Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Economics
Committee Chair/Advisor
Patrick Warren
Committee Member
Robert Fleck
Committee Member
David Drukker
Committee Member
Daniel Stone
Abstract
This dissertation presents three chapters that contribute to the study of economic forces shaping disagreements in society.
In Chapter 1, 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.
In Chapter 2, 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.
In Chapter 3, 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.
Recommended Citation
Alam, Mustafa W., "Three Essays in the Economics of Disagreements: Incentives, Measurement and Persistence" (2025). All Dissertations. 4057.
https://open.clemson.edu/all_dissertations/4057
Author ORCID Identifier
0009-0008-4565-2340
Included in
American Politics Commons, Econometrics Commons, Economic Theory Commons, Industrial Organization Commons, Models and Methods Commons, Political Economy Commons, Theory and Algorithms Commons