通过预先训练的模型预测社交媒体用户的间接攻击
Zhenkun Zhou1, Mengli Yu2,3,4, Xingyu Peng5
1Department of Data Science, School of Statistics, Capital University of Economics and Business, Beijing, China.
PeerJ. Computer science
|September 24, 2024
概括
这项研究引入了一种用于预测在线间接攻击的新模型,这是社交媒体上一个常见的问题. 开发的ERNIE模型有效地识别了像微博这样的平台上的社会排斥,恶意幽默和内诱导行为.
科学领域:
- 社会心理学 社会心理学
- 计算语言学 计算语言学
- 网络心理学 网络心理学
背景情况:
- 间接攻击是社交媒体上日益严重的问题,传统方法很难检测到.
- 由于成本和客观性挑战,现有的自我报告问卷对在线环境是不切实际的.
- 了解和减轻在线间接攻击对于社交媒体平台管理至关重要.
研究的目的:
- 开发和评估一种用于预测在线社交媒体环境中的间接攻击的新型模型.
- 将间接攻击分为以下子类型:社会排斥,恶意幽默和内诱导.
- 为了提高预测准确度,利用预先训练的模型.
主要方法:
- 从微博用户数据中构建特征,包括基本,动态和基于内容的属性.
- 通过将这些特征与大规模预训练模型集成,开发了一个预测模型.
- 将间接攻击分为三个不同的亚型进行细分分析.
主要成果:
- 拟议的预测模型 (ERNIE) 与基线预训练模型相比显示出更高的性能.
- 该模型显著提高了在线预测间接攻击的准确性.
- 经验证据支持该模型在识别各种形式的间接攻击方面的有效性.
结论:
- 该研究提出了一种实用且有效的模型,用于预测用户在社交媒体上的间接攻击.
- 这项研究提高了对在线间接攻击行为的理解.
- 这些发现可以帮助社交媒体平台进行内容调节和用户管理策略.
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