在印度尼西亚社交媒体上检测仇恨言论和侮辱性语言:进展和挑战
Muhammad Okky Ibrohim1, Indra Budi2
1Dipartimento di Informatica, Università degli Studi di Torino, Italy.
Heliyon
|August 28, 2023
概括
这项研究回顾了印尼的仇恨言论和侮辱性语言 (HSAL) 检测研究. 大多数研究都在推特数据上使用经典的机器学习,突出了对先进检测系统的需求.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 自然语言处理自然语言处理.
背景情况:
- 仇恨言论和侮辱性语言 (HSAL) 在社交媒体上激增,造成社会冲突的风险.
- 有效的HSAL检测对于减轻在线和离线伤害至关重要.
- 现有的关于HSAL检测的研究涵盖了多种语言,但缺口仍然存在,特别是在印尼社交媒体上.
研究的目的:
- 系统地审查和总结有关印尼仇恨言论和滥用语言检测的现有研究.
- 确定目前在印尼HSAL检测中使用的方法和数据集.
- 确定挑战和未来的研究方向,以开发改进的HSAL检测系统.
主要方法:
- 采用了遵循基奇纳姆方法的系统文献审查.
- 审查的重点是关于在印度尼西亚社交媒体环境中检测HSAL的研究.
- 分析包括研究方法,文本表示特征和实验数据集.
主要成果:
- 大多数印度尼西亚HSAL检测研究使用经典的机器学习算法.
- 传统的文本表示功能通常被应用,通常与Twitter数据集.
- 目前的系统在检测特定的HSAL属性方面存在重大局限性.
结论:
- 需要超越经典的机器学习方法来实现更强大的印尼HSAL检测.
- 未来的系统应该旨在识别仇恨言论的目标,类别和严重程度.
- 开发检测HSAL发起者,传播者和恶意行为者 (例如假账户) 的能力至关重要.
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