Rebekah Tromble is Assistant Professor of Political Communication in the Institute of Political Science at Leiden University. Her research combines interests in the political uses of social media, public discourse (both online and offline), digital research methodology and ethics, and computational social science. Her most recent work investigates how and why politicians across multiple countries use social media to engage in reciprocal dialog with members of the public. She is currently working several projects: one that maps and unpacks the biases that are generated when using common techniques for collecting social media data; another that examines the extent of echo chambers, incivility, and intolerance in political conversations on Twitter; and a third that develops computational techniques for both assessing the quality of online news reporting and detecting misinformation.
So-called fake and quality' news are often juxtaposed in popular discourse. Fake news is assumed to distort democratic choices, while quality information provides a guide for responsible citizenship. However, decades of scholarship suggests that it is not quite this simple. Based on research into traditional mass media, we know that citizens rarely deeply process and learn from quality information. At the same time, we know relatively little about how such information is processed in online environments, where much of it is encountered incidentally by citizens who are otherwise inattentive to public affairs. We also know little about the effects of misinformation, per se.
Rebekah's work therefore seeks a better understanding of the extent and impact of both quality and false or misleading information in online political news. Before we can understand the impacts of this information, however, we must first detect it. In its first phase, the project is therefore focused on developing the Quality of Political Reporting Index (QoPRI), a theoretically-grounded and comprehensive metric for evaluating the quality of individual online news stories. Index items are being translated into news story-level indicators and coded using automated and supervised machine learning approaches. In its second phase, the project will then assess the relationship between quality news and false news, both theoretically and empirically and examine whether and what QoPRI items are predictive of false news more specifically.