Works in
Progress

In the 2024 election, the gender gap among voters aged 18 to 29 widened dramatically, with young men breaking for Trump by more than 16 percentage points. One explanation is the campaign's embrace of alternative media, particularly podcasts, where Trump appeared frequently while avoiding traditional venues such as debates and network interviews. We investigate whether gendered media environments contributed to this shift through incidental political exposure, in which politics emerges within shows that are not explicitly political.

Using generative AI to analyze transcripts from over 30,000 episodes across the top 300 U.S. podcast channels in 2024, we systematically label ideological leaning, topical content, and gendered expression. Our findings show that even in non-political genres, masculine-leaning channels feature more political content — and more conservative political content — compared to feminine-leaning ones. We provide evidence that channels most popular with young men aged 18–24 had the highest amounts of conservative political content in the lead-up to the election compared to women in the same age group and other age-group men.

Pure independents, who represent about ten percent of Americans, are defined by the lack of partisan structure in their political attitudes, behaviors, and preferences. We demonstrate that many of these people are willing to reveal underlying partisan preferences through the partisan feeling thermometers. Accounting for these feelings reveals a clear and stable partisan structure to their attitudes. Leveraging existing cross-sectional, panel surveys, and original data, we demonstrate that: 1) Most pure independents have an affective lean. 2) Affective-leaning independents have distinctly partisan attitudes, behaviors, and preferences. 3) Affective lean is directionally stable over time. Building on these findings, we propose a new measure of party identification — Partisan Identity 9 (PID9). This measure builds on the traditional Party Identification 7 (PID7) measure by splitting the pure independent category into three categories. PID9 meaningfully improves model performance and provides powerful insights into the political attitudes, behaviors, and preferences of American independents.

Combined survey responses and social media trace data is quickly becoming the gold standard in studying the influence of social media. This process of "data donation" asks survey respondents to provide researchers with their digital footprint on relevant platforms. Errors in inference can arise if people select into the sample based on characteristics related to the concept of interest being studied.

On surveys fielded through YouGov in 2022 and 2024, we asked respondents to donate Facebook data, YouTube histories, TikTok downloads, and install a custom web-browsing plug-in. We show that ideological conservatives under-donate, as do individuals who self-report exposure to sensitive or polarized content online. We demonstrate how these biases impact inference and offer a re-weighting solution using supervised machine learning approaches.

Much of the existing COVID-19 research has focused on the politicization of the pandemic in the United States, where public opinion diverged along party lines in outcomes like public health guidelines, policy responses, and individual health behaviors. However, little attention has been paid to divergences in public opinion on perceptions of what groups were negatively impacted most by COVID-19.

Using original data linking social media digital trace data from Twitter and survey responses from 2020, we explore both traditional associations with COVID-19 polarization like Trump support and partisanship, and new factors such as traditional media and social media consumption. We ask how exposure to conservative media and social media influences individuals' beliefs about which racial and class groups were most negatively affected by COVID-19's economic fallout.