1 code implementation • 21 Feb 2024 • Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng, Jiwoo Kim, Meng-Hsin Wu, Tongshuang Wu, Kenneth Holstein, Haiyi Zhu
AI tools are increasingly deployed in community contexts.
no code implementations • 28 Jul 2023 • Sumit Asthana, Sagih Hilleli, Pengcheng He, Aaron Halfaker
Finally, we evaluate the effectiveness of the system with seven users in the context of their work meetings.
1 code implementation • 20 Dec 2022 • Yixin Liu, Budhaditya Deb, Milagro Teruel, Aaron Halfaker, Dragomir Radev, Ahmed H. Awadallah
We collect a high-quality dataset, DeFacto, containing human demonstrations and informational natural language feedback consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary.
no code implementations • 4 Jun 2020 • Nathan TeBlunthuis, Benjamin Mako Hill, Aaron Halfaker
We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible.
no code implementations • 14 Jan 2020 • C. Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, Haiyi Zhu
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions.
1 code implementation • 11 Sep 2019 • Aaron Halfaker, R. Stuart Geiger
Algorithmic systems---from rule-based bots to machine learning classifiers---have a long history of supporting the essential work of content moderation and other curation work in peer production projects.
no code implementations • 11 Jul 2019 • Christoph Kinkeldey, Claudia Müller-Birn, Tom Gülenman, Jesse Josua Benjamin, Aaron Halfaker
In this paper we present PreCall, an interactive visual interface for ORES, a machine learning-based web service for Wikimedia projects such as Wikipedia.
no code implementations • EMNLP 2017 • Diyi Yang, Aaron Halfaker, Robert Kraut, Eduard Hovy
Most studies on human editing focus merely on syntactic revision operations, failing to capture the intentions behind revision changes, which are essential for facilitating the single and collaborative writing process.
no code implementations • LREC 2016 • Diyi Yang, Aaron Halfaker, Robert Kraut, Eduard Hovy
In this work, we introduced a corpus for categorizing edit types in Wikipedia.