Critical News Reading with Twitter? Exploring Data-Mining Practices and Their Impact on Societal Discourse

This article shows that the collaboration between social science andcomputer science scholars proves fruitful in enhancing conceptual and method-ological innovation in research appropriate for the digital world. It presentsarguments for ways in which a multi-disciplinary approach can strengthen me-dia studies and innovatively advance both research breadth and depth. To illus-trate this interesting connection of both disciplines, we present the exampleanalysis of large data from Twitter and discuss this analysis in a communica-tion science research environment. We propose TwiNeR, a software tool thatanalyzes tweet content using an advanced language modeling approach forclassifying tweets into five prototypical messages referring to ‘activities’ relatedto 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).