Content analysis of a sample of images about climate change on Twitter

Laburpena

We carried out a content analysis of images (photographs, illustrations and graphics) posted on Twitter, during five randomly selected weeks between 28 November 2019 and 29 November 2020. The random process of selecting five weeks, performed using the website random.org, yielded the following weeks: 3, 11, 22, 32 and 45. These weeks correspond to the following dates: Week 3: from 11 to 17 November 2019 Week 11: from 6 to 12 January 2020 Week 22: from 23 to 29 March 2020 Week 32: from 8 to 14 June 2020 Week 45: from 7 to 13 September 2020 The sample was selected using the Twitter API (twitter.com) by selecting the “top tweets” that included photos or videos and were posted during the periods mentioned. The sample was chosen on 30 January 2021. We considered that the time interval between the tweet dates and the date the sample was chosen allowed enough time for each image to reach its full interaction potential. The searches carried out using the Twitter API were as follows: 1. “climate change” since:2019-11-25 until:2019-11-17 filter:media 2. “climate change” since:2020-01-12 until:2020-01-06 filter:media 3. “climate change” since:2020-03-29 until:2020-03-23 filter:media 4. “climate change” since:2020-06-14 until:2020-06-08 filter:media 5. “climate change” since:2019-09-13 until:2019-09-07 filter:media Each of these searches yielded a result of between 90 and 100 tweets. The results were saved on a spreadsheet and all of the fixed images were selected (photographs, graphs, illustrations, etc.). When several images appeared on the same post, we considered each one of them independently. Besides the images, we saved the following information for each tweet: date, user, number of likes, number of retweets, number of comments and text associated with each tweet. The interactions (number of likes, number of retweets and number of comments) were considered indicators of interest in the content of the message and therefore an indication of the potential of that image (along with the text associated) to foster public involvement in climate change. Of the 419 total images included in the initial sample, 39 contained text only (the image showed only a sign, press cutting or similar), so these were excluded, leaving a final sample (n) of 380 images. <em>Coding</em> After putting the selected images in chronological order in a database, we developed the codebook based on examples from previous studies. To classify the types of images, we used the classification system proposed by O’Neill (2017) for the most common images in traditional media: - Identifiable people: i.e. politicians, businesspeople and celebrities. - Non-identifiable people. - Impacts of climate change: i.e. episodes of extreme weather, ice melting, desertification and endangered animal species. - Energy, emissions and pollution: i.e. factory smokestacks, renewable energy sources and traffic. - Protests: i.e. demonstrations and other protest actions. - Scientific images: i.e. graphics on greenhouse gas emissions and maps of global warming. - Other images. Basing our work on the principles outlined by Climate Visuals (2018), we propose seven factors that lend effectiveness to images as a means to foster climate change engagement: - Showing real people, avoiding staged images. Images that show people expressing identifiable emotions are especially effective. Politicians, due to their low credibility and the fact that they’re perceived as not being authentic, are not very effective. - Telling stories. Images that tell a story by themselves, especially the newest ones, tend to be more effective at fostering public involvement. - Showing the causes of climate change on the appropriate scale. For example, showing a gridlocked motorway could be more effective than showing a single driver. Images that show individual behaviour (such as eating meat) can trigger defensive reactions and may not be effective. - Showing powerful climate impacts. For example, floods and the effects of extreme weather, which can have a huge emotional impact. - Showing solutions. The levels of involvement and the ideology determine the response to the images. However, images that show “solutions” to climate change tend to generate positive emotions. - Establishing local connections. It’s a good idea to use images that connect climate change with a local environment. However, at the same time, they should connect with the problem on a global level. - Showing people who are directly affected. Although images of protests tend to generate scepticism among most observers, protests by people who are directly affected by climate change are usually perceived as more authentic and emotionally moving.