Identifying Political Topics in Social Media Messages: A Lexicon-Based Approach

Abstract

In this paper, we introduce a lexicon-based method for identifying political topics in social media messages. After discussing several critical shortcomings of unsupervised topic identification for this task, we describe the lexicon-based approach. We test our lexicon on candidate-generated campaign messages on Facebook and Twitter in the 2016 U.S. presidential election. The results show that this approach provides reliable results for eight of nine political topic categories. In closing, we describe steps to improve our approach and how it can be used for future research on political topics in social media messages.