This paper displays that the collaboration between social science and computer science scholars proves fruitful in increasing conceptual and methodological innovation in inquiry fit for the digital world. It proposes arguments for ways in which a multi-disciplinary approach can reinforce media studies and innovatively foster both research breadth and depth. To elucidate this interesting link of both disciplines, the paper proposes the example assay of large data from Twitter and debates this assay in a communication science research environment. It presents TwiNeR, a software tool that analyzes tweet content employing an advanced language modeling approach for classifying tweets into five prototypical messages referring to ‘activities’ linked 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).