Dank or not? Analyzing and predicting the popularity of memes on Reddit.

COVID-19 Content-based analysis Image analysis Machine learning Memes Popularity prediction Sentiment analysis Social media Visual humor

Journal

Applied network science
ISSN: 2364-8228
Titre abrégé: Appl Netw Sci
Pays: Switzerland
ID NLM: 101732938

Informations de publication

Date de publication:
2021
Historique:
received: 13 10 2020
accepted: 02 02 2021
entrez: 15 3 2021
pubmed: 16 3 2021
medline: 16 3 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33718590
doi: 10.1007/s41109-021-00358-7
pii: 358
pmc: PMC7939928
doi:

Types de publication

Journal Article

Langues

eng

Pagination

21

Informations de copyright

© The Author(s) 2021.

Déclaration de conflit d'intérêts

Competing interestsThe authors declare that they have no competing interests.

Références

JAMA. 2016 Dec 13;316(22):2402-2410
pubmed: 27898976
Sci Rep. 2012;2:335
pubmed: 22461971
Phys Rev Lett. 2014 Jan 31;112(4):048701
pubmed: 24580496
Sci Rep. 2014 Sep 26;4:6477
pubmed: 25257730
Front Neurorobot. 2013 Dec 04;7:21
pubmed: 24409142
Insights Imaging. 2018 Aug;9(4):611-629
pubmed: 29934920

Auteurs

Kate Barnes (K)

Aquincum Institute of Technology, Budapest, Hungary.
Colorado College, Colorado Springs, USA.

Tiernon Riesenmy (T)

Aquincum Institute of Technology, Budapest, Hungary.
University of Kansas, Lawrence, USA.

Minh Duc Trinh (MD)

Aquincum Institute of Technology, Budapest, Hungary.
Haverford College, Haverford, USA.

Eli Lleshi (E)

Aquincum Institute of Technology, Budapest, Hungary.
Tufts University, Medford, USA.

Nóra Balogh (N)

Aquincum Institute of Technology, Budapest, Hungary.
Dmlab Ltd., Budapest, Hungary.

Roland Molontay (R)

Aquincum Institute of Technology, Budapest, Hungary.
MTA-BME Stochastics Research Group, Budapest, Hungary.
Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary.

Classifications MeSH