预测微博平台上仇恨言论的受害者
Sahrish Khan1,2, Rabeeh Ayaz Abbasi1, Muddassar Azam Sindhu1
1Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
Heliyon
|December 19, 2024
概括
这项研究引入了一个新的框架,用于预测社交媒体上受仇恨言论准的用户. 天真贝叶斯分类器达到93%的准确性,识别了主动减轻仇恨言论的关键特征.
科学领域:
- 计算语言学 计算语言学
- 社交计算社会计算
- 机器学习 机器学习
背景情况:
- 仇恨言论是微博平台的一个重要问题.
- 现有的研究主要侧重于用于检测仇恨言论的内容分析.
- 需要主动识别仇恨言论的潜在目标.
研究的目的:
- 提出一个新的仇恨言论目标预测框架 (HTPK).
- 为目标识别引入新的仇恨言论目标数据集 (HSTD).
- 确定最佳特征并比较用于预测仇恨言论目标的机器学习算法.
主要方法:
- 发展仇恨言论目标预测框架 (HTPK).
- 创建仇恨言论目标数据集 (HSTD),标记目标和非目标.
- 使用术语频率-反向文档频率 (TFIDF),N-grams和部分语音 (PoS) 标签作为功能.
- 评估各种机器学习算法,包括天真贝叶斯 (NB).
主要成果:
- 纯粹的贝叶斯分类器达到93%的最高准确率.
- 拟议的HTPK框架在预测仇恨言论目标方面表现出有效性.
- 确定了TFIDF,N-gram和PoS标签作为目标预测的最佳特征.
结论:
- 这项研究为积极的仇恨言论缓解策略提供了基础.
- 该研究强调了HTPK框架和NB分类器的有效性.
- 识别潜在目标对于开发更有效的针对网络仇恨言论的干预措施至关重要.
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