1. WESLEYAN UNIVERSITY
SUMMER RESEARCH POSTER SESSION
July 30, 2015
Name: Joli Holmes Year: 2017
Faculty Mentor: Logan Dancey Department: Government
Title: Topic Modeling Party Messaging from Congressional Tweets
Abstract:
This project uses Twitter data from members of Congress to explore the messaging
patterns of political parties, party leaders, and representatives within the parties. We use a
topic modeling technique called latent Dirichlet allocation (LDA). Given an unstructured
group of text documents, LDA organizes the text documents by identifying themes based
on the co-occurrenceofwords that are present in the group of documents.i The organized
documents can then be compared by the themes, or topics, identified in each document.
Each document in our corpus represents six months of tweets from each member of
Congress as well as documents for each of the party accounts and all congressional
committee accounts. The data come from the period of July 2014 through December 2014,
and uncovered topics related to several major events and issues during this time period,
including immigration, Ebola, Ferguson, and sexual assault on campus and in the military.
The topic model also shows clear topics that are consistent with party messaging, with
Republicans focusing on Obamacare, immigration, jobs, and energy, and Democrats
focusing on minimum wage, women’s rights, the middle class, and immigration. Both
party leaders and representatives participate in party messaging, although party leaders and
party accounts are the most frequent users of these party messaging topics. Our results
suggest that Democrats participate in party messaging more than Republicans. In future
work, we intend to explore which members are more or less likely to adopt the party
message on Twitter. These data can also be used to explore the issue priorities of both
parties and individual members of Congress.
i David Blei, “Probabilistic Topic Models”, Communications of the ACM 55, no. 4 (April 2012): 77.