But it is difficult to relate social information which occurred in the original discussion, to the issue model, since the information loses the context of the argumentation sequence. We will deepen that below, too. In our terminology full-text search engines can be seen as an automatism which does not require any modeling since it works on the original arguments of the discussion. We don't believe that search engines can provide appropriate solutions to our problem, since they can't reflect the argumentation structure of the discussion. The IBIS method can be seen as the father of the two approaches described here.
Users have to categorize their texts on submission.
Which is certainly the case in our problem field. In addition to primitives similar to those of gIBIS ZENO offers the opportunity to state preference expressions over previous discussion contributions as a discussion contribution and an evaluation function for discussion contributions and a computational framework to determine, what issue branches of the discussion have been agreed upon and what have been rejected. We note that in both Systems the option to lead meta-discussions on the discussion or the tools itself is mentioned, e.g. for building coalitions. In real world communication environments there have been developing routines to handle complexity based on trust. Recommendations can be a good starting point for exploring a discussion.