用88%准确率模型识别俄方推特网络的假账号行为
Mapping the Russian Internet Troll Network on Twitter using a Predictive Model
- 基于行为模式构建预测模型，区分账号真伪
- 测试集准确率达88%，与真实数据集相似度超90%
- 适合研究网络操纵、信息战的学者和平台安全团队
俄罗斯网络水军通过虚假身份在多个社交媒体上传播虚假信息。鉴于此类威胁在社交平台日益频繁，理解其运作机制至关重要。本文利用被识别为俄罗斯影响力网络一部分的推特内容，构建预测模型以映射该网络的运作。通过引入逻辑分类标准，对部分账号进行真实性功能分类，并训练模型识别网络中类似行为模式。模型在测试集上达到88%的预测准确率。通过与300万条俄罗斯水军推文数据集对比验证，二者相似度达90.7%。进一步将模型应用于俄罗斯推文数据集，预测结果与实际类别对应率达90.5%。结果表明，该模型可有效辅助识别此类网络中的关键参与者。
原文摘要 · Abstract (English)
Russian Internet Trolls use fake personas to spread disinformation through multiple social media streams. Given the increased frequency of this threat across social media platforms, understanding those operations is paramount in combating their influence. Using Twitter content identified as part of the Russian influence network, we created a predictive model to map the network operations. We classify accounts type based on their authenticity function for a sub-sample of accounts by introducing logical categories and training a predictive model to identify similar behavior patterns across the network. Our model attains 88% prediction accuracy for the test set. Validation is done by comparing the similarities with the 3 million Russian troll tweets dataset. The result indicates a 90.7% similarity between the two datasets. Furthermore, we compare our model predictions on a Russian tweets dataset, and the results state that there is 90.5% correspondence between the predictions and the actual categories. The prediction and validation results suggest that our predictive model can assist with mapping the actors in such networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。