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Dank or not? Analyzing and predicting the popularity of memes on Reddit
Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions we...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939928/ https://www.ncbi.nlm.nih.gov/pubmed/33718590 http://dx.doi.org/10.1007/s41109-021-00358-7 |
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author | Barnes, Kate Riesenmy, Tiernon Trinh, Minh Duc Lleshi, Eli Balogh, Nóra Molontay, Roland |
author_facet | Barnes, Kate Riesenmy, Tiernon Trinh, Minh Duc Lleshi, Eli Balogh, Nóra Molontay, Roland |
author_sort | Barnes, Kate |
collection | PubMed |
description | Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other. |
format | Online Article Text |
id | pubmed-7939928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-79399282021-03-09 Dank or not? Analyzing and predicting the popularity of memes on Reddit Barnes, Kate Riesenmy, Tiernon Trinh, Minh Duc Lleshi, Eli Balogh, Nóra Molontay, Roland Appl Netw Sci Research Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other. Springer International Publishing 2021-03-09 2021 /pmc/articles/PMC7939928/ /pubmed/33718590 http://dx.doi.org/10.1007/s41109-021-00358-7 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Barnes, Kate Riesenmy, Tiernon Trinh, Minh Duc Lleshi, Eli Balogh, Nóra Molontay, Roland Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title | Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title_full | Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title_fullStr | Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title_full_unstemmed | Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title_short | Dank or not? Analyzing and predicting the popularity of memes on Reddit |
title_sort | dank or not? analyzing and predicting the popularity of memes on reddit |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939928/ https://www.ncbi.nlm.nih.gov/pubmed/33718590 http://dx.doi.org/10.1007/s41109-021-00358-7 |
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