A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

被引：28

作者：

Rezvan, Mohammadreza ^{[1
]}

Shekarpour, Saeedeh ^{[2
]}

Balasuriya, Lakshika ^{[1
]}

Thirunarayan, Krishnaprasad ^{[1
]}

Shalin, Valerie L. ^{[1
]}

Sheth, Amit ^{[1
]}

机构：

[1] Kno E Sis Ctr, Dayton, OH 43017 USA

[2] Univ Dayton, Dayton, OH 45469 USA

来源：

WEBSCI'18: PROCEEDINGS OF THE 10TH ACM CONFERENCE ON WEB SCIENCE | 2018年

基金：

美国国家科学基金会;

关键词：

Annotated corpus; context; sexual; racial; political; appearance-related; intellectual; cyberbullying; harassment; offensive Lexicon; profane word;

D O I：

10.1145/3201064.3201103

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A quality annotated corpus is essential to research. Despite the recent focus of the Web science community on cyberbullying research, the community lacks standard benchmarks. This paper provides both a quality annotated corpus and an offensive words lexicon capturing different types of harassment content: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual, and (v) political(1). We first crawled data from Twitter using this content-tailored offensive lexicon. As mere presence of an offensive word is not a reliable indicator of harassment, human judges annotated tweets for the presence of harassment. Our corpus consists of 25,000 annotated tweets for the five types of harassment content and is available on the Git repository(2).

引用

下载

页码：33 / 36

页数：4

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