Social Media in State Politics: Mining Policy Agendas Topics

Abstract

Twitter is a popular online microblogging service that has become widely used by politicians to communicate with their constituents. Gaining understanding of the influence of Twitter in state politics in the United States cannot be achieved without proper computational tools. We present the first attempt to automatically classify tweets of state legislatures (policy makers at the state level) into major policy agenda topics defined by Policy Agendas Project (PAP), which was initiated to group national policies. We investigated the effectiveness of three popular machine learning algorithms, Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory Network (LSTM). We proposed a new synthetic data augmentation method to further improve classification performance. Our experimental results show that CNN provides the best F1 score of 78.3%. The new data augmentation method improves the classification perfromance by about 2%. Our tool provides a good prediction of the top three popular PAP topics in each month, which is useful for tracking popular PAP topics over time and across states and for comparing with national policy agendas.