How the online social networks are used: dialogues-based structure ofMySpace

Abstract

Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user's psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf's distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting 'weak-ties' hypothesis are found to follow the laws recently conjectured for online games. Longrange temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.

Co-Evolutionary Mechanisms of Emotional Bursts in Online Social Dynamics and Networks

“…A detailed analysis of the network structure, the role that the types of messages exchanged among users play in building the network, as well as the arousal-related resilience, have been recently studied in [19,29,30]. Unlike Blogs and conventional online social networks [16], where communications mostly occur within a community, in the online chat networks, the exchange of messages almost inevitably involves the nodes within the central core. Consequently, the maximum-flow spanning tree with an almost linear structure is obtained, see Figure 1d.…”

“…Within this approach in the analysis of online social dynamics data, a variety of social networks were observed in relation with different online activities of users [12,15,16,27,30,[52][53][54][55][56]. Here, we use the accepted methods to construct and analyse the networks from the empirical data from Blogs and Chats; we demonstrate that, although in both systems no a priori associations among users exists and the networks grow starting from scratch, the networks that eventually emerge in these processes belong to two entirely different classes of social structures.…”

“…Recently, intensive research based on these empirical data, seen as examples of complex dynamical systems in the physics laboratory, has been performed: this research contributed to quantitative study of social phenomena on Blogs [10,11], Diggs [12], Forums [13], online games [14,15], online social networks MySpace [16], Facebook [17], Twitter [18], online chats [19] and other online communication systems. By using different machine-learning methods of text analysis (a recent review of methods can be found in [20]), one can infer contents that are communicated in the text messages exchanged between users.…”

“…Depending on the interaction mode among users, a specific type of network architecture may appear [10,[14][15][16][26][27][28][29][30]; most of the emergent networks exhibit prevailing characteristics of a social structure;…”

Dynamics of Users Activity on Web-Blogs

“…Various aspects of the subject have been of broad interest recently [3][4][5]. Some of the regularities presented here are now known among researchers (in particular the relationship describing the number of entries to the mailing lists, or comments posted on blogs) on the rank of list member (its order in the list of the users that are most frequently writing there).…”