Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset-Reference-Cited by-同舟云学术

Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset

Published:2023-08-09 Issue:5 Volume:26 Page:3607-3632
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

Wang Haiyang,Wang Ye,Song Xin,Zhou Bin,Zhao Xuechen,Xie Feng

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s11280-023-01191-x.pdf

Reference61 articles.

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2. Dori-Hacohen, S.: Controversy analysis and detection (2017)

3. Rethmeier, N., Hübner, M., Hennig, L.: Learning comment controversy prediction in web discussions using incidentally supervised multi-task cnns. In: Balahur, A., Mohammad, S.M., Hoste, V., Klinger, R. (eds.) Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA@EMNLP 2018, Brussels, Belgium, October 31, 2018, pp. 316–321 (2018). https://doi.org/10.18653/v1/w18-6246

4. Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Transactions on Social Computing 1(1), 1–27 (2018)

5. Hessel, J., Lee, L.: Something’s brewing! early prediction of controversy-causing posts from discussion features. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 1648–1659 (2019). https://doi.org/10.18653/v1/n19-1166

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