"Did I Say Something Wrong?" A Word-Level Analysis of Wikipedia Articles for Deletion Discussions
Michael Ruster

TL;DR
This study analyzes Wikipedia deletion discussions at the word level to identify linguistic markers of constructive versus disruptive messages, revealing that 'You'-messages strongly indicate disruption, while 'I'-messages also unexpectedly correlate with disruption.
Contribution
It introduces a large-scale, automated dataset of annotated discussion messages and applies classifiers to identify linguistic indicators of discussion quality, highlighting the roles of pronouns and function words.
Findings
'You'-messages are strong indicators of disruptive communication.
'I'-messages also correlate with disruptive messages, contrary to expectations.
Function words' importance remains inconclusive.
Abstract
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…
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Taxonomy
TopicsWikis in Education and Collaboration · Natural Language Processing Techniques · Topic Modeling
