Entropy and complexity unveil the landscape of memes evolution

Abstract

On the Internet, information circulates fast and widely, and the form of content adapts to comply with users' cognitive abilities. Memes are an emerging aspect of the internet system of signification, and their visual schemes evolve by adapting to a heterogeneous context. A fundamental question is whether they present culturally and temporally transcendent characteristics in their organizing principles. In this work, we study the evolution of 2 million visual memes published on Reddit over ten years, from 2011 to 2020, in terms of their statistical complexity and entropy. A combination of a deep neural network and a clustering algorithm is used to group memes according to the underlying templates. The grouping of memes is the cornerstone to trace the growth curve of these objects. We observe an exponential growth of the number of new created templates with a doubling time of approximately 6 months, and find that long-lasting templates are associated with strong early adoption. Notably, the creation of new memes is accompanied with an increased visual complexity of memes content, in a continuous effort to represent social trends and attitudes, that parallels a trend observed also in painting art.

Figure 1

Evolution rate of internet memes. The number of new templates per each month is reported as a function of time. The growth rate is estimated through an exponential fit, with a doubling time of $T\sim 6$ months.

Figure 2

Mutation rate of memes. Left column: distribution of instances’ inter-arrival times ($\mathrm{\Delta }t$); central column: lifetime distribution of memes; right column: exemplary growth curve of meme adoption. Each row correspond to a typical size of meme cluster. Interestingly, the larger the cluster size the longer the lifetime. However, long lasting memes template shows skewed distributions for the inter-arrival times ($\mathrm{\Delta }t$). This highlights the tendency of long lasting memes to undergo strong early adoption.

Figure 3

Trajectories in entropy-complexity plane for the four Reddit communities. All, except r/AdviceAnimals present an evolution towards higher values of complexity that resembles that of painting arts. Each dot is the average value of entropy and complexity for each year. Visually complex templates could be linked to the tendency of using memes to continuously represent social trends and attitudes, supporting the hypothesis that memes are part of the emerging internet meta-language.

Figure 4

Dataset used in this work. Each curve shows the number of posts per month that were downloaded from Reddit. Each posts correspond to an image. The total amount of downloaded memes is about 2 million.

Figure 5

Panel (a): joint distribution of average-pairwise distance $\overline{S}$ and cluster size. Each dot corresponds to a cluster, i.e. a template for memes. Red dots represent “noisy clusters” identified by HDBSCAN that are outliers with respect to the cluster size and ${\overline{S}}_k$ distributions, shown respectively in panels (b) and (c). Noisy clusters are removed from the analysis.