Neutral theory for competing attention in social networks

Plata, Carlos A.; Pigani, Emanuele; Azaele, Sandro; Calleja-Solanas, Violeta; Palazzi, María J.; Solé-Ribalta, Albert; Borge-Holthoefer, Javier; Meloni, Sandro; Suweis, Samir

doi:10.1103/physrevresearch.3.013070

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…In particular, the analogies between the systems here considered and ecological systems have been of interest for researchers and some modeling approaches move from an ecological perspective. For example, the analogy between the diffusion of online social media content and ecology has been investigated in [30][31][32][33]. The number of retweets of an hashtag may be interpreted as the offspring and one may think that the number of retweets depends on the attention a given hashtag is capable to gain.…”

Section: Introductionmentioning

confidence: 99%

Upscaling human activity data: A statistical ecology approach

et al. 2021

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Big data require new techniques to handle the information they come with. Here we consider four datasets (email communication, Twitter posts, Wikipedia articles and Gutenberg books) and propose a novel statistical framework to predict global statistics from random samples. More precisely, we infer the number of senders, hashtags and words of the whole dataset and how their abundances (i.e. the popularity of a hashtag) change through scales from a small sample of sent emails per sender, posts per hashtag and word occurrences. Our approach is grounded on statistical ecology as we map inference of human activities into the unseen species problem in biodiversity. Our findings may have applications to resource management in emails, collective attention monitoring in Twitter and language learning process in word databases.

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Section: Introductionmentioning

confidence: 99%

Upscaling human activity data: A statistical ecology approach

et al. 2021

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“…Hierarchical networks are found ubiquitously across many areas of physics, as well as in social interaction clusters, biological systems, and medicine [1][2][3][4][5][6]. While the full universality of scale-free or power-law behavior in the underlying connectivity structure is still under debate [7,8], many real-world networks clearly exhibit strong heterogeneities.…”

Section: Introductionmentioning

confidence: 99%

Global hierarchy vs. local structure: spurious self-feedback in scale-free networks

Merger,

Reinartz,

Wessel

et al. 2021

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Networks with fat-tailed degree distributions are omnipresent across many scientific disciplines. Such systems are characterized by so-called hubs, specific nodes with high numbers of connections to other nodes. By this property, they are expected to be key to the collective network behavior, e.g., in Ising models on such complex topologies. This applies in particular to the transition into a globally ordered network state, which thereby proceeds in a hierarchical fashion, and with a non-trivial local structure. Standard mean-field theory of Ising models on scale-free networks underrates the presence of the hubs, while nevertheless providing remarkably reliable estimates for the onset of global order. Here, we expose that a spurious self-feedback effect, inherent to mean-field theory, underlies this apparent paradox. More specifically, we demonstrate that higher order interaction effects precisely cancel the self-feedback on the hubs, and we expose the importance of hubs for the distinct onset of local versus global order in the network. Due to the generic nature of our arguments, we expect the mechanism that we uncover for the archetypal case of Ising networks of the Barabási-Albert type to be also relevant for other systems with a strongly hierarchical underlying network structure.

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An ecological approach to structural flexibility in online communication systems

et al. 2021

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Human cognitive abilities are limited resources. Today, in the age of cheap information—cheap to produce, to manipulate, to disseminate—this cognitive bottleneck translates into hypercompetition for rewarding outcomes among actors. These incentives push actors to mutualistically interact with specific memes, seeking the virality of their messages. In turn, memes’ chances to persist and spread are subject to changes in the communication environment. In spite of all this complexity, here we show that the underlying architecture of empirical actor-meme information ecosystems evolves into recurring emergent patterns. We then propose an ecology-inspired modelling framework, bringing to light the precise mechanisms causing the observed flexible structural reorganisation. The model predicts—and the data confirm—that users’ struggle for visibility induces a re-equilibration of the network’s mesoscale towards self-similar nested arrangements. Our final microscale insights suggest that flexibility at the structural level is not mirrored at the dynamical one.

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Neutral theory for competing attention in social networks

Cited by 5 publications

References 34 publications

Upscaling human activity data: A statistical ecology approach

Upscaling human activity data: A statistical ecology approach

Global hierarchy vs. local structure: spurious self-feedback in scale-free networks

An ecological approach to structural flexibility in online communication systems

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