期刊
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
卷 17, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3032989
关键词
Argument; argumentation; corpus; dialogue; sensemaking; engagement; analytics
资金
- EPSRC in the UK [EP/N014871/1]
- Innovate UK [101777]
- National Science Foundation in the USA [1314778]
- EPSRC [EP/N014871/1] Funding Source: UKRI
- Innovate UK [101777] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/N014871/1] Funding Source: researchfish
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1314778] Funding Source: National Science Foundation
Governments around the world are increasingly utilising online platforms and social media 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. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.
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