了解基于内容和上下文的功能在检测Twitter上的滥用行为中的作用
Kamal Hussain1, Zafar Saeed2, Rabeeh Abbasi3
1Instituto Superior Técnico, Universidade de Lisboa, Portugal.
Heliyon
|April 26, 2024
概括
这项研究引入了一种新的方法,通过分析周围的帖子来检测Twitter上的在线滥用. 这种上下文意识的方法大大提高了滥用检测的准确性,而不是仅仅内容的方法.
科学领域:
- 计算机科学 计算机科学
- 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:
- 对像Twitter这样的社交媒体平台来说,滥用帖子是一个重大挑战.
- 现有的滥用检测方法往往忽略了推文的上下文信息,只关注孤立的内容.
- 这种限制阻碍了对滥用内容及其潜在意图的准确识别.
研究的目的:
- 开发和评估一种新的方法来检测Twitter上的在线滥用.
- 将前后推文的上下文信息纳入,以加强滥用检测.
- 调查各种机器学习算法和特征组合的有效性.
主要方法:
- 提出了一种新的滥用检测方法,利用来自围绕目标帖子的推文的上下文信息.
- 进行了广泛的实验,评估了八种不同的机器学习分类器.
- 测试了基于内容和基于上下文的功能,包括它们的组合.
主要成果:
- 拟议的上下文意识方法比现有方法实现了约7%的绝对改进.
- 结合内容和基于上下文的功能产生了最好的表现.
- 拟议方法的最高精度为86%,而现有方法的最高精度为79.2%.
结论:
- 背景信息显著提高了Twitter上在线滥用检测的准确性.
- 结合内容和上下文特征的混合方法最有效.
- 开发的方法为减轻在线滥用造成的伤害提供了实质性的改进.
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