Critical news reading with Twitter? Exploring data-mining practices and their impact on societal discourse

Abstract

AbstractThis article shows that the collaboration between social science and computer science scholars proves fruitful in enhancing conceptual and methodological innovation in research appropriate for the digital world. It presents arguments for ways in which a multi-disciplinary approach can strengthen media studies and nnovatively advance both research breadth and depth. To illustrate this interesting connection of both disciplines, we present the example analysis of large data from Twitter and discuss this analysis in a communication science research environment. We propose TwiNeR, a software tool that analyzes tweet content using an advanced language modeling approach for classifying tweets into five prototypical messages referring to ‘activities’ related to news and news sources in the Twitter network (i.e., source-fed article, user-fed article, content spread by user, other source content, other user content).