一种熟练的方法来确定和逃避可能的社交机器人攻击在Twitter和Twitch上,采用机器学习方法
Monikka Reshmi Sethurajan1, Natarajan K2
1Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, Karnataka 560074, India.
这项研究引入了一种新的机器学习模型,用于检测Twitter和Twitch等平台上的社交机器人,改进虚假新闻检测. 开发的方法实现了高准确性,大大提高了在线信息真实性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 社交媒体平台面临着由社交机器人传播的不真实的信息的挑战.
- 现有的机器人检测和事实核查模型与机器人用户不可区分性和数据限制作斗争.
研究的目的:
- 开发一个先进的机器学习模型,用于准确的社交机器人检测和假新闻识别.
- 提高社交媒体平台上共享信息的可靠性.
主要方法:
- 一个基于机器学习的自适应性模糊神经模型,集成到一个组图梯度增强 (HGB) 分类器.
- 哈里斯·霍克优化结合Bi-LSTM用于社交机器人预测.
主要成果:
- HGB分类器表现出高效率,在Twitter机器人数据集上达到95.64%的准确性.
- 该模型在Twitch bot数据集上实现了98.98%的准确性,验证了其预测能力.
结论:
- 拟议的综合模型有效地识别了社交机器人,并有助于检测假新闻.
- 这种先进的方法比社交机器人分析的传统方法有了显著的改进.
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