Did I Say Something Wrong? a Word-Level Analysis of Wikipedia Articles for Deletion Discussions

From Wikipedia Quality
Revision as of 09:15, 11 August 2019 by Aubree (talk | contribs) (Adding new article - Did I Say Something Wrong? a Word-Level Analysis of Wikipedia Articles for Deletion Discussions)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Did I Say Something Wrong? a Word-Level Analysis of Wikipedia Articles for Deletion Discussions - scientific work related to Wikipedia quality published in 2016, written by Michael Ruster.

Overview

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, authors 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, authors 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. Authors applied binary classifiers to the data to determine characteristic words for both discussion styles. Thereby, authors also investigated whether function words like pronouns and conjunctions play an important role in distinguishing the two. Authors found that “You”-messages were a strong indicator for disruptive messages which matches their attributed effects on communication. However, authors 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.