HAPI:一种高效的混合功能基于工程的方法,用于在社交媒体上识别宣传
Akib Mohi Ud Din Khanday1, Mudasir Ahmad Wani2, Syed Tanzeel Rabani3
1Dept. of Computer Sciences & Software Engineering-CIT, United Arab Emirates University, Al Ain, United Arab Emirates.
PloS one
|July 10, 2024
概括
本研究介绍了一种用于宣传识别 (HAPI) 的混合特征工程方法,用于检测社交媒体上的欺骗性信息. 基于SVM的HAPI实现了69.2%的准确性,超过了识别在线宣传的现有方法.
科学领域:
- 计算语言学 计算语言学
- 社交计算社会计算
- 信息科学 信息科学 信息科学
背景情况:
- 社交媒体平台促进了快速的信息共享,通常不考虑准确性.
- 包括假新闻和阴谋论在内的宣传在这些平台上传播,以影响公众论.
- 有效地检测宣传对于保持信息完整性至关重要.
研究的目的:
- 为基于文本的内容引入宣传识别 (HAPI) 的混合特征工程方法.
- 开发和评估用于分类宣传和非宣传推特的机器学习方法.
- 为了提高宣传探测系统的准确性.
主要方法:
- 通过API从Twitter收集数据,并为二进制分类 (宣传/非宣传) 提出了一个注释方案.
- 采用混合功能工程,结合TF-IDF,词汇袋,情感功能和推文长度.
- 训练和评估多个机器学习分类器,包括SVM,MNB,DT和LR,使用40个选定的功能.
主要成果:
- 基于支持矢量机器的HAPI (SVM-HAPI) 实现了卓越的性能,总准确率为69.2%,精度为0.69,回忆率为0.69和F-Measure为0.69.
- 提出的HAPI方法在几个评估指标上超过了大多数现有方法.
- 所有评估的算法在宣传检测方面都显示出有希望的结果.
结论:
- 开发的HAPI系统为识别文本内容中的宣传提供了一个强大的解决方案.
- 这项研究强调了将传统和机器学习功能用于宣传检测的有效性.
- 未来的研究应该探索深度学习用于多模式宣传检测,包括文本,图像和视频.
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