基于情感分析的联合学习隐私检测在虚假的网络建议中使用区块链模型
Jitendra Kumar Samriya1, Amit Kumar2, Ashok Bhansali3
1CSE, IIIT Sonepat, Sonepat, India.
Scientific reports
|April 19, 2025
概括
这项研究引入了一个以隐私为中心的系统,使用区块链和情绪分析来检测虚假的在线建议. 这种新的方法有效地识别和分析欺骗性内容,保护用户的信任和打击错误信息.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 人工智能的人工智能
背景情况:
- 假新闻和欺骗性在线内容的扩散对个人和社会构成重大风险.
- 手动验证在线信息是不切实际的,因为虚构的内容的数量和复杂性质.
- 先进的神经语言模型 (NLM) 可以产生现实的假评论,影响消费者选择和在线平台.
研究的目的:
- 开发一个专注于隐私的系统,用于检测和分析虚假的网页推.
- 为了利用区块链技术和情绪分析来加强假新闻检测.
- 提高在线评论的可靠性,保护消费者免受操纵.
主要方法:
- 利用基于情绪的功能,从网页推中提取出来,作为输入数据.
- 采用生成的卷积伯努利贝叶斯神经网络用于特征提取和分类.
- 集成区块链技术与联合学习,以确保网络隐私.
主要成果:
- 拟议的系统实现了高性能指标:99%的准确性,94%的精度,93%的曲线下面积,94%的回忆,96%的F-测量.
- 使用推特数据和情绪分析,证明了区分垃圾邮件和非垃圾邮件内容的能力.
- 验证了开发的预测模型在识别虚假建议方面的有效性.
结论:
- 区块链和情绪分析的整合为检测假网推提供了一个强大的解决方案.
- 开发的专注于隐私的系统有效地减轻了与在线虚假信息相关的风险.
- 这项研究为增强在线信息生态系统的信任和安全提供了有价值的框架.
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