Agent Attributes

The model input-parameters we described in the previous section are macro parameters that are tuned on each community. As shown in Figure 9 on page 157, the agents themselves are characterised by the model-internal attributes of engagement, disengagement-factor and need. These attributes are not tuned directly but are drawn from the respective distributions that are created by the macro model input parameters.

The long-tailed trends that we observe in the collective behaviour of people (some of which we discuss here in this thesis) show that community members are not all the same. For example, some produce a lot of content, whereas the majority of the members become inactive after just a few posts. In order for agents to exhibit differences in their activity patterns similar to what we observe in the real world, we equip them with a number of attributes that define the behaviour of the individual agent and distinguish it from other agents. We represent each agent with three attributes that capture their engagement levels and their drive towards either creating new threads or responding to existing threads during their turn.

Engagement

The attribute engagement works as an activity reservoir. The idea behind this is that the moreengagement is available to an agent the higher is its probability to activate during a given time step, and hence the more active will the agent be over the course of the community’s life time. In our model, the engagement reservoir of each new agent is filled to 100%, and posting actions consume this resource (see Figure12on page165).

The main reason why each agent starts with a full engagement reservoir is to enable the agents to become active at least once after joining the community.

This requirement originates in the fact that community platforms are usually oblivious to, and do not record, lurker activity. These are users who consume content without ever creating an account and contributing to the community.

Figure 10: The distribution of posts per user shows a clear long-tailed shape. This ex-ample is taken from the SCN forum 328 (anonymised) and represents users who created between 1 and 50 posts.

For our model that means that lurkers are not explicitly represented either, and every user known to the system has at least posted once.

Disengagement-factor

It has been observed that human behaviour collectively follows a long-tailed, often exponential, trend [CSN09; VC14], which we confirmed for user activity and expertise in Q&A forums [AH14]. Figure 10 clearly shows the long-tailed distribution of the number of posts that each SAP user wrote in community 328 (anonymised) before they became inactive and either left the community completely or became lurkers. The particular relation between active users and lurkers has also been investigated in a 2006 study, where Nielsen described a 90-9-1 distribution of user activity in online communities: 90% of users do not contribute to the community (i.e. lurkers), 9% contribute in a small amount, and 1% contribute actively and produce most of the content [Nie06].

The disengagement-factor determines how fast the individual agent con-sumes its engagement reservoir. As per the observed long-tailed trend, we use a

natural exponential function (f(x) = e^x, with the Euler constant e) to distribute thedisengagement-factoramong the agents as described in Equation15:

disengagement-factor∼ 1 − e⁻

 x

disengagement-parameter



, (15)

where 0 6 x < 1 is a uniform random number, and disengagement-parameter is the model input parameter that controls the slope of the distribution, tuned on each community (introduced in Section 6.2.1). The negative exponent in Equation 15 generates a distribution with an exponential shape but within the limits of [0, 1). The value is then subtracted from 1 (the maximum engage-ment) in order to invert the slope of the distribution so that the majority of the agents will disengage quickly after just a few posts (i.e. they have a high disengagement-factor), whereas a small portion of the agents will disengage with a much lower rate and remain active for longer periods of time (stemming from a lowdisengagement-factor).

Need

The role of the need attribute is to control when an agent creates a new thread rather than responding to an existing thread. Its name comes from the idea that users turn to the community because they have a need for something the community can provide. In the case of Q&A communities, the users may have a need for information and will turn to the community to ask for help with a certain topic. Not dissimilar to this, in Life & Health communities, the users may have a need for social connections and emotional support, which they hope to fulfil by attracting replies from other people. On the other hand, the need of members of Knowledge Creation communities is expressed by their demand to create valuable content about their topic of interest, where the community provides users who can help to improve the quality of the written knowledge article.

These different interpretations of need translate to the creation of new original posts or articles for which the creators hope to attract responses in the form of answers, replies or content edits. In our model, and in the terminology of this

Figure 11: Similar to the distribution of posts per user, the distribution of new threads created per user also shows an long-tailed trend. This example shows users who created between 1 and 20 threads in the SCN community 328.

thesis, original posts and articles are representative for newly created threads that are open to responses from other agents. Based on our observation of the long-tail shaped distribution of need in the form of threads created per user (see Figure11, and analogue to the distribution of thedisengagement-factoramong the agents (Figure 10 and Equation 15), the agents’ need will be distributed based on the natural exponential function (f(x) = e^x, with the Euler constant e) according to Equation16:

need∼ e⁻

 x

need-parameter



, (16)

where 0 6 x < 1 is a uniformly distributed random number, and the need-expon-entis one of the macro model input parameters that we introduced in Section 6.2.1. The distribution ofneedamong the agents will also be within the limits of [0, 1), where the majority of agents will have a small need and tend to respond to existing threads compared with a small number of agents with great need and the tendency to exclusively create new threads.