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Political memes, as a subset of all the various sorts of memetic imagery, abound online, whether they support a particular politician or political ideal or rail against them.
While it may seem at first glance to be a mystery how one image becomes the basis for an ever-growing library of mutations on an image or theme, a 2018 study sought to explain the spread of political memes on a number of fronts.
Researchers Savvas Zannettou, Tristan Caulfield, Jeremy Blackburn, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, and Guillermo Suarez-Tangil teamed up to do a large-scale measurement study of the meme ecosystem, studying 160 million memes collected from 4 online communities – the /pol/ subsection of 4Chan, the /r/The_Donald subreddit, Twitter, and a recently made “free speech social network” called Gab.
Here’s what they found.
Algorithms sifted through the memes
In their methodology, the researchers took around 100 million memes from the 4 communities and also took an additional 600 to 700 million memes from Know Your Meme.
To sift through the data, they used a technique called perceptual hashing or pHashing to determine which images look the same or similar. An algorithm converted the images into a set of vectors described in numbers, with similar images having similar vector sets or pHashes.
The researchers said “the time to compare all the 74M images from Twitter (the largest dataset) against the medoids of all 12K annotated clusters” took “about 12 days” using a pair of NVIDIA Titan Xp graphics cards. In other words, their system studied about 73 images per second.
Researchers found over 100 million unique pHashes, with many looking similar to one another. To fine-tune the results, the researchers also used a clustering algorithm to group related memes by the communities they were from.
Context is king
The researchers also cross-referenced the memes to entries on Know Your Meme to better understand the context of the memes being shared.
According to the researchers, the study found 4chan’s /pol/ and the Gab social network were sharing more hateful and racist memes compared to mainstream communities. These included anti-Semitic or pro-Nazi memes.
Additionally, memes that at first glance were neutral – variants of the Pepe the Frog meme were a given example – were combined with other memes to “incite hate or influence public opinion on world events,” such as those regarding terrorist organizations or events like Brexit in the United Kingdom.
As such, racist memes appeared to be common on fringe parts of the online communities studied. At the same time, politics-related memes were also on mainstream communities, which brings the possibility that memes are being weaponized to either help or harm particular agendas, though the origins of the weaponization may be inscrutable.
Sharing hate efficiently
Researchers said the 4Chan effort had the “largest overall influence for racist and political memes”, but noted it was the least efficient, based on influence with respect to the amount of memes posted.
The most effective of the 4 sources of political memes was the r/The_Donald subreddit, which it said was “very successful” in sharing its memes to fringe and mainstream online communities.
The researchers added the r/The_Donald subreddit was not just the most active when it came to sharing political memes. It was also seen as the prime subreddit for posting racism and politcs-related memes.
Caveats and future research
The research did not appear to determine the actual initial source of the memes, but were instead studying the spread through their 4 chosen forms of online media.
As such, it’s also likely that these memes could have come from other places – from online chat channels to chat applications like Discord or Telegram – but were harnessed in full on one of the 4 channels for maximum effect.
The research, which was last updated in September 2018, breaks ground for trying to peer through the veil of memetic sharing. In future studies, the researchers said they would try to include video memes, as well as further study the content of posts using optical character recognition technologies to capture text and introduce crowdsourced labeling to speed up the research.
Meanwhile, the algorithm used in their study, which they said is openly available, might also be used to help stamp out the kinds of content it used to study, should social media companies decide to let it to help them.
Given the current climate of political meme-sharing these days within the US and beyond its borders, it may be prudent for some social media companies to think about finding ways to negate the hatred spread on their platforms. – Rappler.com