Text has always been an important data source in political science. What has changed in
recent years is the feasibility of investigating large amounts of text quantitatively. The internet …

Objectives Unstructured free-text patient feedback contains rich information, and analysing
these data manually would require a lot of personnel resources which are not available in …

The recent increase in digitally available data, tools, and processing power is fostering the
use of computational methods to the study of communication. This special issue discusses …

This study examined the echo chamber phenomenon and opinion leadership on Twitter
based on the 2016 US presidential election. Network analysis and 'big data'analytics were …

Supervised machine learning methods are increasingly employed in political science. Such
models require costly manual labeling of documents. In this paper, we introduce active …

We put forward a new approach to studying issue definition within the context of policy
diffusion. Most studies of policy diffusion—which is the process by which policymaking in …

The shocking election of President Trump spawned myriad analyses and post-mortems, but
they consistently underestimate the crucial role of the Tea Party on the GOP and Republican …

Social scientists have long hand-labeled texts to create datasets useful for studying topics
from congressional policymaking to media reporting. Many social scientists have begun to …

Researchers often rely on humans to code (label, annotate, etc.) large sets of texts. This kind
of human coding forms an important part of social science research, yet the coding process …

We propose a five-step computational framing analysis framework that researchers can use
to analyze multilingual news data. The framework combines unsupervised and supervised …