The rise of social media poses unprecedented challenges to authoritarian regimes seeking to influence public attitudes and behaviors. Authoritarian actors have adapted by shifting from tightly controlled, top-down messaging to more diffuse approaches on digital platforms.
Online political advertising is booming. But how much does scholarship know? This gap exists because most research uses only televised ads.
Recent research highlights social media's potential for collective grievance, but in active conflict online signs of political loyalty can place civilians at great risk. A security-driven theory predicts that civilians will alter their online behavior after major shifts in territorial control: specifically, they should post more positive content and more content favoring the winning side.
This research investigates when speech on social media is judged toxic enough to deserve platform intervention. Platforms set rules and depend on user reports, but little is known about what everyday users consider unacceptable and what moderation actions they prefer.
This article introduces a scalable topic-modeling approach aimed at enabling political scientists to analyze extremely large text corpora. The authors propose tensor latent Dirichlet allocation (tensor LDA) and report theoretical identifiability and sample-complexity guarantees, an open-source GPU-based implementation, and computational performance gains that make it feasible to study social-media phenomena at scale. The article illustrates the method with two large, applied analyses: a multi-year study of the #MeToo movement on Twitter and a study of social-media conversations about alleged election fraud in the 2020 U.S. presidential election.
The Chinese government operates a massive social media operation disguised as public opinion. This study provides empirical evidence that identifies 50c posts, revealing they don't engage critics but instead distract by promoting party narratives and avoiding controversy. Analyzing over 448 million fabricated comments annually demonstrates the regime's focus on changing the subject rather than debating issues directly.
This article explores factors influencing belief in misinformation shared on social media platforms.
Negative economic outlooks during the COVID-19 pandemic significantly influenced political discourse and voter sentiment, particularly in the United States.
Social media platforms like Twitter are increasingly central to political conflicts, especially for non-democratic regimes facing protests.
In this article, we delve into PollyVote's forecast for the popular vote outcome of the 2020 U.S. Presidential Election.
Descriptive representation and other political science concepts are essential for understanding governance, but economic conservatism among affluent populations often seems tied to class interests. This paper challenges that view by showing how desire for social status plays a role.
Political scientists frequently classify documents to measure variables like speech ideology or whether text describes a Militarized Interstate Dispute. Simple classifiers often perform well for these tasks, but when words appearing early in a document change the meaning of words that come later, models that capture time-dependent relationships may raise accuracy.
Political language shapes civic discourse, and social media lets ordinary users adopt elite rhetoric. Standard ideological text-scaling tools break down on the short, informal posts typical of platforms such as Twitter, preventing meaningful individual-level inference.