This document describes a study on tracking the propagation of memes across fringe web communities. The researchers developed a pipeline to extract image hashes from communities like 4chan, Reddit, and Gab, cluster similar images into memes, annotate the memes, and analyze how they spread over time. They used Hawkes processes to quantify the influence one community has on another and found that /pol/ was most influential in spreading racist memes, while The_Donald was most efficient normalized by the number of memes posted. The study provided insights into how different communities share memes and aimed to characterize communities through the memes they propagate.

1. On the Origins of Memes by Means of Fringe
Web Communities
Savvas Zannettou, Tristan Caulfield, Jeremy Blackburn, Emiliano De Cristofaro,
Michael Sirivianos, Gianluca Stringhini, Guillermo Suarez-Tangil

2. WARNING
IMAGERY IN THIS TALK IS
UNCENSORED AND MIGHT BE
OFFENSIVE

5. Memes are fun!

6. Not always though…

7. Hateful/Racist Memes

8. Memes in politics

9. Memes in politics

10. Memes in politics
Memes have become a popular,
and seemingly effective, method to
transmit ideology.
Memes have been weaponized

11. But what do
we really
know about
memes?
• How can we track meme propagation
across Web communities?
• How can we characterize variants of
the same meme?
• Can we characterize Web
communities through their memes?
• Can we measure the influence of Web
communities with respect to memes
they share?

12. Memes processing pipeline
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

13. Let’s see our data sources…
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

14. Know Your Meme (KYM)
• Crowdsourced encyclopedia for Memes
• Provides useful metadata
• E.g., origin, descriptive tags, description, examples, image galleries
• Built custom crawler
• Obtained data for 15K KYM entries
• Download every image per entry (706K)

15. Datasets
# of posts 1.4B 1.0B 48M 12M 15K
# of posts with
images
242M 62M 13M 955K 15K
# of Images 114M 40M 4M 235K 706K

16. Perceptual hashing extraction
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

17. Perceptual hashing (pHash)
• Generates a hash for each image
• Visually similar images have minor differences in
their hashes
• Reduces dimensionality of the images
• Run the pHash algorithm for
• All images from KYM (706K)
• All images from Twitter, Reddit, /pol/, and Gab
(159.5M)

18. Creating clusters of images/memes
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

19. Pairwise comparisons and clustering
• Calculated all pairwise comparisons between
all pHashes from /pol/, The_Donald, and Gab
• Used TensorFlow and GPUs to speed-up the
process
• Hamming distance
• Performed clustering using:
• DBSCAN algorithm

20. Example clusters
Nut Button Meme Goofy’s Time Meme

21. Annotating clusters
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

22. Annotating clusters
• Calculated medoid of each cluster
• “Representative” image in cluster
• Compared all medoids with all KYM images
• We have a hit if the Hamming distance is <= pre-
defined threshold
• Assign the representative label according to:
• Number of hits
• Average distance between all hits
• Performed small-scale evaluation of
annotations

23. Finding all memes and analyzing final dataset
3. Clustering
1. pHash Extraction
2. pHash-based Pairwise
Distance Calculation
pHashes of some or all Web
communities' images
Clusters of images
5. Cluster Annotation
Pairwise Comparisons
of pHashes
annotated
images
6. Association of
Images to Clusters
Annotated
Clusters
pHashes of
annotated images
pHashes
(all Web Communities)
7. Analysis and
Influence Estimation
Occurrences of Memes in
all Web Communities
4. Screenshot
Classifier
annotated
images
pHashes of non-screenshot
annotated images
Know Your
Meme
Generic
Annotation
Sites
Meme Annotation Sites
Generic
Web
Communities
4chan Twitter Reddit Gab
Web Communities posting Memes
images

24. Top memes per Web community

25. Studying specific groups of memes
• Focus on racist and political memes
• Use KYM tags to find relevant memes
• “politics,” “2016 us presidential election,” “trump,” and
“clinton” tags
• “racism,” “racist,” or “antisemitism” tags
• Obtain 117 racist memes and 556 political memes
from KYM dataset

26. How are memes shared over time?
Political Memes Racist Memes

27. How are memes shared over time?
Political Memes Racist Memes
2nd US
presidential
debate

28. How are memes shared over time?
Political Memes Racist Memes
2016 US
elections
2nd US
presidential
debate

29. How memes are shared over time?
Political Memes Racist Memes
2016 US
elections
Gab activity
increase
2017
2nd US
presidential
debate

30. How are memes shared over time?
Political Memes Racist Memes
2016 US
elections
Gab activity
increase
2017
/pol/
constant
share
2nd US
presidential
debate

31. How are memes shared over time?
Political Memes Racist Memes
2016 US
elections
Gab activity
increase
2017
/pol/
constant
share
Gab activity
increase in
2017
2nd US
presidential
debate

32. How to quantify the influence?
• Hawkes processes
• Assume K processes
• Each with a rate of events (i.e., posting of a meme),
called the background rate
• An event can cause impulse responses in other
processes
• Increases the rates of other processes for a period of
time
• Enables us to assess root cause of events

33. Hawkes processes example
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34. Hawkes processes example
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41. For our
purposes…
• Hawkes model with 5 processes
• One for each platform/community (/pol/,
The_Donald, Reddit, Twitter, Gab)
• Distinct model for each cluster; fit each
model with Gibbs sampling
• Calculate the influence and efficiency of each
community

42. Communities’ influence (racist memes)
/pol/ is most
influential in terms
of spreading racist
memes

43. Communities’ efficiency (racist memes)
If we look at the
influence normalized
to the number of
memes posted, the
The_Donald is most
efficient in terms of
disseminating memes

44. Summary
• Proposed meme processing pipeline
• Code and datasets available on Github
(https://github.com/memespaper/memes_pipeline)
• Important differences between the memes posted on
Web communities
• Quantified influence among Web communities

45. Project LegacyAcknowledgments