Please use this identifier to cite or link to this item:
https://hdl.handle.net/2445/192142
Title: | Visualizing and ranking the influence of users who post memes in social networks |
Author: | Pérez Onielfa, Carles |
Director/Tutor: | Escalera Guerrero, Sergio Casacuberta, Carles |
Keywords: | Teoria de grafs Anàlisi multivariable Programari Treballs de fi de grau Xarxes neuronals convolucionals Aprenentatge automàtic Xarxes socials en línia Mems Graph theory Multivariate analysis Computer software Convolutional neural networks Machine learning Bachelor's theses Online social networks |
Issue Date: | 13-Jun-2022 |
Abstract: | [en] Memes evolve and mutate through their diffusion in social media. They have the potential to propagate ideas and, by extension, products. Many studies have focused on memes, but none so far, to our knowledge, on the users who post them, their relationships, and the reach of their influence. In this project, we define a meme influence graph together with suitable metrics to visualize and quantify influence between users who post memes. Then, we describe a process to implement our definitions using a new approach to meme detection based on text- to-image area ratio and contrast. After applying our method to a set of users of the Instagram platform, we conclude that our metrics add information to already existing user characteristics and that our methodology can also be used to study the popularity of memes types among the users. |
Note: | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero i Carles Casacuberta |
URI: | https://hdl.handle.net/2445/192142 |
Appears in Collections: | Programari - Treballs de l'alumnat Treballs Finals de Grau (TFG) - Enginyeria Informàtica Treballs Finals de Grau (TFG) - Matemàtiques |
Files in This Item:
File | Description | Size | Format | |
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tfg_perez_onielfa_carles.pdf | Memòria | 11.51 MB | Adobe PDF | View/Open |
codi_font_perez_onielfa_carles.zip | Codi font | 74.3 kB | zip | View/Open |
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