Mathematical modelling of the statistics of communication in social networks
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Chat rooms are of enormous interest to social network researchers as they are one of the most
interactive internet areas. To understand the behaviour of users in a chat room, there have been
studies on the analysis of the Response Waiting Time (RWT) based on traditional approaches of
aggregating the network contacts. However, real social networks are dynamic and properties such
as RWT change over time. Unfortunately, the traditional approach focuses only on static network
and neglecting the temporal variation in RWT which may have lead to misrepresentation of the true
nature of RWT.
In order to determine the true nature of RWT, we analyse and compare the RWT of three
online chat room logs (Walford, IRC and T-REX) putting into consideration the dynamic nature of
RWT. Our research shows that the distribution of the RWT exhibits multi-scaling behaviour, which
signi cantly a ects the current views on the nature of RWT. This is a shift from simple power-law
distribution to a more complex pattern. The previous study on users RWT between pairs of people
claims that the RWT has a power-law distribution with an exponent of 1. However, our research
shows that multi-scaling behaviour and the exponent has a wider range of values which depend on
the environment and time of day. The di erent exponents observed on di erent time scales suggest
that the time context or environment has a signi cant in
uence on users RWT. Furthermore, using
the chat characterise, we predicted the factors which could minimize response waiting time and
improving the friendship connection during online chat sessions.
We apply our ndings to design an algorithm for chat thread detection. Here, we proposed two
variations of cluster algorithm. The rst algorithm involves the traditional approach while in the
second one, the temporal variations in RWT was taken into consideration to capture the dynamic
nature of a text stream.
An advantage of our proposed method over the previous models is that previous models have
involved highly computationally intensive methods and often lead to deterioration in the accuracy
of the result whereas our proposed approach uses a simple and e ective sequential thread detection
method, which is less computationally intensive
Authors
Ikoro, Gibson OkechukwuCollections
- Theses [3593]