Abstract
Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources –visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.
Highlights
Of memes critically determines the success of the message and its lifetime on the system
We choose the n most active users and the m memes that those users produced within that time interval. This bipartite network is encoded in an n ×m rectangular binary matrix, Mt, where t indicates the origin of the time window w and Mu,h = 1 if user u mentioned the hashtag h within the period spanning from t1 to t2 and zero otherwise. This procedure allows generating bipartite networks as time goes on by using a rolling-window scheme to evaluate the evolution of the system, such that a window at time t has a φw overlap with that at time t – w (φ = 0.5 in the results reported here; for φ closer to 1.0 results com at higher resolution, whereas φ = 0.0 implies non-overlapping windows)
Our analyses have unveiled the mechanisms underlying the evolution of an information ecosystem, revealing that there is a traceable pattern for an emerging collective attention event to culminate
Summary
Of memes (hashtags in Twitter, for instance) critically determines the success of the message (outreach) and its lifetime on the system (persistence). Even though the finding of a nested architecture in bipartite communication networks would be suggestive, online social networks provide us with time-resolved data, which makes it possible to trace back the origins of the nested pattern –at variance with all previous works: we have scarce evidence of how nestedness arises in nature, given the observational limitations and costs of fieldwork[16]. This is the reason why ecologists have focused rather on other aspects[17,18,19], letting aside the temporal dimension, i.e., the growth and evolution of the system and the emergence of nested patterns. But not least, our observation of an empirical modular-to-nested structural transition can shed light into the problem on the origin of nested architectures, which remains an elusive question
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