When reviewing the analyses we conducted, the most problematic aspect in our work is the definition of the datasets. Because the Wikinews community is not represented by communication data which contains direct message exchanges between the actors in the network, we had to define the communication links ourselves. In doing so, one will, consciously or unconsciously, manipulate the data to some extent. Nevertheless, we believe that our definition of the datasets avoids bias as far as possible.

However, because the datasets are defined differently from those that TeCFlow was made for, the question remains whether the results attained are valid.

While for example the indices were defined for analyzing direct mail-communication and can be interpreted clearly in that context, we had to consider in each analysis whether it is suitable to use an index or not. Additionally, we had to interpret the results generated with TeCFlow against the background of how the datasets were defined. In general, unlike the analysis of a mailbox, our analysis required us to reassess each of TeCFlow’s functions with respect to its meaning and its significance.

Nonetheless, when recapitulating how we finally used TeCFlow for our analysis, we believe that the tool significantly facilitated our work.

Considering the first three hypotheses, emphasis was placed on automated processing, meaning that TeCFlow made the analysis easier and faster to accomplish. Therefore, the advantage here was mainly quantitative. Yet, in the analysis of the third hypothesis it would not have been feasible to look at every single article of an author in order to identify his role at a certain point in time. This was the first step into a qualitative advantage. At the latest for the fourth hypothesis TeCFlow not only acted as a facilitator but as an enabler. Considering the amount of data, it would have been impossible to conduct such an analysis of the content manually. Moreover, without TeCFlow and its features, we would not have had the idea to approach the problem the way we did. The Dynamic View Function made it possible to easily identify the shifting interests in the community.

Therefore, we conclude that while the definition of the datasets is problematic, applying Social Network Analysis to Wikinews is possible and TeCFlow is of great worth for the analysis if the peculiarities that follow from the data definition are considered in the interpretation of the results.