Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) |
| Pages | 454-464 |
| Number of pages | 11 |
| Publication status | Published - Oct 2018 |
| Externally published | Yes |
| Event | 22nd Conference on Computational Natural Language Learning, CoNLL 2018 - Brüssel, Belgium Duration: 31 Oct 2018 → 1 Nov 2018 |
Abstract
News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone’s stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial’s stance — or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators’ political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone’s stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.
ASJC Scopus subject areas
- Social Sciences(all)
- Linguistics and Language
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
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Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). 2018. p. 454-464.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Challenge or Empower:
T2 - 22nd Conference on Computational Natural Language Learning, CoNLL 2018
AU - El Baff, Roxanne
AU - Wachsmuth, Henning
AU - Al-Khatib, Khalid
AU - Stein, Benno
PY - 2018/10
Y1 - 2018/10
N2 - News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone’s stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial’s stance — or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators’ political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone’s stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.
AB - News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone’s stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial’s stance — or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators’ political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone’s stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.
UR - http://www.scopus.com/inward/record.url?scp=85072911204&partnerID=8YFLogxK
U2 - 10.18653/v1/k18-1044
DO - 10.18653/v1/k18-1044
M3 - Conference contribution
AN - SCOPUS:85072911204
SN - 9781948087728
SP - 454
EP - 464
BT - Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)
Y2 - 31 October 2018 through 1 November 2018
ER -