The structure of online social networks mirrors those in the offline world

Abstract

a b s t r a c tWe use data on frequencies of bi-directional posts to define edges (or relationships) in two Facebook datasets and a Twitter dataset and use these to create ego-centric social networks. We explore the internal structure of these networks to determine whether they have the same kind of layered structure as has been found in offline face-to-face networks (which have a distinctively scaled structure with successively inclusive layers at 5, 15, 50 and 150 alters). The two Facebook datasets are best described by a four-layer structure and the Twitter dataset by a five-layer structure. The absolute sizes of these layers and the mean frequencies of contact with alters within each layer match very closely the observed values from offline networks. In addition, all three datasets reveal the existence of an innermost network layer at ∼1.5 alters. Our analyses thus confirm the existence of the layered structure of ego-centric social networks with a very much larger sample (in total, >185,000 egos) than those previously used to describe them, as well as identifying the existence of an additional network layer whose existence was only hypothesised in offline social networks. In addition, our analyses indicate that online communities have very similar structural characteristics to offline face-to-face networks.Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Sizes of Permanent Campsite Communities Reflect Constraints on Natural Human Communities

“…These groupings correspond to communities that are conventionally labelled as bands (or overnight camp groups), communities (or clans), mega-bands, and tribes (Lehmann, Lee, and Dunbar 2014), respectively. Data from Facebook, Twitter, e-mail, and massive multiplayer online games suggest that this same grouping pattern and scaling ratio occurs even in online environments (Arnabaldi et al 2015;Dunbar et al 2015;Fuchs et al 2014;Gonçalves, Perra, Vespignani 2011;Haerter, Jamtveit, and Mathiesen 2012;Pollet, Roberts, and Dunbar 2011). These structural features of communities turn out to mirror the internal structure of personal social networks (Hill and Dunbar 2003;Sutcliffe et al 2012;Zhou et al 2005) and are similar to the layering pattern found in animal species that live in complex societies (Hill, Bentley, and Dunbar 2008).…”

“…It would be particularly illuminating to observe the conditional frequencies with which individuals joined and left communities so as to test the hypothesis that joining rates are higher on the downside of each number and leaving rates higher on the upside. The hierarchically inclusive layered structuring of both natural communities and personal social networks is a consequence of a combination of cognitive limits on the number of relationships that can be maintained at a given emotional intensity and the time available to maintain such relationships (Miritello et al 2013;Dunbar 2011a, 2011b;Saramäki et al 2014;Sutcliffe et al 2012;Roberts et al 2014), with very characteristic frequencies of interaction for each layer that are, in fact, mirrored even in the online world (Arnabaldi et al 2015;Dunbar et al 2015). We might expect the camp communities to be organized in the same way.…”

“…In other words, do these layers in some sense represent "sweet spots" in organization size that are preferred because they work better socially? To establish whether the same kinds of layerings occur within q2 communities, we would need data of a very different kind (namely, data on interaction frequencies; e.g., Dunbar et al 2015). Similarly, we cannot address the question of whether sites of particular size are socially optimal, because we do not have data on either community longevity or residents' satisfaction ratings.…”

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

“…In order to make full use of users' online and offline information, some researchers begin to quantize the interplay between online social network and offline social network and investigate network dynamics from the view of mobile traffic data [155][156][157][158]. Specifically, the online and offline social networks are, respectively, constructed based on online interest based and location based social network among mobile users.…”

High-Speed Railways’ Impact on the Connection of Cities along the Line Based on the Analysis of Micro-Blog Data

“…Dunbar et al found that the online social network structure can reflect the social network structure in the real world, and they have very similar characteristics [9]. There are many literatures on social network data analysis at home and abroad, such as Twitter and social economy characteristics [10], geographic support and major disaster management [11], geography research based on micro-blog network information [3].…”