@inproceedings{hartmann-etal-2019-issue,
title = "Issue Framing in Online Discussion Fora",
author = "Hartmann, Mareike and
Jansen, Tallulah and
Augenstein, Isabelle and
S{\o}gaard, Anders",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1142",
doi = "10.18653/v1/N19-1142",
pages = "1401--1407",
abstract = "In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.",
}
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%0 Conference Proceedings
%T Issue Framing in Online Discussion Fora
%A Hartmann, Mareike
%A Jansen, Tallulah
%A Augenstein, Isabelle
%A Søgaard, Anders
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F hartmann-etal-2019-issue
%X In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.
%R 10.18653/v1/N19-1142
%U https://aclanthology.org/N19-1142
%U https://doi.org/10.18653/v1/N19-1142
%P 1401-1407
Markdown (Informal)
[Issue Framing in Online Discussion Fora](https://aclanthology.org/N19-1142) (Hartmann et al., NAACL 2019)
ACL
- Mareike Hartmann, Tallulah Jansen, Isabelle Augenstein, and Anders Søgaard. 2019. Issue Framing in Online Discussion Fora. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1401–1407, Minneapolis, Minnesota. Association for Computational Linguistics.