In this last section of our paper we want to address the applications of our work, some of which we already mentioned in the analysis, in more detail.

One potential application is using SNA to identify and assess candidates for admin status. Remember that our analysis showed that high centrality corresponds well with the granting of admin status. Obviously, the requirements which the community expects of its admins, are reflected in a high centrality.

Currently, candidates can be identified and nominated by any community member. TO DO

We suggest that, in addition to the nomination by community members, candidates should be identified by means of Betweenness Centrality. Moreover, the centrality of a candidate nominated by another user can be used as an additional criterion for the assessment of that candidate.

Another application is related to quality control. As we mentioned earlier, Wikinews’ aim is to attract more “citizen reporters”, which wasn’t achieved yet. While quality of content is controlled by the system of collaborative editing itself, a measurement instrument for the amount of original reporting currently does not exist. Our approach to look for star-structures (see Chapter 6) should indicate when there is a shift towards more users contributing late-breaking news directly and therefore provides a method for monitoring the achieving of objectives.

Once this goal has been achieved, it is also possible to find experts on a certain subject matter using the Term Analysis. In contrast to the results in Chapter 6, the Term View will then show dense clusters of interrelated topics. Identifying the most central users in a certain cluster reveals the users who deal most with the topics in that cluster, and therefore can be regarded as experts for these topics.

Starting from the analysis of H4, which showed that a timeline of external events can be obtained from the Term Analysis, we suggest that the same method can be used to identify trends and trendsetters. By observing the centrality of terms over time, the rise of new topics and the vanishment of topics that are no longer of interest could be discovered. Once a trend has been identified, it would be easy to detect the initiators and trendsetters.

However, in our analysis the terms that we traced corresponded to major events, but when looking for trends one has to discover the rise of new topics at a very early stage. It has to be examined in further research with what level of reliability this is possible.