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Controversy is a reflection of our zeitgeist, and an important aspect to any discourse.
A framework for understanding unintended consequences of machine learning
Harini Suresh and John V Guttag. 2019 · 1901
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Something’s brewing! early prediction of controversy-causing posts from discussion features
Jack Hessel and Lillian Lee. 2019 · 1904
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Benjamin Sznajder, Ariel Gera, Yonatan Bilu, Dafna Sheinwald, Ella Rabinovich, Ranit Aharonov, David Konopnicki, and Noam Slonim. 2019 · 1908
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Social bias frames: Reasoning about social and power implications of language
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Directions in abusive language training data: Garbage in, garbage out
Bertie Vidgen and Leon Derczynski. 2020 · 2004
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Detecting controversial events from twitter
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Telling apart tweets associated with controversial versus non-controversial topics
Aseel Addawood, Rezvaneh Rezapour, Omid Abdar, and Jana Diesner. 2017 · 2017
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Fast Krippendorff: Fast computation of Krippendorff’s alpha agreement measure
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Automatic controversy detection in social media: A content-independent motif-based approach
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Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017 · 2017
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Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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From surrogacy to adoption; from bitcoin to cryptocurrency: Debate topic expansion
Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, and Noam Slonim. 2019 · 2019
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A survey on bias and fairness in machine learning
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An autonomous debating system
Noam Slonim, Yonatan Bilu, Carlos Alzate, Roy Bar-Haim, Ben Bogin, Francesca Bonin, Leshem Choshen, Edo Cohen-Karlik, Lena Dankin, Lilach Edelstein, et al. 2021 · 2021
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Anger breeds controversy: Analyzing controversy and emotions on reddit
Kai Chen, Zihao He, Rong-Ching Chang, Jonathan May, and Kristina Lerman. 2022 · 2022
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Handling and presenting harmful text in NLP research
Hannah Kirk, Abeba Birhane, Bertie Vidgen, and Leon Derczynski. 2022 · 2022
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A survey on bias and fairness in machine learning
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Language models are few-shot learners
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