9524143564
Meme learn
Introduction
Meme (noun) - An image, video, piece of text, etc, typically hu.moro.us in naturę, that is .co.pi.ed and spread rapidly by Internet users, often with slight variations. -
In our work, we acąuired and structured image memes from various so.ur.ces, effectively .creating a first ever (to the best of our knowledge) meme network. Having done that, we performed analyses and created a generative model.
Embeddings
10
Graph emhndding
Embeddings visualization with UMAP decomposition (color = meme templote)
10
-10
20-
10-
5
C- *rUv -b
ic
Comploto embedding
Meme generation
82.5% 66.5%
accuracy
We created a classification model, which selects the best matching image tern piąte fo r a given text. On top of this mod el, we bui lt a m eme gene rato r, whose sa m ple output is presented below.
Meme recommendation
Our simple recommendation system delivers the most similar memes to the one provided. It is based on cosine similarity and takes into cosideration multiple aspects of memes: image, text and users' soda I interactions.
JEŚLI NAUKA 0 DANYCH TO DANOLOCIA
TO CZY NAUKA 0 ZIEMI TO aEMOŁOGIA
^c=K-
t *
* MA fi *A« 7> U\U
•A,
AUTHOR
l!n-«» w.illnho.t KainnMK-jhiUir^irMI-
- COMMENTS
USERS
' )
Fig 1. Meme as .co.mp.lex data source
Meme Generation
Meme Recommendation
Model
"Zrobił zadanie, me zabrał do szkoły"
;ags
embeddings
Tag mbeddings
Piotr
Bielak
L
Politechnika
Wrocławska
\ /
Michał
Bieroński
Michał
jóźwiak
Fig 3, Applications
Finał thoughts
• Memes' short texts were enough to perform pretty interesting tasks.
• Based on embedding dustering, well defined communities do not seem to emerge from I mgfli p's user base.
• Generative model works much better for Polish memes - higher nu.mb.er of parts of speech seems to h el p.
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