What makes an image go viral in a revolt? What’s the origin of a ‘meme’ in this context and how do they propagate? How do citizen empowerment and global solidarity bonds relate?
This study tries to explore these questions, based on the most shared photographs on Twitter (tagged with #OccupyGezi) during the Turkish Uprising in May-June 2013.
We collected every tweet with the #OccupyGezi hashtag since May 31st,2013@14 hours GMT until June 26th,2013 @ 21 hours GMT (around 6 million tweets), and ran a process through them, to extract the most referenced images, taking the first six of them, sorting by number of tweets with the image embedded.
With this information, we build a network for each image, taking into account native and non-native retweets and also tried to extract the 'via' of some tweets.
The result is the visualization you see for each image.
Of course, we tried to build a story, and contextualize each and every image on the study. If you click on each image, you'll go to the original tweet, with the exception of tweets that have been deleted.
Please note that we are not claiming these are the most viral images of the Turkish Revolt. They are just the most tweeted with the hashtag #OccupyGezi during the selected dates (on the 'Viral Notes' of each image)
There is no single answer to the question "How can an image go viral?" The dynamic visualizations of this research shows that virality is achieved through very different diffusion models. In some cases, the origin of the viral has been a "retweet" by an influencer, while in others, is based on distributed networks with multiple active nodes. This is the case of Image 2 (riot vehicles shooting high-pressure streams of water to a woman ), with ten viral hubs. The least number of them (only two) appears in the propagation of the image 5 on the demonstration outside the Turkish public television . They are @fevri_sosyolog (an user with 54,113 followers), who starts the diffusion, and @TheRedHack, Turkish hacker group who is the main responsible for the virality, with 336,553 followers and about 3.000 retweets.
Virality dynamics also vary in speed and half-life. In the case of image 5, the picture what went viral most quickly in the research, half of the tweets were published in only 13 minutes (viral half-life). By contrast, the image which spread more gradually was picture 4 (CNN Turkey vs . CNN World), which took 1 hour and 38 minutes to reach half the total tweets, followed by image 3 ( the global map of the hashtag # direngezi , 1h 12m), and image 2 (over 45 minutes). The second fastest was Image 1 on demonstrations in early June, ( 26 minutes) followed by image 6, the Twitter bird wearing a gas mask displayed in a stencil (37 minutes) . In all cases, influencers boosted propagation speed .
The role of influencers. Accounts with a greater number of followers have a great importance in virality processes .These profiles can be linked to activism ("global revolution"), as @ YourAnonNews ( 1,163,544 followers at the moment) or @ @ AnonOpsLegion (1,126,342 followers) , or be local influencers as Turkish rock singer @ AylinAsLIM ( 659633 followers) virality relevant in image 6, or the famous cartoonist of the magazine " Penguen " (penguin), @erdilyasaroglu ( 1,194,404 followers at this time ), whom RT of a tweet of " Occupy Wall Street" accelerated the virality of the pictures. The most outstanding case is the intervention of @TheRedHacker, who created a real "explosion" of tweets.
And “poor” accounts. There are cases in which the large viral life of an image is based , however , on the existence of a large number of nodes that hold diffusion for longer. This happens, for example, in the Image 2. In the same way, the virality of an image arises occasionally by the tweet from an account without an excessive number of followers , as the map of the hashtag # DirenGezi , launched by @ justinwedes, an activist with " only " 3,856 followers, but very well connected.
The importance of the image. The picture opening the research, a powerful image that reflects the uprising of the crowd ( large demonstration in Istanbul approaching the police line), is also what presents the most potent viral pattern: with the largest number of tweets of the study ( 5009), a remarkable speed (half of the tweets are produced in only 25 minutes ) and the highest peak viral (11.2 tweets per second ). The photograph showing two monitors with CNN Turkey aring a documentary about the life of penguins while CNN World was broadcasting the protests in Istanbul, is another example of the influence of the image. Similar photographs had been spread in social networks days ago but with a cooking show . The picture of penguins multiplied notoriety, and grows into meme, because the complaint becomes grotesque and ridiculous.
The text that accompanies the image. The text can elevate the status of an image, transforming an event into a story. The tweets frame the image, and increase the reach of the scene shown, as: “An image of a real revolution “ or "The whole world is watching"
Networks vs . Media. The involvement of mainstream media in the virality of the images studied is nonexistent. The fires spread outside of them, this is original content and messages are propagated without the intervention of the press.
Common Narrative. Visibility of the crowd ( in contrast with the "isolation" of dissident ) , peaceful resistance, police violence, complaints to the media and “tribute” to new channels are key moments in all the new revolutions
What can we learn about the power of networks from the way viral images are spreading
Autonomy to report. The silence of the media about the protests and police actions to contain them , fired twitter use to tell what was happening in the streets. The data reflects the intense activity of this network: up to two million tweets with the hashtags most present in the "revolt Turkish " (#direngezi , #occupygezi and #geziparki) were published on the day of heaviest police violence (between 31 May and 1 June). Particularly significant is the language used in most of these tweets: Turkish in 80% of cases. The 90 % of geolocated tweets were from within the country. With more than 36 million citizens with Internet access, 16.6 % are users of twitter. Twitter complemented the role of other social media like Facebook , Tumblr or streaming portals .
Auto organization based on social networks. The resistance on the street evolved forms of protest less immediate and in more depth, such as neighbourhood assemblies ( as in Spainish 15M ) which are still celebrating months later. Broadcast networks and generated trust (even through seemingly automatic actions like sharing a picture) are crucial for the consolidation of these self-organizing systems .
The power of multi-hubs networks. While networks generated in Turkish protests are more hierarchical than in other cases , the study of the viral images highlights the importance of networks that combine multiple small nodes with other influential.
The creation of a speech. The communication via social networks is not unconnected communicative actions. Instead, it creates a narrative essential to erode establishment and empower citizens .
Diffusion speed: Number of tweets that are published per second. Potential/Estimated Reach: Estimated number of the potential Twitter accounts reached by the image. Total tweets: Number of Tweets evolved in the viral process Viral Time: Lifetime of the viral process Hubs: Nodes in the network with a high number of connections (high-degree nodes) Viral Hubs: Hubs with a high number of RTs (more than 10 retweets) Viral half-life: Time to reach half of the final total tweets propagated. Diffusion speed peak:Largest number of tweets per second
Chronology of #OccupyGezi
Data Collection, Analysis and Visualization by Outliers Collective
Text by Yolanda QuintanaPress Release in spanish / Nota de prensa en español