W ikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing W iki Deletion Discussions (2025)

Abstract: Automated content moderation for collaborative knowledge hubs like Wikipedia or Wikidata is an important yet challenging task due to multiple factors. In this paper, we construct a database of discussions happening around articles marked for deletion in several Wikis and in three languages, which we then use to evaluate a range of LMs on different tasks (from predicting the outcome of the discussion to identifying the implicit policy an individual comment might be pointing to). Our results reveal, among others, that discussions leading to deletion are easier to predict, and that, surprisingly, self-produced tags (keep, delete or redirect) don’t always help guiding the classifiers, presumably because of users’ hesitation or deliberation within comments

## introduction wikipedia and its sister wikis play an indispensable role as a collaborative knowledge source, and are widely used by students @xcite and the general public @xcite alike. they fulfill use cases that range from core knowledge go-tos, as well as "free" supporting documentation for content providers and search engines (e.g., google 2 or youtube 3 ). however, due to their size and, most importantly, their collaborative nature, ensuring high quality in these platforms is challenging, especially given the need to "map" content to existing policies at least semi-automatically @xcite . this is particularly relevant in the genai era, as aigenerated content has proliferated throughout the internet @xcite . task lang. platform label set outcome prediction en wikipedia delete, keep, redirect, no-consensus, merge, speedy keep, speedy delete, withdrawn wikidata-ent. delete, keep, merge, redirect, no-consensus, comment wikidata-pr. delete, keep, no-consensus wikiquote delete, keep, redirect, merge, no-consensus wikinews delete, keep, speedy delete, comment es wikipedia borrar (delete), mantener (keep), fusionar (merge), otros (others) gr wikipedia διαγραφή (delete), διατήρηση (keep), δεν υπάρχει συναίνεση (noconsensus) stance detection en wikipedia delete, keep, merge, comment policy prediction en wikipedia wikipedia:notability, wikipedia:what wikipedia is not, wikipedia:no original research, wikipedia:verifiability, wikipedia:arguments to avoid in deletion discussions, wikipedia:biographies of living persons, wikipedia:criteria for speedy deletion, wikipedia:articles for deletion, wikipedia:wikipedia is not a dictionary, wikipedia:deletion policy the way this generally works is that, given an article flagged by a community member, users justify their stance towards it and its adherence to the policies, and then editors act. however, manual efforts to clear the backlog are insufficient. therefore, nlp techniques for predicting the outcome of a deletion discussion, or for capturing a user's stance towards a specific article are critical @xcite . despite this need, there is a surprising lack of work beyond wikipedia, and there is almost no published work that looks at non-english languages (with the exception of @xcite ). moreover, in-depth comparative analyses of parameter-efficient techniques have also been so far largely unexplored. we therefore aim to address all of the above with an analysis on a novel collection of deletion discussions, in three languages and four platforms. these discussions come, if resolved, alongside the discussion outcomes (generally speaking, suggesting to keep or delete the article, although the actual outcome tags are more fine grained than this), with individual comments having their own stance and referring to specific policies @xcite . our classification experiments set strong baseline results for the community to build upon, and provide insights into these community-led activities. we consider three tasks, namely (1) outcome prediction -given a full discussion around an article language platform total ## experiments with these datasets in place, we proceed to run classification experiments. ## conclusion automated content moderation is a challenging yet important part of maintaining healthy content in community driven wiki-platforms. through this work, we analyze four different wiki-platforms and three languages to give an all-round understanding of automated content moderation scenarios in these wikis. our analysis shows that these community based platforms can highly benefit from the usage of plm based content moderation strategies, and to that end, we contribute a dataset and a range of strong baseline results from different plms for the community to build on.