Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques
The homophily phenomenon in social networks causes users to interact primarily with others who share their interests and cultural backgrounds, leading to the formation of "echo chambers" [1–3]. The notion of cultural diversity among users and communities becomes relevant in this context....
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| Formato: | Objeto de conferencia Resumen |
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2023
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/165745 |
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I19-R120-10915-1657452024-05-08T20:04:23Z http://sedici.unlp.edu.ar/handle/10915/165745 Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques Oppenheim, Abi Albanese, Federico Feuerstein, Esteban 2023-09 2023 2024-05-08T13:22:32Z en Ciencias Informáticas Machine learning Social Media Reddit Community Embedding Diversity The homophily phenomenon in social networks causes users to interact primarily with others who share their interests and cultural backgrounds, leading to the formation of "echo chambers" [1–3]. The notion of cultural diversity among users and communities becomes relevant in this context. While previous studies have investigated diversity in interaction graphs, to the best of our knowledge, none have explored the degree of diversity based on community embedding, which has been proven effective in measuring the positioning of communities in various social dimensions [4–7]. Building on the work of [7], we propose characterizing and measuring diversity through an innovative algorithm based on community embedding. We propose a novel algorithm based on community embedding to characterize and measure diversity. Our approach builds upon prior work on diversity in social media and involves iteratively updating values for the diversity of communities and individual users. To demonstrate the effectiveness of our algorithm, we conduct a case study analyzing over over 800 million posts in 9 million discussion subreddits of different ethnic groups on Reddit. Next, we generated embeddings for each community using community2vec [8] and developed algorithms to quantify cultural diversity based on these embeddings. 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 66-67 |
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Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
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SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Machine learning Social Media Community Embedding Diversity |
| spellingShingle |
Ciencias Informáticas Machine learning Social Media Community Embedding Diversity Oppenheim, Abi Albanese, Federico Feuerstein, Esteban Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| topic_facet |
Ciencias Informáticas Machine learning Social Media Community Embedding Diversity |
| description |
The homophily phenomenon in social networks causes users to interact primarily with others who share their interests and cultural backgrounds, leading to the formation of "echo chambers" [1–3].
The notion of cultural diversity among users and communities becomes relevant in this context. While previous studies have investigated diversity in interaction graphs, to the best of our knowledge, none have explored the degree of diversity based on community embedding, which has been proven effective in measuring the positioning of communities in various social dimensions [4–7].
Building on the work of [7], we propose characterizing and measuring diversity through an innovative algorithm based on community embedding. We propose a novel algorithm based on community embedding to characterize and measure diversity. Our approach builds upon prior work on diversity in social media and involves iteratively updating values for the diversity of communities and individual users.
To demonstrate the effectiveness of our algorithm, we conduct a case study analyzing over over 800 million posts in 9 million discussion subreddits of different ethnic groups on Reddit. Next, we generated embeddings for each community using community2vec [8] and developed algorithms to quantify cultural diversity based on these embeddings. |
| format |
Objeto de conferencia Resumen |
| author |
Oppenheim, Abi Albanese, Federico Feuerstein, Esteban |
| author_facet |
Oppenheim, Abi Albanese, Federico Feuerstein, Esteban |
| author_sort |
Oppenheim, Abi |
| title |
Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| title_short |
Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| title_full |
Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| title_fullStr |
Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| title_full_unstemmed |
Quantifying cultural diversity in social networks: a community embedding approach : Defining diversity measures through graph and machine learning techniques |
| title_sort |
quantifying cultural diversity in social networks: a community embedding approach : defining diversity measures through graph and machine learning techniques |
| publishDate |
2023 |
| url |
http://sedici.unlp.edu.ar/handle/10915/165745 |
| work_keys_str_mv |
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