — even when the ad itself is completely neutral? …
JEPS · 2026
Examines what features of online ads lead people to perceive them as political. Through experiments varying source, content, and political orientation, the study maps the factors …
AJPS · 2026
In linguistically diverse democracies, do political parties actually bother to speak minority languages — or do they just stick to the dominant tongue? This paper analyzes Facebook …
PSRM · 2026
Examines how political polarization and populism shape the emotional responses of citizens to election outcomes. The study finds that while polarization amplifies emotional …
PSRM · 2025
Proposes a framework for evaluating the validity and trustworthiness of measures created using supervised machine learning. The paper argues that standard validation practices from …
POQ · 2024
Investigates why older Americans disproportionately engage with dubious political news online. The study finds that partisan motivation plays a central role: older partisans are …
PNAS Nexus · 2024
Uses computational analysis of large language corpora to uncover racial hierarchies embedded in American English. The study reveals systematic associations between race, perceived …
JEPS · 2023
Experimentally tests how exposure to unsubstantiated voter fraud claims affects public confidence in elections. The study finds that such claims meaningfully reduce trust in …
PA · 2023
Standard political measurement tools assume that people who give the same answer do so for the same reason — but what if a liberal and a conservative both vote "no" for completely …
PA · 2022
Topic models have become one of the most popular tools for analyzing political text at scale — but are researchers actually checking whether the topics they find measure what they …