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Chapter 486research as the most prevalent sets of keywords that actors use online when referring to processed food. To include dominant online English-speaking voices, the queries were conducted in the two largest English-speaking nations in the Western world (List of Countries by English-Speaking Population, n.d.), namely, the United States and the United Kingdom, and their 50 top-ranked results were integrated. This list was cleaned of duplicates and URLs that were unavailable or URLs that did not include content about processed food. The cleaned URL list was used for search-within-a-domain Google queries with the terms “processed food” and “food processing”. The two top-ranked pages from every URL that contained textual information about processed food and visualizations35were included in our dataset, which ultimately contained 164 web pages and their 344 visualizations.36We downloaded those pages (text + visuals) into Atlas.ti software to further analyse them.4.3.2 Data analysisOur unit of analysis was a page (web page) that belongs to a particular actor and communicates a particular sentimental storyline. We first coded the actor to which every page belonged and categorized the actors. We adapted the categories suggested by Cullerton et al. (2016), acknowledging the emergence of new actor categories through new media (Vaast et al., 2013) (see Supplemental Material, Annex B, Table B1). In the second step, we coded for the overall sentiment expressed in each page based on a manual analysis of the complete text, which is valuable for revealing the valence of emotions evoked from it (Lappeman et al., 2020), and the reading of the title of the page, which may place the page’s audience in a particular relationship with its content (O’Neill, 2013). Following this manual sentiment analysis, we constructed the online sentiment coalitions: we grouped the actors that shared a predominantly positive, negative or balanced sentiment about processed food. 35 We did not include webpages in a PDF format, as this format contains layouts that are often inappropriate for the type of analysis conducted.36 To avoid over-representation of a particular actor, we limited the scraping of images from a particular URL to the first 10 images.Efrat.indd 86 19-09-2023 09:47