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Patterns of Type and Content in an Online Policy Controversy492stances). Therefore, when discussing the results, we must consider the dialogic interaction between human and machine involved. There is no ‘human vision’ opposing a ‘machine vision’. Our results have analytical value when the machine mode of seeing is contextualised – what we explored when ‘grounding’ the online products (Rogers, 2019).Moreover, when working with computer-generated visual networks, we must account for the implications of statistically treating visual content. The machine associates the image content based on a labelling and tagging system of the image, enabling the algorithm to operate to cluster the images. This approach is appropriate to specific research questions like ours, but it does discard other possibly relevant visual information. Due to the algorithm’s dependence on the ‘textual representation of the image’ in the tag, we run the risk of considering the computer vision output as the truth. The manual interpretation of the visuals and relating it to context specifics – the ‘grounding’ – worked to address this limitation. This also helped to bridge the division between image and text. To integrate textual and visual analysis, without overlooking our main object – visuals – we used the actors’ textual content to inform their positioning by integrating interpretive (qualified) and digital (quantified) understanding.Another criticality is that many important actors in the controversy were not speaking for themselves online (through personal websites, for instance), being recognisable and reachable only through informants. To mitigate this bias, we included on our actor’s list those who were mentioned by top-ranked news and stakeholders URLs (as explained on Methods section). We determined each actor’s position based on their online content. Even though we were not able to verify whether this is their real position within the scope of this research, we minimised bias in the positioning with two independent researchers’ assessment.In the future, visual network and visual framing studies of all sorts of sustainability controversies can build on our automated visual analysis combined with actors’ stances and improve the complementarity of interpretive and digital methods. Moreover, our exploratory study can be further strengthened by statistical analyses of the relations between actors’ standpoints and the use of type and content of the visuals to better comprehend the role visuals play in spreading online information and misinformation.Efrat.indd 49 19-09-2023 09:47