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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
Data sources: Datacite
DBLP
Article . 2023
Data sources: DBLP
DBLP
Article . 2021
Data sources: DBLP
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Competition Dynamics in the Meme Ecosystem

Authors: Trenton W. Ford; Rachel Krohn; Tim Weninger;

Competition Dynamics in the Meme Ecosystem

Abstract

Creating and sharing memes is a common modality of online social interaction. Because of the prevalence of memes, an abundance of research focuses on understanding how memes are shared and perceived within their online social environment and in what ways they differ from other modalities of online communication. In the present work, we present findings that suggest a relationship between how memes are introduced, shared, and diminish in online social systems and the behavior of biological species within an ecosystem. Framed by this perspective, we borrow ecological methodologies and concepts like resource availability, speciation, and competition to study human attention, creativity, and sharing dynamics (respectively) over a large collection of memes shared on Reddit. Specifically, we find that the population of memes has scaled almost exactly with the total amount of content created over the past decade. We find a consequence of limited human attention in the face of a growing number of memes is that the diversity of memes has decreased at the community level, albeit slightly, in the same period. A further consequence is that the average life span of memes has decreased dramatically, which is further evidence of an increase in competition and a decrease in a meme species’ primary resource: human attention. From this work, we have found reasonable preliminary support for linking memes to species and, thus, meme research to ecological research. We believe that future research should work to strengthen this relationship and transfer more methodologies to meme and information research from ecology.

Country
United States
Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, J.4, H.0, H.0; J.4, Computer Science - Social and Information Networks

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Top 10%
Top 10%
Green