My research examines the social media behavior of politicians. Using machine learning methods to curate lists of Twitter accounts by class, such as politicians, journalists, influencers, etc, I research temporal and topical patterns of how political communication – including campaign outreach, network alignments, hate speech, and polarization play out online before, during, and after major electoral campaigns. My work is primarily based on Twitter data, using the Twitter academic API to pull amd store tweets of accounts identified through an iterative process of shortlisting handles of interest. Thereafter, we use amix of descriptives and advanced statistical techniques to seek patterns in the data.
Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.
Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.
Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.
Ben studies the social and political impacts of government algorithms. This work falls into several categories. First, evaluating how people make decisions in collaboration with algorithms. This work involves developing machine learning algorithms and studying how people use them in public sector prediction and decision settings. Second, studying the ethical and political implications of government algorithms. Much of this work draws on STS and legal theory to interrogate topics such as algorithmic fairness, smart cities, and criminal justice risk assessments. Third, developing algorithms for public sector applications. In addition to academic research, Ben spent a year developing data analytics tools as a data scientist for the City of Boston.
I have been involved in the building of data infrastructure in the study of elections, political systems, violence, geospatial units, demographics, and topography. This infrastructure will eventually lead to the integration of data across many domains in the social, health, population, and behavioral sciences. My core research interests are in elections and political organizations.
My research focuses on the causes, dynamics and outcomes of conflict, at the international and local levels. My methodological areas of interest include spatial statistics, mathematical/computational modeling and text analysis.
Map/time-series/network plot, showing the flow of information across battles in World War II. Z axis is time, X and Y axes are longitude and latitude, polygons are locations of battles, red lines are network edges linking battles involving the same combatants. Source: https://doi.org/10.1017/S0020818318000358
Jeffrey D. Morenoff is a professor of sociology, a research professor at the Institute for Social Research (ISR), and a professor of public policy at the Ford School. He is also director of the ISR Population Studies Center. Professor Morenoff’s research interests include neighborhood environments, inequality, crime and criminal justice, the social determinants of health, racial/ethnic/immigrant disparities in health and antisocial behavior, and methods for analyzing multilevel and spatial data.
Every year, states negotiate, conclude, sign, and give effect to hundreds of new international agreements. Koremenos argues that the detailed design provisions of such agreements matter for phenomena that scholars, policymakers, and the public care about: When and how international cooperation occurs and is maintained.
Theoretically, Koremenos develops hypotheses regarding how cooperation problems like incentives to cheat can be confronted and moderated through law’s detailed design provisions. Empirically, she exploits her data set composed of a random sample of international agreements in economics, environment, human rights and security.
Her theory and testing lead to a consequential discovery: Considering the vagaries of international politics, international cooperation looks more law-like than anarchical, with the detailed provisions of international law chosen in ways that increase the prospects and robustness of cooperation.
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.