WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection

• URL: http://arxiv.org/abs/2003.06190v1
• Date: Fri, 13 Mar 2020 10:26:45 GMT
• Title: WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection
• Authors: No\'e Cecillon (LIA), Vincent Labatut (LIA), Richard Dufour (LIA), Georges Linares (LIA)
• Abstract summary: We propose an original framework, based on the Wikipedia Comment corpus, with comment-level annotations of different types. This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches. We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the spread of online social networks, it is more and more difficult to monitor all the user-generated content. Automating the moderation process of the inappropriate exchange content on Internet has thus become a priority task. Methods have been proposed for this purpose, but it can be challenging to find a suitable dataset to train and develop them. This issue is especially true for approaches based on information derived from the structure and the dynamic of the conversation. In this work, we propose an original framework, based on the Wikipedia Comment corpus, with comment-level abuse annotations of different types. The major contribution concerns the reconstruction of conversations, by comparison to existing corpora, which focus only on isolated messages (i.e. taken out of their conversational context). This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches. We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection, trying to avoid the recurring problem of result replication. Finally, we apply two classification methods to our dataset to demonstrate its potential.

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