Temporal pattern classification of internet meme propagation: A hybrid machine learning approach

William Little; Pengcheng Xiao; William Little; Pengcheng Xiao

doi:10.3934/era.2025238

Electronic Research Archive

2025, Volume 33, Issue 9: 5323-5346. doi: 10.3934/era.2025238

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Research article

Temporal pattern classification of internet meme propagation: A hybrid machine learning approach

William Little ^1,2,
Pengcheng Xiao ^{1
,
,}

1.
Department of Mathematics, Kennesaw State University, 1100 South Marietta Pkwy SE, Marietta, GA 30060, USA
2.
Department of Computer Science, Kennesaw State University, 1100 South Marietta Pkwy SE, Marietta, GA 30060, USA

Received: 10 March 2025 Revised: 22 August 2025 Accepted: 02 September 2025 Published: 12 September 2025

Quantitative analysis of internet memes—digital cultural phenomena that propagate through online networks—remains an understudied domain in computational social science. Building upon established research that modeled meme popularity through ordinary differential equations, this study presents a novel machine learning approach to classify and predict meme popularity trajectories. Previous work identified four distinct post-peak patterns: smooth decay, oscillatory decay, plateau, and sustained growth. We significantly expanded the empirical foundation by constructing a comprehensive dataset of 2000+ memes, leveraging Google Trends time-series data. Our methodological framework employed a two-stage machine learning pipeline: first, implementing k-means clustering ($ k = 4 $) for unsupervised pattern discovery, followed by support vector classification for supervised learning. This approach enabled both validation of previously identified trajectory patterns and development of a predictive model for meme popularity evolution. Additionally, we conducted a systematic analysis of the relationship between meme taxonomic categories (e.g., catchphrases, viral videos) and their temporal popularity patterns. Our findings contribute to the emerging field of computational memetics and offer insights into the quantitative dynamics of online cultural transmission. The resulting classification model demonstrates robust predictive capabilities, with implications for understanding viral content dynamics in digital ecosystems.
- internet memes,
- machine learning,
- time-series analysis,
- K-means clustering,
- support vector classification,
- cultural propagation,
- digital content analysis,
- pattern recognition
Citation: William Little, Pengcheng Xiao. Temporal pattern classification of internet meme propagation: A hybrid machine learning approach[J]. Electronic Research Archive, 2025, 33(9): 5323-5346. doi: 10.3934/era.2025238

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Abstract

Quantitative analysis of internet memes—digital cultural phenomena that propagate through online networks—remains an understudied domain in computational social science. Building upon established research that modeled meme popularity through ordinary differential equations, this study presents a novel machine learning approach to classify and predict meme popularity trajectories. Previous work identified four distinct post-peak patterns: smooth decay, oscillatory decay, plateau, and sustained growth. We significantly expanded the empirical foundation by constructing a comprehensive dataset of 2000+ memes, leveraging Google Trends time-series data. Our methodological framework employed a two-stage machine learning pipeline: first, implementing k-means clustering ($ k = 4 $) for unsupervised pattern discovery, followed by support vector classification for supervised learning. This approach enabled both validation of previously identified trajectory patterns and development of a predictive model for meme popularity evolution. Additionally, we conducted a systematic analysis of the relationship between meme taxonomic categories (e.g., catchphrases, viral videos) and their temporal popularity patterns. Our findings contribute to the emerging field of computational memetics and offer insights into the quantitative dynamics of online cultural transmission. The resulting classification model demonstrates robust predictive capabilities, with implications for understanding viral content dynamics in digital ecosystems.

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