Twitter has served as a central forum for academics to communicate with colleagues, policymakers, and the public. Elon Musk's takeover brought broad changes to the platform—reduced public data access, shifts in moderation and misinformation policy, and altered platform affordances. This study asks a narrow empirical question: how did those changes affect academic activity on Twitter?
This study identifies the psychological drivers behind political fake news sharing on Twitter, testing three competing explanations: ignorance, disruptive intent, and partisan polarization.
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 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.
This study uses a neo-institutional lens to examine how German police use Twitter for external communication. The topic matters because police increasingly use social media to bypass traditional media gatekeeping and to gain more direct control over public messaging. The analysis asks whether organizational differences—across governance levels and local environments—affect communication strategies and user engagement.
This study investigates the impact of imprisoning Saudi activists for online dissent. Analyzing over 300 million tweets and Google search data from 2010-2017, we find two key effects.
This article examines how rebel organizations in civil conflicts can leverage social media to project a favorable image and gain international support. Focusing on the Libyan civil war, we analyze the rebels' diplomatic efforts during their campaign for US backing.
Social media played a crucial role in the Egyptian Revolution of 2011 by enabling 'first-mover mobilization'. This study explores how Facebook and Twitter helped organize the January 25th protest, which acted as a tipping point for hesitant revolutionaries.
Understanding the effect of international social ties on domestic ideological polarization is challenging because the counterfactual—what would have happened without cross-border links—cannot be directly observed. This study uses a new statistical approach to estimate that missing comparison and separate polarization into domestic and transnational components following the Arab Uprisings.
When people interact, one individual's outcome can be affected by others' treatment status across multiple networks (for example, online and face-to-face ties). Estimating the spillover effect for a specific network is essential to understand how influence travels, but unbiased estimation typically requires observing all relevant networks — an assumption often violated in practice.
The study examines whether and to what extent state-managed social media were used for partisan purposes during the impeachment crisis of President Dilma Rousseff.
Social media use has surged among citizens and politicians in Brazil, and platforms like Twitter became central arenas for debate and propaganda during the highly polarized 2014 legislative and presidential elections. This study asks whether the decision to 'follow' a profile on Twitter can be used to estimate politicians' ideological positions and whether that approach can expose variation within a fragmented legislative body such as the Brazilian Chamber of Deputies.
Many social-science questions ask how individuals' influence shifts over time, but answering them is hard when network data are sampled. Sampling removes ties and can bias inference, and frequent snapshot data are usually required to observe change. This paper introduces a practical method that recovers influence rankings from sampled, longitudinal network data and makes daily tracking of influence feasible.