Chat room hard

22 Feb

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.

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.

Different from other types of online communication, such as blogs or fora where entries are posted at a given time (decided by the writer), IRC chats are instantaneous in real time, i.e.

users read while the post is written and can react immediately.

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Prior to the very common social networking sites of today, IRC channels provided a safe and independent way for users to share and discuss information outside traditional media.In this paper, we provide both: a new way of analysing data from online chats, and a model of interacting agents to reproduce the stylized facts of our analysis.In addition to the activity patterns of users, we also analyse and model their emotional expressions that trigger the interactions of users in online chats.The latter regime, also denoted as the “highly attentive regime”, could be verified empirically so far only by using data about donations.So, it is an interesting question to analyze other forms of online communication to see whether there is evidence for the second regime.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.How do users behave in online chatrooms, where they instantaneously read and write posts?We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication.In a second step, we look more closely into the content of the discussions and how they depend on the emotions expressed by users.Remarkably, we find that most users are very persistent in expressing their positive or negative emotions - which is not expected given the variety of topics and the user anonymity.