A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues

Barbara Konat, John Lawrence, Joonsuk Park, Katarzyna Budzynska, Chris Reed


Abstract
Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- & con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.
Anthology ID:
L16-1617
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3899–3906
Language:
URL:
https://aclanthology.org/L16-1617
DOI:
Bibkey:
Cite (ACL):
Barbara Konat, John Lawrence, Joonsuk Park, Katarzyna Budzynska, and Chris Reed. 2016. A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3899–3906, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues (Konat et al., LREC 2016)
Copy Citation:
PDF:
https://aclanthology.org/L16-1617.pdf