Data news refers to news reports which are based on data analysis and presented in visualized texts (verbal texts, interactive and/or static images) and/or videos. It is often instantiated as multimodal artefacts. Some media studies scholars (Appelgren & Nygren 2014; Wu 2017 etc.) identify it as a new genre, arguing that its main purpose has shifted from telling stories to deducing factual conclusions. However, it has not been made clear what genre it is. Neither has it been made clear how data news as a genre can be realized semiotically or linguistically. In the present paper, we try to build up a genre model for analyzing data news by integrating ideas from the genre model in the Sydney School (Martin & Rose 2008) and the multimodal genre model (Bateman 2008, Bateman & Wildfeuer & Hiippala 2017). Following the two models, we approach genre from a social semiotic perspective and conceptualize the relationship between semiotic systems and context as natural and bi-directional.

Accordingly, discourse semantics (Martin & Rose 2007, 2003; Bateman & Schmidt 2012) works as the toolkit for capturing generic features in the present study. We will first analyze the configurations of multimodal meaning at the stratum of discourse semantics from the perspective of intermodal and inter-metafunctional couplings, and then move on to see how these meaning configurations help to identify stages of an elementary genre and to map out genre relations. The data for the analysis is America Under the Gun, one of the 72 Data Journalism Award finalists in 2013. It is an online multimodal artifact, which includes a multimodal headline in the top middle, 5 verbal texts, 3 graphs, 3 images, and 4 maps.

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Wu, X. (2017): Shuju Xinwen: Lilun Chuancheng, gainian shiyong he jieding weidu (Data journalism: Theoretical evolutions, concepts and defining features. in: Xinwen Chuanbo Yu Yanjiu (Journalism and Communication) 10, 120-126.