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Visual and Textual Framing by Coalitions in a Policy Controversy874The next step was analysing the textual framing. We coded the text of the pages of each sentiment coalition for particular framings of processed food (see supplemental material, Annex B, Table B2). A first set of frames was defined deductively based on academic papers about food technology issues (Aschemann-Witzel et al., 2019; Marks et al., 2007; Nisbet & Huge, 2007; Nisbet & Lewenstein, 2002; Oleschuk, 2020). These frames were: “environmental harm”, “environmental opportunity”, “health opportunity”, “health threat”, “home cooking”, “many possibilities”, and “safety concerns”. New frames were added inductively along with the analysis. These were: “food security”, “injustice”, “nutritional value”, “safety standards”, and “lack scientific evidence”.Next, we coded for (1) type of visual (e.g. photograph, infographic),37 (2) the content (“what is depicted?” e.g. people, food),38 and (3) the visual frame. The visual frames were interpreted inductively based on the reading of denotive and connotive signs. In denotive reading, the visual was interpreted “literally” (see also “denotative content”, O’Neill, 2013, p. 13), for example, a visualization portraying happy people involved in food-related activities was coded with the visual frame “food happiness”. Frames based on denotive reading were: “abundance”, “contemplation”, “food classification”, “food happiness”, “industrial- food-people”. In connotive reading, implicit meaning, usually culture-dependent, was revealed (see also “connotative content”, O’Neill, 2013, p. 13), for example, a woman who holds her head in a way that implies she has a headache was coded with the visual frame “unpleasantness”. Frames based on connotive reading were: “body care”, “unpleasantness”. Finally, we analysed the textual and visual framing of the three sentiment coalitions.4.4 Results: Framing the dream, nightmare, or providing information4.4.1 Online sentiment coalitionsOverall, the online negative sentiment coalition about processed food was the largest one in our data set (Figure 4.2). We can also see that “journalist” was the 37 Visualization-type codes were adapted from Morseletto (2017) and from a series of project meetings in which the researchers coded images for their type and discussed disagreement until consensus was achieved.38 For the content analysis method see Bell (2001) and Rose (2016).Efrat.indd 87 19-09-2023 09:47