Recent years have witnessed the explosion of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing contents, and sharing interests between friends who may never meet before. As more and more people become the active users of online social networks, one may ponder questions such as: (1) Do OSNs indeed improve our sociability? (2) To what extent can we expand our offline social spectrum in OSNs? (3) Can we identify some interesting user behaviors in OSNs? Our work in this paper just aims to answer these interesting questions. To this end, we pay a revisit to the well-known Dunbar's number in online social networks. Our main research contributions are as follows. First, to our best knowledge, our work is the first one that systematically validates the existence of the online Dunbar's number in the range of [200,300]. To reach this, we combine using local-structure analysis and user-interaction analysis for extensive real-world OSNs. Second, we divide OSNs users into two categories: rational and aggressive, and find that rational users intend to develop close and reciprocated relationships, whereas aggressive users have no consistent behaviors. Third, we build a simple model to capture the constraints of time and cognition that affect the evolution of online social networks. Finally, we show the potential use of our findings in viral marketing and privacy management in online social networks.
By investigating the relationship between neocortex size and group size in primates, Dunbar @cite_21 predicted the number of group size in human beings was 150, which was notable as Dunbar's number. According to him, human beings can only maintain a small fraction of relationships within the circle of Dunbar's number, and other relationships beyond that circle are not reciprocated or personalized. Dunbar's number targets on real-world social networks when first put forward. However, recent works in online social networks have displayed similar interesting observations @cite_16 @cite_15 . pointed out that time spent using social media, including online social sites, was not associated with larger offline networks @cite_19 . Potential time and cognitive constraints were also considered in their work. Other work of online social networks related to Dunbar's number will be further discussed in Section .
@cite_19 @cite_15 @cite_21 @cite_16
1928223220 1965056875 2168092462 2282408813
Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100–200 stable relationships. Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior. The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found “offline” (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar’s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80 ) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals – used in other studies as the unique estimator of tie strength – has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate predictions Online Social Networks are amongst the most important platforms for maintaining social relationships online, supporting content generation and exchange between users. They are therefore natural candidate to be the basis of future humancentric networks and data exchange systems, in addition to novel forms of Internet services exploiting the properties of human social relationships. Understanding the structural properties of OSN and how they are influenced by human behaviour is thus fundamental to design such human-centred systems. In this paper we analyse a real Twitter data set to investigate whether well known structures of human social networks identified in "offline" environments can also be identified in the social networks maintained by users on Twitter. According to the well known model proposed by Dunbar, offline social networks are formed of circles of relationships having different social characteristics (e.g., intimacy, contact frequency and size). These circles can be directly ascribed to cognitive constraints of human brain, that impose limits on the number of social relationships maintainable at different levels of emotional closeness. Our results indicate that a similar structure can also be found in the Twitter users' social networks. This suggests that the structure of social networks also in online environments are controlled by the same cognitive properties of human brain that operate offline. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure.