Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes

Kate Barnes; Péter Juhász; Marcell Nagy; Roland Molontay

doi:10.1007/s13278-024-01272-3

Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes

Kate Barnes, Péter Juhász, Marcell Nagy, Roland Molontay^*

^*Corresponding author af dette arbejde

Institut for Matematik

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Abstract

This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in “innovator” memes posted during the early stages of a topic’s prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.

Originalsprog	Engelsk
Artikelnummer	119
Tidsskrift	Social Network Analysis and Mining
Vol/bind	14
Nummer	1
ISSN	1869-5450
DOI	https://doi.org/10.1007/s13278-024-01272-3
Status	Udgivet - dec. 2024

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10.1007/s13278-024-01272-3Licens: CC BY

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title = "Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes",

abstract = "This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in “innovator” memes posted during the early stages of a topic{\textquoteright}s prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.",

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AU - Barnes, Kate

AU - Juhász, Péter

AU - Nagy, Marcell

AU - Molontay, Roland

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/12

Y1 - 2024/12

N2 - This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in “innovator” memes posted during the early stages of a topic’s prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.

AB - This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in “innovator” memes posted during the early stages of a topic’s prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.

KW - BERTopic

KW - Latent Dirichlet Allocation

KW - Machine learning

KW - Memes

KW - New York Times

KW - Popularity prediction

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Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes

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