Summarizing Multi-Party Argumentative Conversations in Reader Comment on News

Barker, Emma; Gaizauskas, Robert

doi:10.18653/v1/w16-2802

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…The comment-level annotations capture characteristics such as sentiment, persuasiveness or tone of each comment; whereas thread-level annotations label the quality of the overall thread such as whether the conversation is constructive and whether the conversation is positive/respectful or aggressive. The other prominent comments corpus is the SENSEI Social Media Annotated Corpus 2 (Barker and Gaizauskas 2016). The goal of this work is to create summaries of reader comments, and accordingly, the authors created an annotated corpus of 1845 comments posted on 18 articles from the British newspaper The Guardian.…”

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confidence: 99%

The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments

Kolhatkar

Cavasso

et al. 2019

Corpus Pragmatics

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We present the SFU Opinion and Comments Corpus (SOCC), a collection of opinion articles and the comments posted in response to the articles. The articles include all the opinion pieces published in the Canadian newspaper The Globe and Mail in the 5-year period between 2012 and 2016, a total of 10,339 articles and 663,173 comments. SOCC is part of a project that investigates the linguistic characteristics of online comments. The corpus can be used to study a host of pragmatic phenomena. Among other aspects, researchers can explore: the connections between articles and comments; the connections of comments to each other; the types of topics discussed in comments; the nice (constructive) or mean (toxic) ways in which commenters respond to each other; how language is used to convey very specific types of evaluation; and how negation affects the interpretation of evaluative meaning in discourse. Our current focus is the study of constructiveness and evaluation in the comments. To that end, we have annotated a subset of the large corpus (1043 comments) with four layers of annotations: constructiveness, toxicity, negation and Appraisal (Martin and White, The language of evaluation, Palgrave, New York, 2005). This paper details our corpus, the data collection process, the characteristics of the corpus and describes the annotations. While our focus is comments posted in response to opinion news articles, the phenomena in this corpus are likely to be present in many commenting platforms: other news comments, comments and replies in fora such as Reddit, feedback on blogs, or YouTube comments.

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confidence: 99%

The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments

Kolhatkar

Cavasso

et al. 2019

Corpus Pragmatics

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“…The situation is even worse for the task of constructive comment classification. One of them, the SENSEI Social Media Annotated Corpus (Barker and Gaizauskas, 2016) contains only 1,845 comments from 18 articles. The Yahoo News Annotated Comments Corpus (YNACC) (Napoles, et al, 2017) is much more extensive, at 9,200 comments and 2,400 threads, capturing characteristics such as sentiment, persuasiveness or tone of each comment.…”

Section: Data and Annotationmentioning

confidence: 99%

Classifying constructive comments

Kolhatkar

Thain

Sorensen³

et al. 2023

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We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions. We define constructive comments as high-quality comments that make a contribution to the conversation. We explain the crowd worker annotation scheme and de ne a taxonomy of subcharacteristics of constructiveness. The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept. We provide models for constructiveness trained on C3 using both feature-based and a variety of deep learning approaches and demonstrate, through domain adaptation experiments, that these models capture general rather than topic- or domain-specific characteristics of constructiveness. We also examine the role that length plays in our models, as comment length could be easily gamed if models depend heavily upon this feature. By examining the errors made by each model and their distribution by length, we show that the best performing models are effective independently of comment length. The constructiveness corpus and our experiments pave the way for a moderation tool focused on promoting comments that make a meaningful contribution, rather than only filtering out undesirable content.

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“…Argument mining is a task to construct the structure of a document. It is applied to many natural language processing tasks such as document summarization (Barker and Gaizauskas, 2016;Peldszus, 2014), the automatic scoring of essays (Ghosh et al, 2016), the paper writing support (Stab and Gurevych, 2017b;Nguyen and Litman, 2016), the information retrieval (Stab et al, 2018) and so on. Stab and Gurevych (2014) have tackled the relation identification for essays written by students.…”

Section: Related Workmentioning

confidence: 99%