@inproceedings{kolhatkar-taboada-2017-constructive,
title = "Constructive Language in News Comments",
author = "Kolhatkar, Varada and
Taboada, Maite",
editor = "Waseem, Zeerak and
Chung, Wendy Hui Kyong and
Hovy, Dirk and
Tetreault, Joel",
booktitle = "Proceedings of the First Workshop on Abusive Language Online",
month = aug,
year = "2017",
address = "Vancouver, BC, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3002",
doi = "10.18653/v1/W17-3002",
pages = "11--17",
abstract = "We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59{\%} (baseline=49.44{\%}) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42{\%} of the non-constructive comments and 17.89{\%} of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kolhatkar-taboada-2017-constructive">
<titleInfo>
<title>Constructive Language in News Comments</title>
</titleInfo>
<name type="personal">
<namePart type="given">Varada</namePart>
<namePart type="family">Kolhatkar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maite</namePart>
<namePart type="family">Taboada</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Abusive Language Online</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zeerak</namePart>
<namePart type="family">Waseem</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wendy</namePart>
<namePart type="given">Hui</namePart>
<namePart type="given">Kyong</namePart>
<namePart type="family">Chung</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dirk</namePart>
<namePart type="family">Hovy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joel</namePart>
<namePart type="family">Tetreault</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, BC, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59% (baseline=49.44%) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42% of the non-constructive comments and 17.89% of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.</abstract>
<identifier type="citekey">kolhatkar-taboada-2017-constructive</identifier>
<identifier type="doi">10.18653/v1/W17-3002</identifier>
<location>
<url>https://aclanthology.org/W17-3002</url>
</location>
<part>
<date>2017-08</date>
<extent unit="page">
<start>11</start>
<end>17</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Constructive Language in News Comments
%A Kolhatkar, Varada
%A Taboada, Maite
%Y Waseem, Zeerak
%Y Chung, Wendy Hui Kyong
%Y Hovy, Dirk
%Y Tetreault, Joel
%S Proceedings of the First Workshop on Abusive Language Online
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC, Canada
%F kolhatkar-taboada-2017-constructive
%X We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59% (baseline=49.44%) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42% of the non-constructive comments and 17.89% of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.
%R 10.18653/v1/W17-3002
%U https://aclanthology.org/W17-3002
%U https://doi.org/10.18653/v1/W17-3002
%P 11-17
Markdown (Informal)
[Constructive Language in News Comments](https://aclanthology.org/W17-3002) (Kolhatkar & Taboada, ALW 2017)
ACL
- Varada Kolhatkar and Maite Taboada. 2017. Constructive Language in News Comments. In Proceedings of the First Workshop on Abusive Language Online, pages 11–17, Vancouver, BC, Canada. Association for Computational Linguistics.