Research

Working Papers

“Media Slant and Offline Polarization in the Online Era” (Draft)

Abstract. I demonstrate that political polarization can intensify due to innovations in the information market even if a population’s ideological distribution is fixed. Viewership-maximizing news firms cater to a diverse audience who assess source accuracy using noisy private signals that vary in precision and ideological bias. If better-informed consumers disproportionately migrate to newer platforms for news (e.g., the Internet), traditional media firms increase news slant to appeal more to less-informed partisans on both sides of the ideological spectrum. This leads to a greater divergence in beliefs about the state of the world among partisan traditional media audiences—increasing disagreements and potential hostility. The theory helps explain two empirical trends observed over recent decades: (i) rising slant in traditional news (e.g., cable television news) and (ii) increasing polarization among demographic groups least likely to use the Internet. I test the model’s central mechanism using an algorithmic text analysis of Facebook posts from 600 U.S. local television news stations. Media markets with greater expansions in moderately high-speed Internet access between 2012 and 2016 exhibit significantly larger increases in news slant, controlling for economic, demographic, and voting characteristics.

“An LLM-Aided Measure of Media Slant” (Draft)

Abstract. I present a method for measuring media slant using social media text data. Posts from political figures and media outlets are collected and processed through a large language model to extract “frames”—standardized declarative claims that encapsulate the core message of each post. These frames are embedded into a shared semantic space and clustered using a hierarchical density-based clustering algorithm, representing broadly held positions among politicians and media firms. For each account, vectors are constructed to represent their post frequency across each cluster. I separate the vectors for politicians and use party labels to train a classifier model that predicts political affiliation. The trained model is then applied to the vectors of media outlets, generating probabilistic scores that position each outlet on a partisan spectrum. This method contributes to the literature on measuring media slant using modern machine-learning tools to better discern semantic patterns in language and allows for granular separation of partisan positions and a nuanced measure of partisan slant without sacrificing interpretability.

“Optimal Allocation of Fact-Checking Resources on Long-Term Prevalence of Misinformation” with Patrick Warren & Morgan Wack (Theory Draft)

Abstract. I use a stylized compartmental model to analyze the long-term dynamics of misinformation propagation in social networks, focusing on the allocation of fact-checking resources. I conceptualize a false narrative as spreading through multiple types of claims, which can differ in their virality and resistance to fact-checking interventions. The analysis reveals that harder-to-debunk claims can persist when fact-checkers concentrate on easy-to-debunk claims—an approach commonly arising from crowd-sourced, consensus-based systems such as Community Notes—and ultimately become the primary vector sustaining the false narrative over time. I characterize the optimal allocation of fact-checking effort and show that, given sufficient resources, effective long-term mitigation of misinformation requires devoting resources to both easy and hard-to-debunk claims, no matter their initial virality or perceived cost. These findings challenge the prevailing focus on short-term fact-checking ``successes” and underscore the need to supplement crowd-sourced interventions with targeted professional fact-checking of complex or resilient misinformation. The theoretical framework provides actionable guidance for platforms and policymakers seeking to minimize the long-run societal impact of persistent false narratives.

An empirical extension of this paper is underway with Patrick Warren and Morgan Wack.