Characterizing community structures on social media over time: a graph learning approach

In an age where information is more accessible than ever, it’s easy to assume that people are becoming more informed and open-minded. In spite of that, people are increasingly finding themselves in echo chambers, surrounded by like-minded individuals and exposed mainly to content that reinforces the...

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Autores principales: Zolezzi, María Victoria, Albanese, Federico, Feuerstein, Esteban
Formato: Objeto de conferencia Resumen
Lenguaje:Inglés
Publicado: 2023
Materias:
Ciencias Informáticas
social media
interactions
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/165778
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SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-165778
record_format dspace
spelling I19-R120-10915-1657782024-05-08T20:04:10Z http://sedici.unlp.edu.ar/handle/10915/165778 Characterizing community structures on social media over time: a graph learning approach Zolezzi, María Victoria Albanese, Federico Feuerstein, Esteban 2023-09 2023 2024-05-08T16:27:41Z en Ciencias Informáticas social media interactions In an age where information is more accessible than ever, it’s easy to assume that people are becoming more informed and open-minded. In spite of that, people are increasingly finding themselves in echo chambers, surrounded by like-minded individuals and exposed mainly to content that reinforces their existing beliefs. There are, however, social media users that break with that pattern by changing the group of users they interact with over time. In this study, we analyze the dynamics of interactions between users on Twitter and Reddit over extended periods, with the aim of identifying changes in community structures. We leverage the data available through these platforms’ APIs to construct user interaction graphs and use several methods to classify users into communities, including SBM, Infomap and Louvain, to classify users into communities. Additionally, we use NLP techniques such as Community Pooling, BERTopic and Perspective [8], as well as graph algorithms, to characterize different user profiles in online debates. Our research analyzes how social media communities and their users evolve over time, with implications for understanding online discourse and facilitating healthy interactions on these platforms. As a first approach, we analyzed three months of Donald Trump’s tweets, finding clear signs of polarization. Regarding the user flow between communities, we found that most of the users who changed communities twice went back to their original one (∼ 96%). Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Resumen http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 45-48
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
social media
interactions
spellingShingle Ciencias Informáticas
social media
interactions
Zolezzi, María Victoria
Albanese, Federico
Feuerstein, Esteban
Characterizing community structures on social media over time: a graph learning approach
topic_facet Ciencias Informáticas
social media
interactions
description In an age where information is more accessible than ever, it’s easy to assume that people are becoming more informed and open-minded. In spite of that, people are increasingly finding themselves in echo chambers, surrounded by like-minded individuals and exposed mainly to content that reinforces their existing beliefs. There are, however, social media users that break with that pattern by changing the group of users they interact with over time. In this study, we analyze the dynamics of interactions between users on Twitter and Reddit over extended periods, with the aim of identifying changes in community structures. We leverage the data available through these platforms’ APIs to construct user interaction graphs and use several methods to classify users into communities, including SBM, Infomap and Louvain, to classify users into communities. Additionally, we use NLP techniques such as Community Pooling, BERTopic and Perspective [8], as well as graph algorithms, to characterize different user profiles in online debates. Our research analyzes how social media communities and their users evolve over time, with implications for understanding online discourse and facilitating healthy interactions on these platforms. As a first approach, we analyzed three months of Donald Trump’s tweets, finding clear signs of polarization. Regarding the user flow between communities, we found that most of the users who changed communities twice went back to their original one (∼ 96%).
format Objeto de conferencia
Resumen
author Zolezzi, María Victoria
Albanese, Federico
Feuerstein, Esteban
author_facet Zolezzi, María Victoria
Albanese, Federico
Feuerstein, Esteban
author_sort Zolezzi, María Victoria
title Characterizing community structures on social media over time: a graph learning approach
title_short Characterizing community structures on social media over time: a graph learning approach
title_full Characterizing community structures on social media over time: a graph learning approach
title_fullStr Characterizing community structures on social media over time: a graph learning approach
title_full_unstemmed Characterizing community structures on social media over time: a graph learning approach
title_sort characterizing community structures on social media over time: a graph learning approach
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/165778
work_keys_str_mv AT zolezzimariavictoria characterizingcommunitystructuresonsocialmediaovertimeagraphlearningapproach
AT albanesefederico characterizingcommunitystructuresonsocialmediaovertimeagraphlearningapproach
AT feuersteinesteban characterizingcommunitystructuresonsocialmediaovertimeagraphlearningapproach
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