Igor Melnyk, Youssef Mroueh, et al.
NeurIPS 2024
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia’s metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.
Igor Melnyk, Youssef Mroueh, et al.
NeurIPS 2024
Yuya Jeremy Ong, Jay Pankaj Gala, et al.
IEEE CISOSE 2024
Jatin Ganhotra, Robert Moore, et al.
EMNLP 2020
Liat Ein-Dor, Alon Halfon, et al.
EMNLP 2020