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Chapter 248spread of visual information by online actors with a positive, negative, or neutral stance about hydraulic fracturing for shale gas exploration. The results revealed (1) the top-ranked actors in the online debate in three different internet regions, (2) their positions in the debate, and (3) indicated patterns in the relations between position in the controversy, and the posting of particular image types. In addition, we showed that online actors do change their position over time, and the posting of visualisations changes accordingly.One main conclusion is that there were no changes in the general spread of type and content of visuals, which in this case were people (spokesperson, protest groups, workers), landscapes (natural and industrial) and data (graphs, text, icons, and infographics). The division over these different categories remained the same between 2018 and 2019. This indicates a thematic saturation on shale gas controversy’s visuals, meaning that there is a specific set of visualisation types (people, landscapes, and data) that are strongly associated with shale gas extraction discussions.Besides, controversy stage, actor’s position in the debate, and the use of these visuals seem to be related. In the early stages when shale gas seemed a promising energy source, governmental actors and industry dominated the online debates, and posted photographs of governmental officials (Mexico), but when the controversy intensified opponents varied more in types of images. They used relatively more data visualisations of environmental risks. In SA and the UK, the online controversy made significant shifts between 2018 and 2019. In SA, the opponents became more dominant in the online debate, mostly because of a drop out of proponents. In the UK, the debate became more neutral-negative, probably because of a shift in attention in the public and policy debates toward Brexit. This shift in issue attention is a phenomenon well known in media-attention studies (Downs, 1972).Hence, when ‘grounding’ the results, local ephemerides in each country offer interesting insights about the differences in these images’ usage when comparing places and positions over time. The findings were achieved through classic content analysis techniques, manual coding (for the identification of relevant actors’ position), and automated visual network analysis (which relied mostly on computer vision for clustering images per type and content and identifying Efrat.indd 48 19-09-2023 09:47