  This thesis focuses on gaining linguistic insights into textual discussions
on a word level. It was of special interest to distinguish messages that
constructively contribute to a discussion from those that are detrimental to
them. Thereby, we wanted to determine whether "I"- and "You"-messages are
indicators for either of the two discussion styles. These messages are nowadays
often used in guidelines for successful communication. Although their effects
have been successfully evaluated multiple times, a large-scale analysis has
never been conducted.
  Thus, we used Wikipedia Articles for Deletion (short: AfD) discussions
together with the records of blocked users and developed a fully automated
creation of an annotated data set. In this data set, messages were labelled
either constructive or disruptive. We applied binary classifiers to the data to
determine characteristic words for both discussion styles. Thereby, we also
investigated whether function words like pronouns and conjunctions play an
important role in distinguishing the two.
  We found that "You"-messages were a strong indicator for disruptive messages
which matches their attributed effects on communication. However, we found
"I"-messages to be indicative for disruptive messages as well which is contrary
to their attributed effects. The importance of function words could neither be
confirmed nor refuted. Other characteristic words for either communication
style were not found. Yet, the results suggest that a different model might
represent disruptive and constructive messages in textual discussions better.
