Facilitators: Federica Bardelli // Carlo De Gaetano // Sabine Niederer // Warren Pearce //
Participants: Gabrielle Aguilar // Laura Bruschi // Miranda García // Giulia Giorgi // Matthew Hanchard // Bakar Abdul-Rashid Jeduah // Natalie Kerby // Goran Kusić // Bruno Mattos // Samir van Oeijen Rodríguez // Alessandro Quets // Eivind Røssaak // Miazia Schueler // Zijing Xu // Xin Zhou // Chloe Sussan-Molson // Maud Borie // Alireza Hashemzadegan // Misha Velthuis
Sea-level rise has long been one of the most locally tangible impacts of climate change, both now and in the future. Due to accelerating climate change, the annual rate of sea-level rise has almost tripled over the last century, and the mean sea level rise is expected to rise 0.3m-1.0m by 2100 (Duijndam et al., 2021). The IPCC states that risks include increased flooding, erosion, loss of ecosystems and permanent submergence (Oppenheimer et al., 2019). In the UK, there are fierce debates over whether to protect or surrender coastal homes threatened by sea-level rise (Fisher, 2022), while in the Netherlands the trust in its strong water management and engineering tradition has led to the so-called myth of the dry feet—the idea that sea-level rise in the Netherlands, a country that in part lies below sea-level, can be countered by merely building higher dams (Schuttenhelm, 2020). Scenarios for the future of the Netherlands include new adaptation strategies of living with the water, in which parts of the land are given back to nature to preserve larger cities (Deltares, 2019). Globally, some of the world’s most populous cities, such as New York, Bangkok and Shanghai are amongst the most vulnerable (C40 Cities, 2018), while the existential threats to small islands such as Kiribati, Seychelles and the Maldives could result in entire states disappearing from the world (Martyr-Koller et al., 2021). Emblematic images of people wading through the flooded streets of Venice holding up their shopping bags or stopping for a coffee travelled the news and social media outlets as an illustration of the climate crisis, and the collision of rising sea levels, a sinking city, surging seasonal winds and failing governance as the city experienced its worst floods in 50 years (National Geographic, 2019).
There have been some notable efforts to visualise scientific projections of sea-level rise (e.g. Climate Central, 2015), as well as more creative attempts to communicate the threat such as the iconic Der Spiegel depicting a submerged Koln Cathedral (Mahony, 2016). Yet it is argued that sea-level rise remains a relatively low public concern given the huge potential risks to ecosystems and human habitats (Akerlof et al., 2017), while a recent advanced review of digital media research on climate communication found no research focused on the issue (Pearce et al., 2019). In this project, we will try to fill this gap, looking to see how both present and future sea-level rise is being imagined and interpreted on social media platforms, in terms of textual and visual content, information sources, locations, and point in time (i.e., future or present).
How is sea-level rise being imagined and presented on and across the social media platforms of Instagram, TikTok and Twitter?
CONTENT (What?) What kinds of content are associated with the topic of sea-level rise?
AFFORDANCES (How?) What kinds of formats and scenographies are used for the topic of sea-level rise?
USER COMPOSITION (Who?) Which kinds of users are responsible for the most-engaging posts?
TEMPORALITY (When?) Is sea level rise something of the past, present or the (near or far) future?
LOCALITY (Where?) Which locations are mentioned and depicted and how?
Team: Laura Bruschi, Miranda Garcia, Giulia Giorgi, Alireza Hashemzadegan, Samir van Oeijen Rodríguez, Misha Velthuis.
Short description: investigating sea-level rise on Instagram
Query: "sea level rise", #sealevelrise, "rising sea", "rising sea levels", "rising water", #risingwater, “rising oceans”
We queried Instagram through CrowdTangle, selecting the time frame (2013-2022). From the results, we created subsets of 100 most engaging posts per year and downloaded the images. Instagram dataset available here
Our main objective was to investigate how the issue of sea level rise is represented on Instagram through the years. Considering that our dataset spanned 9 years (2013-2022), we started our research through a hashtag analysis, which we used to gain insights on the general trends and topics. Using an online tool, we extracted the top 25 hashtags per year, based on occurrences, and mapped them onto a flowchart ( see Figure A in the findings).
As we will illustrate, we found a predominance of location and content type hashtags, which led us to focus our analysis on these two aspects. Thus we: (1) analysed the description of the posts to find the most frequently mentioned locations; (2) manually and qualitatively categorised the most recurrent users.
For the geographical analysis, we initially were pursuing to build a new dataset focusing on a content consumption perspective. This way, we would be able to see how the results output of a hashtag search differed from place to place (using VPNs). However, the results of hashtag queries in the UI turned out to be almost identical regardless of VPN Region. We then tried querying different search terms (as opposed to hashtags) and encountered 2 further problems:
In the web interface, as opposed to the mobile app, it is not possible to search via generic queries. We needed the web interface queries, since the scraping tool works with the web interface.
When translating hashtags into different languages, we found only limited amounts of results.
Given these two obstacles, we decided to go back to the original dataset and focus on the locations that were referenced in the content.
Initially our aim within location content research was to look at locations in images, given that Instagram is an Image-focused platform. We explored Google Vision software to analyse locations identified, but ran into technical problems and time-constraints. A quicker and more fruitful approach turned out to be to look at locations mentioned in the descriptions of the top 100 posts of each year.
We fed said descriptions into a Python tool called Mordecai, which then identified words it categorised as locations using spaCy’s entity recognition. Following this, the tool would use said words and spaCy’s named entity location library as input for geoparsing, outputting location names, administrative regions, countries and coordinates.
We arrived at a set of the named locations of the top 100 posts from 2013 to 2022. From this we continued in 3 directions. The first was to input all these locations, with included coordinates, into a world map, acquiring a heatmap of the most mentioned locations, with brighter points for places mentioned more often. The steps followed were: to prepare the maps, we initially had to make our preliminary database suitable for visualizing geographical locations of the top 100 posts by removing entities for which Mordecai did not find a longitude and latitude. Then, we used the web application of Kepler for geospatial analytic visualizations, to portray our findings on the world map providing a heatmap of top locations. Finally, we combined the maps from 2013 to 2022 in a gif file to present the evolutions during this 10-year period (see Figures E and F).
Secondly, we used the python API of Kepler to create an interactive map, showing the mentioned locations, together with textual context of that mention and the weblink to the corresponding image. This allowed us to further explore the textual content behind the geographical heatmap (link to map on Github: click right for legend, zoom in to “draw out” (make separately visible) the multiple individual mentions of a location). For future usage of the Mordecai-Keppler set-up, it is important to note that a careful inspection of the map shows that the output still requires some cleaning up.
The third direction followed was to zoom in on the data from 2022, and to learn more about the top locations mentioned. We selected the top 6 locations mentioned in the descriptions of posts this year, identified corresponding posts, and extracted the corresponding images. We then proceeded to use the Imagesorter application to find similarities through qualitative analysis, arriving at the most used and most representative images of the top 6 mentioned locations. These images were then used as input for composite images, as described in the section Image Analysis.
We were interested in understanding which users dominated our database each year, thus we counted their recurrence and decided to focus on the top 10 users of each year. To gain a different perspective, considering that we also analysed the top 10 images of each year, we decided not to focus on those users that had the highest levels of engagement, but rather on the most recurrent ones.
The top 10 of 2013-2022 was formed of 61 unique users, which we analysed by creating a coding book to manually and qualitatively categorise the users. Our categories were:
Photography → photo reporters, photographers, collective of photographers
Art and Architecture → users, collectives and schools dedicated to art and architecture
Bottom Media → social media news pages
Non-Profit Organisations → local and international NGOs
Governmental Organisations → organisations self describing as such, affiliated with one or more governments.
Traditional Media → official press and magazines
Other → startups, business-oriented pages, users
Most of the accounts had a clear description of their activity in their Instagram bio, thus we used this information to categorise them, but whenever this information was not available we used the official websites linked to the account.
Finally, we looked at the pictures of our database and we analysed them with a qualitative approach, studying their content, scenography and temporality. We looked at each year individually to create a composite-image visualisations.
The composite images (see Figure D) were designed by selecting the 5 most interacted with images per location and per year. The images were uploaded to Photoshop, rasterized and composed in a digital collage manually, using the Auto-blend (stack) function to merge them together with seamless tones and colours. Images were not stacked automatically on top of each other, as done in previous research (see the Smart City project), where is the Auto-blend algorithm to decide which are the most distinctive features of each image to preserve, and which are the ones to discard in the final composite. Their disposition is instead decided by the researchers, in order to preserve each image visibility while composing a cohesive panorama.
Team: Gabrielle Aguilar, Federica Bardelli, Matthew Hanchard, Bakar Abdul-Rashid Jeduah, Natalie Kerby, Goran Kusić, Bruno Mattos, Miazia Schueler, Zijing Xu, Chloe Sussan-Molson.
Short description: Affordances of TikTok for envisaging sea level rise
Query: We used the search terms: sealevel, sealevelrise, rising sea levels
TikTik Dataset 1 - 198 relevant and available coded videos spreadsheet - Google Drive Link
TikTik Dataset 2 - Top 100 videos spreadsheet - Google Drive Link
TikTik Dataset 3 - Top 100 videos mp4 files - Google Drive Link
TikTik Dataset 4 - Slitscan images - Google Drive Link
TikTik Dataset 5 - Supercuts - Google Drive Link
TikTik Dataset 6 - Geoparsed locations - Google Drive Link
TikTik Dataset 7 - Carl and Rashid’s Videos Triangulated - Google Drive Link
The initial dataset provided by Carlo De Gaetano (See TikTok Dataset 1) was gathered from a personal TikTok account, rather than a clean research account, and via a standard web connection (not via a VPN). This gave the dataset some limitations in terms of the data returned being personalised, but it provided a reasonable baseline dataset for exploratory analysis to define our categories and methods before delving into a deeper study.
A separate dataset was generated from Rashid Jeduah’s TikTok account to give a sense of the level of personalisation present in Carlo’s initial dataset. Taking this as our core dataset, we used Triangulate to compare videos provided by Carlo and Rashid. There were 68 videos in common between the two datasets, showing a high degree of similarity in the highest ranked results. The large volume of commonalities between the datasets suggested that TikTok ’s ‘baseline’ results for sea level rise has greater importance than personalisation effects.Overall, we gathered 300 TikTok videos across the terms using Zeeschuimer (the new DMI tiktok scraper), comprising the top 100 videos returned per search query (‘sealevelrise’, ‘rising sea levels’, ‘sea level’ in English language only), based on the volume of plays per video, and then removed all duplicates, which left us with 285 videos. We also downloaded the full .mp4 files (here). After initially exploring the dataset inductively to gain familiarity with it, we adapted a scheme proposed by Hautea et al. (2021) who argue that TikTik has three affordances to develop a set of categories around ‘visibility’, ‘editibility’, and ‘association’, and coded data to it (see Figure 1; also TikTok Dataset 2). Thus, we structured our analysis around the 285 videos present in the original dataset and undertook coding that consisted of an investigation of the videos according to their content, seeking to understand how users presented themselves and which locations were often represented, and according to the platform affordances, features and editing techniques evoked in each one of them. We ended up with 198 videos that we considered relevant to our work. We disregarded 80 videos that were unrelated to rising sea levels and another 7 that had been removed from TikTok from the original dataset.
Team: Xin, Eivind, Maud, Warren
Short description: To get an idea of the ways in which users engaged with sea level rise on this platform, whether/how this engagement had changed over time, and of the ways in which sea level rise was made visible. We chose to look at three periods: 2012; 2015 (which included the discussions leading to the Paris Agreement in the context of COP21) and 2022.
Query: sealevelrise, risingsealevels, sealevels, sealevels and risingwaters (All tweets that mention at least one of the above keywords. Time periods selected are 2012 Nov15-Dec15, 2015 Nov15-Dec15, 2021 Nov15-Dec15)
4CAT: 2012 Nov15-Dec15: 388 items; 2015 Nov15-Dec15: 1,777 items; 2021 Nov15-Dec15: 1,899 items. See Twitter Dataset available here
Web Data Research Assistant (WDRA): 2012 Nov15-Dec15:60 items; 2015 Nov15-Dec15: 236 items; 2021 Nov15-Dec15: 373 items. See WDRA Dataset available here
Visuals associated with the tweets were also retrieved via the two platforms:
4CAT: 2021: 140 items ; 2015: 147 items; 2012: 6 items
WDRA: 2021: 128 items; 2015: 152 items ; 2012: 62 items
We used Google vision to produce web entities and labels, which were then processed via Mordecai, in order to isolate location names and their frequencies. We also use Mordecai to process the text associated with the tweets. This data allowed us to identify places represented in the dataset, both in text and in the visuals.
Manual qualitative coding was used to get a more detailed understanding of the content of the images, i.e.: do they feature people? Is a specific location represented? To which extent are particular types of ecosystems/places dominant? This allowed us to identify predominant genres and their evolution over time.
Analysis of hashtags: We analysed hashtags frequency and processed these through WordArt to visualise dominant themes
Production of composite images:
The composite images were designed by selecting the 5 most interacted with images per location and per genre. The images were uploaded to Photoshop, rasterized and composed in a digital collage manually, using the Auto-blend (stack) function to merge them together with seamless tones and colours. Images were not stacked automatically on top of each other, as done in previous research (see the Smart City project), where is the Auto-blend algorithm to decide which are the most distinctive features of each image to preserve, and which are the ones to discard in the final composite. Their disposition is instead decided by the researchers, in order to preserve each image visibility while composing a cohesive panorama
The flowchart (Figure A) shows some thematic trends. The top part of the graph focuses on locations and year-specific events: the first years focus on specific locations such as #Miami, #Kiribati #Florida, while in the most years the hashtags have a global connotation, like #savetheplanet, #savetheearth. This suggests that at the beginning of our dataset the topic of sea level rise was perceived as location-specific, whereas from 2019 onwards it developed into a worldwide issue.
The bottom part of the graph contains hashtags on content and content type (#art, #photo, #photooftheday), which we analysed in relation to users.
Figure A. Rankflow of the top 25 hashtags per year
For the analysis of the different locations, our departure point were the location data of descriptions in the top 100 posts in each year (visualized i.a. In this map). Some of the patterns that struck us as significant were:
Some annual variation can probably be attributed to natural and political events. For instance, Australia was mentioned more often in descriptions in the run up to the 2022 elections than in other years, while Hawaii was mentioned more often in 2014 in the context of hurricane Ana.
Locations in the “Global North” appeared to be overrepresented on the platform, with “Florida”, “Miami”, “Australia”, “Earth”, “Antarctica” and “Bangladesh” being the most mentioned locations.
The “Earth” location being so popular in turn pointed towards abstract, generalised conceptualization of the issue persisting on the platform (see also Instagram image analysis below).
Almost all posts that mention (locations in) the Netherlands emphasise Dutch examples of innovation and coastal engineering, which reaffirms the persistence, in discourse from/about the Netherlands, of the dry-feet myth/narrative. It appears that the myth is (at least) as much propagated through “admiration from abroad” as it is fuelled/reinforced from within.
After acquiring the image sets of the mentioned locations, we identified patterns within each set of images.
“Earth” images included a wide array of images, from scientific solutions to climate protests to moody depictions of the challenges to come.
In “Miami” images, there was a strong emphasis on the real estate sector, indicating a potential conceptualization of the issue as a future and strongly economic problem.
“Florida” images were also shown in connection to other issues typically championed by ‘left-wing/liberal’ political actors, as well as a relatively high emphasis on Florida’s natural territories and wildlife.
In “Bangladesh” images, the issue was very much one of the present day, displaying people’s negatively impacted livelihoods, including destroyed houses, flooded terrains, etc.
“Australia” images were more activist in nature than images with other mentioned locations, perhaps due to the political landscape, where for years government climate action was politically unattainable. Also striking was an emphasis on nearby pacific countries and the acute problems they faced, indicating a regional focus.
“Antarctica” images, quite unsurprisingly, showed mostly glaciers at risk of melting, accompanied by super-imposed textual explanations.
Figure B. Composite image related to the top mentioned locations
We noticed fluctuating trends in the analysis of the users, though we acknowledge that the lack of data from 2013 and 2014 may have affected our results. Although there is fluctuation, photographers constitute the most frequent category and their number is more or less stable throughout the years, up until 2020. We hypothesise that the Covid-19 pandemic impacted their ability to travel, resulting in photographers having less pictures than before.
We also noticed that most of these accounts are US-based, with some users from different countries (New Zealand, China, Colombia and a few European countries) starting from 2019.
The “Art and Architecture” category contains designers, ceramists, schools and universities of architecture, public art programs and collectives; we categorised as “Bottom Media” social media news pages such as the account @climateempathy, which posts climate change news and updates; “Governmental Organisation” contains not only international organisation such as the the United Nations, but also Nasa, the Climate Vulnerable Forum and local organisation such as the Greenest City Action Plan of the city of Vancouver; in “Non-Profit Organisation” there are international organisations such as Greenpeace and local ones such as the CLEO institute of Miami; “Photography” includes professional and amateurs photographers and collectives and “Traditional Media” represents accounts such as National Geographic and the New Yorker.
The “Other” category groups together start ups, business oriented pages and personal profiles. While the businesses and start-ups of our database were strongly related to the issue of sea-level rise, the only personal profile in our database - who was the most prolific author in 2021 and 2022 - did not relate to the issue at all, rather it is a clickbait account, which reposts whatever is relevant.
Figure C. Users categorisation
We looked at the images with the most engagement to understand how the issue of sea level rise has been framed and discussed on Instagram across time. Overall, we found a coherent narrative unfolding throughout the years: while in the initial years sea level rise appears to be a rather novel and circumscribed phenomenon, starting from 2020 the issue begins to be perceived as a worldwide problem, directing the production from simple reporting and observation towards an active search for solutions.
Upon a closer inspection, we found that each year seems to focus on a specific aspect of the issue, which we tried to illustrate through composite images and a brief description. In 2013, in what we identified as “The didactic year”, there was an attempt in understanding the issue of sea level rise. In the following years this develops into a photography-based narrative, which focuses on melting glaciers, the relationship between nature and human and later into attempts to aestheticize the problem and to find solutions to the issue. Finally, content published in later years - and especially in 2022 - seems to be mostly concerned with disastrous consequences of sea level rise and in finding eco-sustainable answers to the problem.
Figure D: composite images (sample of years)
Figure E: World maps presenting the locations of top 100 posts from 2014 to 2022.
Figure F: Evolutions of top 100 posts from 2013 to 2022 based on their geographical locations.
The content categories we coded to were: location, temporality, staging (speculative, conspiracy, etc.), solutions, and sources. The affordances were: audio (voice-over, music, etc), stitching (remixing videos together), dueting, green screen, and filters. For lack of time we did not extrapolate or analyse the use of stickers.
We found that the US states California and Florida, and major coastal cities London, New York, and Singapore were important places for TikTok videos about sea level rise. These predominantly sat within the Global North, positioning the geographical imaginary of sea level rise on TikTok as both urban and US-dominated. While there are various videos that refer to individual islands, these are dispersed across several different islands rather than any specific site(s). Here, videos about specific urban centres are relatively evenly split between basing their claims in some form of evidence or external resources and not. The former, for example, includes formal resources (i.e., videos posted by Zaobaosg - the most widely read Chinese language newspaper in Singapore) and informal resources (user generated documentation of flooding).
Figure G: TikTok video about what might happen to Florida as a result of global sea level rise.
Most of the videos (92 videos; 46.46% total) found in our coded dataset deal exclusively with future events, speculating on how they will occur and their possible impacts on large urban centres. Not only do they represent the most frequent category in the dataset, but they also accumulate around 70% of the total views of the analysed videos. Many are based on the display of cartographic simulations and videos generated from computer graphics to show what will happen to large urban centres over the next few decades “if nothing is done to stop the advance of climate change”. On the other hand, there is a milder and more general concern with the “future of humanity” if it continues down this path. Furthermore, only 16 of future-based videos actually refer to possible solutions to the rising sea levels issue — 7 of which refer to the same “solution”: a “giant ice machine” that will refreeze arctic water. That includes the most watched video in our dataset, posted by Waste-Ed, a profile labelled as “Russia state-controlled media”. This framing means audiences could be led towards interpreting sea level rise as something distant (from the now) which should only be addressed by future generations.
Contents dealing with climate change as a current concern and as something which effects are already being felt in several locations were also very prominent in the dataset (68 videos; 34.34% total). Clear examples of this are Tuvalu, Bangladesh and Puerto Rico, places impacted by floods attributed to climate change in recent years, mentioned 3, 2 and 2 times in videos identified as dealing with current issues. Videos in this category help bring our object of analysis and concern closer to the public, which can often interpret it as something distant and that should only be addressed by future generations — videos dealing with future issues were watched three times more than those dealing with the present. Even so, only 11 videos (16.17% of the category) identified in this temporal category propose or identify solutions to the problem, 2 of which are ironic and, as such, do not propose anything tangible.
Figure H: TikTok video that presents global sea level rise as a present issue, with a focus on Puerto Rico.
Besides that, we also found videos that evoke different present, past and speculative times and situations, linearly or not, to address the rise in sea levels, which we categorised as part of a “mixed temporality” (33 videos; 16.66% total). For instance, we found videos that call for the future expansion of present solutions undertaken by localities that are already dealing with the direct consequences of sea level rise increase. Present problems are depicted as being a direct consequence of past or continued government interference. It is interesting to see how users who undermine climate change fall into this category, since they have the habit of presenting images of the past and present side by side as a way to minimise their real impacts, thus concluding that the future shouldn't be a real concern.
We found that the past did not feature strongly within TikTok representations, garbeining only 3 videos (1.51%). All videos categorised here were published as part of a series by user @beastly2480 called “Landmasses that used to exist”. These posts, tagged with hashtags like #sealevelsrising, #sealevelrise and #waterrising, show how land masses — specifically, Beringia, Doggerland and Sundaland — were submerged in past centuries as a result of rising sea levels. Focused on historical trivia, these videos do not seem as an attempt to raise awareness of present-day issues related to climate change, differentiating them from those discussed in the previous category. In the end, we also had 2 videos (1% total) that addressed no temporality at all.
TikTok creators use audio and video editing to communicate about sea level rising in an authoritative way, often mirroring visual rhetoric and style of newscaster, with green screen and text-on-screen being among their favorite editing techniques. Text on screen is the most used technique, appearing in 70% of the videos in our coded dataset, and is generally used to provide a caption or extra information regarding the issue under discussion. While green screen is the second most used technique, used in nearly 30% of the videos, its views far outweigh all of the other techniques, suggesting that if a creator uses a green screen to discuss climate change, they have a good chance of accumulating substantial views. Replies are also a common feature, comprising 15% of the data set. They are typically used in combination with text on screen, as well as for debunking or explaining sea level rise with examples.
All three of these techniques emphasise information sharing and allow the creator to take an authoritative position—which lends then credibility as most are independent creators (that is, unattached from institutional affiliation). The videos are mostly unidirectional, even considering replies, where the creator lectures to their audience or replies to an audience question, which aligns with our finding that the videos tend to be informationally focused. TikTok features like stitching, dueting, and filters, which might be seen as more conversational (multi-directional) or playful, are barely used in this dataset.Audio - Although previous research had emphasised the use of popular songs to “assemble affective publics around climate change” (Hautea et al., 2021), our data turned out to mostly feature foreboding music associated with the sea levels rising - among which one can cite Amity’s Horrorville or some snippets of Blade Runner 2049 soundtrack. A search through such sounds show a general association between them and mystery, “creepypasta”, or even conspiratorial content. In general, the videos featuring those songs were very low on informational content and seemed focused on creating strong affective reactions in the viewer.
The 25 most popular videos showed characteristics consistent with the analysis above. The total number of views for these videos was 52,218,800, with the most viewed video played 14.3 million times. Overall, these videos concentrate on Global Northern and urban in terms of location information. For the temporality, 15 (60%) videos speculated future events, such as digital video effects of coastal regions and cities being attacked by huge waves, or digital maps changing as sea level rise. However, most of these speculations and informative videos were not information-based; only 3 provided sources: IPCC, Geoscience Australia (a government site), and a Coastal Risk Screening Tool. Only six videos contained solutions, but all edits and repetitions of the same video proposed a conceptual model of the giant ice machine as the solution to rising sea levels. Meanwhile, three-quarters of the videos only talked about the situations, imagined consequences, or irony of sea level rise.
Figure I: TikTok video about a “giant ice machine” that could refreeze the Arctic as a way to circumvent global sea level rise.
Focusing on how the narration was done, that is, in text or green screen. In most cases (18 videos), the user narrated the video in text layered on top of the video (for example, "Europe if sea levels increased by 100 meters" to explain the graphics in the background). The green screen was the second most used technique, appearing in 9 (36%) videos. Other video editing techniques (duetting, stitching, and filters) common to other topics were rarely used in sea level rise, probably because of the nature of this topic.
When looking into the actors, the top 3 most engaged accounts also emerged most frequently in the dataset: @getwasteed (a Russian State Controlled Media), @bobbymoore44, and @jazminereneex. Unlike Twitter, it is general users, instead of news agencies, experts, and professionals that were most active and gained visibility on TikTok.
That being said, TikTok users envision sea level rise as…
Present (affected coastlines farmland and some third world/global south countries) and speculative futures (cities);
Urban and in global north;
Faces on-screen presenting information to the viewer borrows visual rhetoric and style of newscaster. These often combined with green-screen and overlay text;
Audio is often voice over, with news channels typically using muzak. A few future focussed videos use film as cultural reference points, drawing on sci-fi.
Top 3 categories
2012: 1. Images of coastline and areas at risk; 2. Scientific graphs and 3. Flooded areas
2015: 1. Images of coastline and areas at risk; 2 Scientific graphs and 3. Glaciers ex aequo with floods.
2021: 1. Images of coastline and areas at risk; 2. Images and flyers with Alt text and 3. Maps
More dystopian images (e.g. imaginaries of New York under the sea) in 2015, perhaps related to the Paris COP?
Scientific graphs and charts present throughout in a way which is relatively stable, with a peak in 2015
Maps become more prominent in dataset from 2015
Glaciers and polar areas well represented in 2015 and 2021
Images of affected people/victims present but not dominant in any of the datasets
Cities represented particularly in 2015 dataset but not elsewhere.
Islands represented in 2012 datasets but not really visible in later datasets
Share of images with political statements expressed as Alt-text visible in 2015 and particularly important in 2021, e.g. statements of Greta Thunbergs and other climate activists.
Diversification of content in 2021: more ‘other’ than in previous years
Temporality: Future present in 2 ways: In 2015 with a few dystopian images of the future and via scientific graphs (present throughout the dataset)
These images are available here.
Findings after close-reading of the top 30 most retweeted posts 2012, 2015 and 2021:
The top retweets present news/facts from climate reports and climate scenarios, NASA sources are increasingly dominant.
The top retweets are written predominantly by scientific organizations, NGOs, and skilled enthusiasts located in the USA, Australia or international organizations with researchers stationed across the globe.
The number of retweets increased dramatically from 2012 to 2021. Top 2012: 11 retweets, top 2015: 83 retweets, and top 2021: 611 retweets.
#sealevelrise is the most used of the hashtags across the decade.
2012: the top retweets have no images, but are informative on #sealevelrise.
2012: the top retweets are by scientific users/NGOs like “Climate Central” and many by “NOAA Digital Coast”.
2015: The top retweets are from “NASA Climate” on melting glaciers on Greenland, the second are from Allan Margolin’s series “Climate Cartoon” that makes fun of climate deniers, and third comes NOAA again that were active also in 2012.
2021: Extremely active climate influencers/groups like “Clean Boss” (country unclear) that uses NASA sources, comes in first, and “Markus the Knight of the Darkness” (Australia) comes in third. The scientist Alexander Verbeek, Canada, comes in second.
We had to check the list of locations extracted by Mordechai to ensure they actually corresponded with locations. For example, ‘MT’ was identified as a frequently mentioned location in the 2015 dataset. However, this was found to be an artefact of Twitter culture of the time, where ‘MT’ meant ‘modified tweet’ (before the introduction of quote tweets). While the appearance of this term, and its disappearance in 2021, provides an insight into the changing vernacular of Twitter, it was removed from the data as it was clearly not intended to communicate location.
4CAT was more effective in retrieving a broader range of tweets associated with our query. However for the visuals it was interesting to note that for 2012 4CAT was able to retrieve 6 images whereas WDRA retrieved ~60 images. For gathering older visual materials WDRA may be more effective.
Akerlof, K., Covi, M. & Rohring, E. (2017). Communicating Sea Level Rise. Oxford Research Encyclopedia of Climate Science. Retrieved June 20, 2022, from https://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-417
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