Wikimedia Research/Showcase/Archive/2024/01
January 2024[edit]
- Time
- Wednesday, January 17, 17:30 UTC: Find your local time here
- Theme
- Connecting Actions with Policy
January 17, 2023 Video: YouTube
- Presenting the report "Unreliable Guidelines"
- By Amber Berson and Monika Sengul-Jones
- The goal behind the report Unreliable Guidelines: Reliable Sources and Marginalized Communities in French, English and Spanish Wikipedias was to understand the effects of the set of reliable source guidelines and rules on the participation of and the content about marginalized communities on three Wikipedias. Two years following the release of their report, researchers Berson and Sengul-Jones reflect on the impact of their research as well as the actionable next steps.
- By Amber Berson and Monika Sengul-Jones
- Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
- By Lucie-Aimée Kaffee and Arnav Arora
- The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. However, currently only a few comments explicitly mention those policies. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process.
- Paper: Kaffee, Lucie-Aimée, Arnav Arora, and Isabelle Augenstein. Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
- The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. However, currently only a few comments explicitly mention those policies. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process.
- By Lucie-Aimée Kaffee and Arnav Arora