在LLM时代揭露影响力活动:一种基于行为的人工智能方法来检测国家赞助的巨魔
Fatima Ezzeddine1,2, Omran Ayoub1, Silvia Giordano1
1Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
概括
这项研究介绍了一种人工智能解决方案,用于检测社交媒体上的国家赞助的巨魔,仅使用行为模式,而不是文本. 该方法准确地识别了鱼账户,为不断变化的影响力活动提供了弹性方法.
科学领域:
- 社交媒体分析 社交媒体分析
- 人工智能的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 在社交媒体影响力活动中检测国家赞助的巨魔是一个关键的挑战.
- 现有的方法通常依赖于文本内容,而文本内容对复杂的生成技术越来越脆弱.
研究的目的:
- 提出和评估一种基于人工智能的新型解决方案,用于识别国家赞助的troll帐户.
- 开发一种方法,它完全依赖于从分享活动序列中获得的行为线索.
主要方法:
- 使用基于长短期记忆 (LSTM) 的分类器来区分鱼和有机帐户序列.
- 开发了一种基于分类序列的"巨魔分数"指标,以量化类似巨魔的行为.
- 评估了使用2016年俄罗斯干预美国总统大选活动数据的方法.
主要成果:
- 实现了接近99%的曲线下的面积 (AUC) 来识别账户序列.
- 在俄罗斯巨魔和有机用户之间进行区分,AUC为91%.
- 在识别基于行为模式的鱼帐户方面表现出很高的表现.
结论:
- 提出的基于行为的人工智能方法提供了一种弹性和有效的方法来检测国家赞助的巨魔.
- 与基于文本的方法相比,这种技术对用于生成非真实的内容的大型语言模型 (LLM) 更为强大.
- 该解决方案在不同的信息操作中显示了通用性,为未来的研究铺平了道路.
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