Social resilience in online communities

Abstract

We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, and Myspace, to study how social networks decline. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit (c/b) ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. Further, our resilience analysis shows that the topology of a social network alone cannot explain its success of failure. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.

Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

“…Garcia et al [129] studied a series of SNSs to understand what helps them survive, and when they become vulnerable. Their sample included Friendster (http://www.…”

“…Garcia et al [129] argued that the long-term health of any SNS depends on a cost-benefit analysis. If the costs of being a member outweigh the benefits for a long enough period of time, a person is likely to leave.…”

“…Equally, if a solid proportion of people had a large number of friends, it would be less vulnerable. The fraction of the network with a threshold number of friends is called by Garcia et al [129] the k-core distribution, and they argue that the combination of a low k-core distribution together with a negative cost-benefit distribution increases the likelihood of failure. In the case of Friendster, some argue that ill-conceived technical changes increased the costs, which meant that its low k-core distribution made it vulnerable to collapse.…”

How exposure to different opinions impacts the life cycle of social media

“…Wu et al (2013) studied the relationship between user arrival and departure on social networks with respect to network topology, and elaborated on the important role of active friends in social networks in departure decisions. In another study based on network topology, Garcia et al (2013) focused on understanding the resilience of social networks and how friends' departures impact focal users. Giliette (2011) provided a more popular view on some of the technical, managerial, financial, and social issues that could lead to social media outlet failure.…”

Emotions and Activity Profiles of Influential Users in Product Reviews Communities

“…To identify social influence, traditional measures focused on the concept of centrality [12], often measuring it as degree or betweenness centrality [13]. Recent works have shown that coreness centrality [14,15] outperforms degree and betweenness centrality in detecting influentials both data-driven simulations [16] leading to applications to political movements [17,18], scientific rumors [19,20], gender inequality in Wikipedia [21], and cascades of users leaving a social network [22].…”