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We process our analysis as follows: first, we look into the communication patterns of instant online discussions, to find out about the average response time of users and its possible dependence on the topics discussed.

This shall allow us to identify differences between instantaneous chatting communities and other forms of slower, persistent communication.

This type of interaction requires much higher user activity in comparison to persistent communication e.g. Further, it is more spontaneous, often leading to emotionally-rich communication between involved peers.

Consequently, instant communication should require specific tools and models for analysis, that are capable of covering these predominant features.

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Online communication can be seen as a large-scale social experiment that constantly provides us with data about user activities and interactions.This success indicates that our modeling framework can be used to test further hypothesis about emotional interaction in online communities.A) Schema of the evolution of a conversation in an IRC channel.The activity is expressed as the time interval τ between two consecutive posts of the same user.Inset: Probability distribution of the user activity for individual IRC channels. C) Scaled probability distribution of the time interval ωTo characterize these activity patterns, we analyzed the waiting-time, or inter-activity time distribution P(τ), where τ refers to the time interval between two consecutive posts of the same user in the same channel and ask about the average response time. We should note, however, that the tail is better fitted by a log-normal distribution (KS = 0.136) rather than an exponential (KS = 0.190) or a Weibull (KS = 0.188) one (again using the maximum likelihood methodology described by Clauset et al.) as shown in Fig. Here, KS stands for the Kolmogorov-Smirnov statistical test; the smaller this number, the better the fit.Our dataset (described in detail in the data section), consists of 20 IRC channels covering topics as diverse as music, sports, casuals chats, business, politics, or computer related issues – which is important to ensure that there is no topical bias involved in our analysis.For each channel, we have consecutive daily recordings of the open discussion over a period of 42 days, which amounts to more than 2.5 million posts in total generated by more than 20.000 different users.Consequently, time series analyses have already revealed remarkable temporal activity patterns, e.g. Such patterns allow conclusions how humans organize their time and give different priorities to their communication tasks.One particular quantity to describe these patterns is the distribution P(τ) of the waiting time τ that elapses before a particular user answers e.g. Different studies have confirmed the power-law nature of this distribution, P(τ) ~ τ allows to derive α by comparing two different rates, the average rate λ of messages arriving and the average rate µ of processing messages. if messages arrive faster than they can be processed, α = 3/2 was found, which is compatible with most empirical findings and simulation models. if messages can be processed upon arrival, α = 5/2 was found together with an exponential correction term.Nowadays, IRC channels are still one of the most used platforms for collective real-time online communication and are used for various purposes, e.g.organization of open-source project development, Internet activism, dating, etc.

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  1. May 10, 2012. For a more detailed discussion about scaling relations, and memory in time series please refer to. In conclusion, our analysis of user activities have revealed a universal dynamics in online chatting communities which is moreover similar to other human activities. This regards a the temporal activity of.

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