Intermedia agenda setting is a widely used theory to explain how content transfers between news media. The recent digitalization wave, however, challenges some of its basic presuppositions. We discuss three assumptions that cannot be applied to online and social media unconditionally: one, that media agendas should be measured on an issue level; two, that fixed time lags suffice to understand overlap in media content; and three, that media can be considered homogeneous entities. To address these challenges, we propose a “news story” approach as an alternative way of mapping how news spreads through the media. We compare this with a “traditional” analysis of time-series data. In addition, we differentiate between three groups of actors that use Twitter. For these purposes, we study online and offline media alike, applying both measurement methods to the 2014 Belgium election campaign. Overall, we find that online media outlets strongly affect other media that publish less often. Yet, our news story analysis emphasizes the need to look beyond publication schemes. “Slow” newspapers, for example, often precede other media’s coverage. Underlining the necessity to distinguish between Twitter users, we find that media actors on Twitter have vastly more agenda-setting influence than other actors do.

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News media regularly include the voice of the "man or woman in the street" alongside that of the actors involved in a news story. Journalists use these vox pops to give an impression of public opinion. With the coming of social media, access to people's opinions has never been so easy. Little research exists about how social media (Twitter in particular) are used by journalists to describe public opinion. This is the question this research aims to answer by using a combination of a qualitative and a quantitative content analysis of Dutch and Flemish news websites. We found several patterns regarding the use of Twitter vox pops. First, we found Twitter to be regularly used as a representation of public opinion. Second, many items generalised these opinions to larger groups with strong -mostly negative-emotions. Third, when referring to Twitter, the articles used (abstract) quantifiers and hyperbolic terms (commotion, explosions) to imply an objective basis for these inferences about the "vox Twitterati".

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