The growth, spread, and mutation of internet phenomena: A study of memes
Open accessAbstract
An internet memes is defined as “an image, video, piece of text, etc., typically humorous in nature, that is copied and spread rapidly by Internet users, often with slight variations” (Oxford Living Dictionary, 2018). Such units of information are spread rapidly by internet users, often for a relatively short period of time. The modeling of this spread is of interest for a variety of reasons, especially since large amounts of information are spread via this method. In this study, we attempt to evaluate some previously-implemented models of memetic diffusion and improve upon them when possible. In particular, we implement an SIR-based system of stochastic differential equations.
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The prior study [6] manually accrued a dataset of 100 elements using keywords from KnowYourMeme.com and Google Trends time-series.
“The prior study [6] manually accrued a dataset of 100 elements, using keywords from a variety of meme pages on KnowYourMeme.com.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
Section 2.1 (Data collection) says the authors selected memes using KnowYourMeme.com and collected Google Trends data using the memes’ exact names. It states that 66 memes were selected, not 100; the number 100 refers to Google Trends’ normalized maximum popularity value. (LLM, conf=0.90)
Experimental — accuracy not yet measuredPrevious work identified four distinct post-peak patterns: smooth decay, oscillatory decay, plateau, and sustained growth.
“Previous work identified four distinct post-peak patterns: smooth decay, oscillatory decay, plateau, and sustained growth.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
Section 2.1 (page not provided) classifies memes into four patterns corresponding to smooth decay, oscillatory or spikey decay, leveling off, and long-term growth. But it describes long-term growth as a continuous rise throughout the dataset, not a post-peak pattern. (LLM, conf=0.87)
Experimental — accuracy not yet measuredA meme's date-of-origin, minus two years, was factored into each Pytrends request, allowing for a more accurate start date of the time-series data.
“Differing from the original paper [6], a meme's date-of-origin, minus two years for caution, was factored into each Pytrends request, allowing for a more accurate start date of the time-series data.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
The finding of highest silhouette at k=4 reinforces the number of predefined popularity patterns, 4, described in a previous study.
“This finding reinforces the number of predefined popularity patterns, 4, described in a previous study [6].”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
A cluster size of four was chosen based on intuition from [6] and silhouette analysis.
“A cluster size of four was chosen based on intuition from [6], as there are four popularity patterns expected to take shape, in addition to the results provided by a silhouette analysis.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
The machine learning framework offers greater scalability for large datasets and flexibility for handling noisy time-series data compared to previous work.
“While we built upon the theoretical foundation established by [6], our machine learning framework offers greater scalability for large datasets and flexibility for handling noisy time-series data.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
The silhouette analysis provided statistical validation for the four-pattern framework originally proposed by [6].
“The silhouette analysis provided statistical validation for the four-pattern framework originally proposed by [6], while our expanded dataset and machine learning approach offered new insights into pattern characteristics and distribution.”
From Temporal pattern classification of internet meme propagation: A hybrid machine learning approach (2025) · cites this paper as [6]
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Research works printed but not linked: 6
- Wiggins B, Bowers G. Memes as genre: A structurational analysis of the memescape. New Media Soc 2015;17(11):1886-996.
- Oxford living dictionary. Oxford University Press; 2018.
- Massad E, Rocha A, Coutinho F, Lopez F. Modelling the spread of memes: how inovations are transmitted from brain to brain. Appl Math Sci 2013;7:2295-306.
- Wang L, Wood B. An epidemiological approach to model the viral propagation of memes. Appl Math Model 2011;35(11):5442-7.
- S Zannettou S, Caulfield T, Blackburn J, De Cristofaro E, Sirivianos M, Stringhini G, et al. On the origins of memes by means of fringe web communities. 2018, arXiv:1805.12512. · arXiv
- Allen L. A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis. Infect Dis Model 2017;2(2):128-42.
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Temporal pattern classification of internet meme propagation: A hybrid machine learning approach
Little, W., Department of Mathematics, K. S. U., Xiao, P., & Department of Computer Science, K. S. U. (2025) · Electronic Research Archive · doi:10.3934/era.2025238
Self-citation
- Authors
- Lonnberg, A., Xiao, P., & Wolfinger, K.
- Affiliations
- Indiana University Indianapolis (US)
- Kennesaw State University (US)
- University of Colorado Boulder (US)
- Corresponding author
- Xiao, Pengcheng
- Venue
- Results in Applied Mathematics
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 6
- Pages
- 100092
- ISSN
- 2590-0374 (ISSN-L)
- OpenAlex
- W2998941119
- Cited by
- 26
- References
- 0
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 2 · 2023: 4 · 2022: 8 · 2021: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Results in Applied Mathematics (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Kennesaw State University
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- internet memes, information dissemination, SIR model, stochastic differential equations, epidemic modeling, viral content
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