Parameter Fitting

After we validated the individual parts of the model, we now look at its ca-pability as a whole to produce the key activity figures we observe in the four community platforms we introduced in Chapter3. Therefore, we randomly se-lect a number of communities from each platform, and measure the number of users, as well as the number of threads and responses they created. These key factors are the basis for many other community metrics, such as posts per user and community growth.

We tune the model on each of the selected communities by fitting the input parametersjoin-rate,need-parameter,disengagement-parameterand thread-visibility. When the simulated community has reached the desired size, we measure the error in the key aspects (users, threads and responses) between the simulated community and the real world community. The model error is computed according to Equation 21, where U is the number of users or agents, T the number of threads created and R the number of responses; the indices obsand sim refer to the observed and simulated data. Because of the random element in the agent attributes, we execute every model parameter setting 10 times and record the average model error for that setting.

model-error = 1

This describes one iteration of the parameter fitting. In practice, we repeat this step three to four times for each community until we narrowed down the best combination of input parameters and achieved an acceptable model error that shows the data fitting capability of the model. If necessary, the error can be fur-ther reduced by increasing the number and granularity of the fitting iterations.

In Table 26, we report the key community activity features, the model param-eter settings and the resulting model error for the selected communities. At a first glance we see that our model fits the observed communities with low errors between 0.0342 and 0.1092. The fact that our model is able to produce these low errors across all the community types we investigate speaks for its generalisabil-ity and wide range of applicabilgeneralisabil-ity.

Upon closer inspection of the model parameters in Table 26 we see certain trends in among the communities. For example, the Stack Exchange communi-ties stand out as they tend to have a high user attraction factor (highjoin-rate), but at the same time also a low retainment of users as shown by the low disen-gagement-parameter. However, that is not a general trait of Q&A communities, as the selected SCN communities show the opposite with a lowerjoin-ratebut a higher retention of users. The Boards.ie and Wikipedia communities distin-guish themselves from the Q&A communities by having a lowerneed-parameter, which means that more people contribute by responding to existing threads or

Observed data Simulation parameters

Users Threads Responses Join-rate Need-parameter Disengagement-para. Thread-visibility Modelerror Boards.ie

127 9353 10137 195325 20 0.1 0.4 20 0.0993

251 2026 1208 14862 40 0.1 0.3 40 0.0562

255 1386 1294 13961 30 0.1 0.5 30 0.0463

1011 891 250 12927 20 0.03 0.6 100 0.1092

SCN

44 2649 4902 22866 40 0.2 0.5 20 0.0516

267 3111 6445 22776 65 0.25 0.55 10 0.0479

323 6399 15886 44354 45 0.35 0.35 30 0.0389

404 328 560 1868 30 0.3 0.5 80 0.0565

Stack Exchange

22 2753 3624 9220 90 0.35 0.15 70 0.0351

48 1774 2540 3443 80 0.6 0.1 50 0.0586

62 16327 17931 30942 70 0.47 0.06 35 0.0773

129 2242 3349 7554 60 0.35 0.15 45 0.0475

Wikipedia

175 369 144 1573 50 0.1 0.6 30 0.0556

223 750 376 4651 45 0.1 0.33 110 0.0424

307 2041 675 10124 80 0.08 0.15 90 0.0342

324 4256 1028 25666 80 0.04 0.3 10 0.0495

Table 26: The results of the parameter fitting shows that our model simulates the ob-served community features closely for all four community platforms.

editing existing articles in the case of Wikipedia. This can also be seen in the raw numbers of threads and responses in the observed data column of the table.

In summary, the model fits the observed characteristics of the selected com-munities well, and the results indicate that certain traits are particular to each community type or platform. The differences are caused by the various particu-larities of the community platforms, such as the user interface, the participation incentive and the general community environments. In future work, a

compara-tive examination of a large number of communities from different platforms will provide more support for identifying the different community traits.

6.4 summary

In the beginning of this chapter, we proposed simulation as a way to further study the dynamics of user behaviour and the effects on the survivability and success of the online community, beyond what machine learning on recorded data can provide. We asked: Can we encode the basic elements of user be-haviour in different online communities in one computational model (Research Question 10)? To answer this question, we introduced an agent-based model to simulate user interaction behaviour in online communities. This model repre-sents online users as agents with different activity profiles who participate in the community in order to either seek satisfaction for their needs (e.g. of informa-tion or support) or provide satisfacinforma-tion for others’ needs. The validainforma-tion of the model’s ability to accurately reproduce user activity of real communities shows that it is feasible to capture online user behaviour of different community types in one computational model. With that, we can confirm Research Question10.

To further define the model, we asked: What are the core elements of user be-haviour that the different community types have in common (Research Question 11)? We designed a model that consists of the following three parts: First, the agent attributes need, engagement and disengagement-factordefine the activity profile of the individual agents. Second, the agent interaction rules for creating new threads or responding to existing ones control how the individual agents in-teract within the community. Third, the model input parameters are macro param-eters that allow us to tune the agent attributes and interaction rules to simulate the characteristics of specific communities. We showed that the model accurately reproduces various aspects of activity in the different community types, which supports the validity of the proposed model towards simulating the following core elements of user behaviour in online communities:

1. Users join the community based on its current activity level