保持真实性：巨魔网络的社会足迹
Keeping it Authentic: The Social Footprint of the Trolls Network
AI总结：
本研究针对2016年俄罗斯特工社交媒体影响力网络的隐蔽干预问题，提出基于社会足迹的图谱绘制方法，通过训练AI预测模型实现88%测试准确率，经额外模型验证准确率超90%，为识别此类信息行动参与者及功能提供有效路径。
摘要：
2016年，一个由俄罗斯特工运营的社交媒体账号网络试图转移美国公众围绕总统选举的政治讨论。这是一场协同行动，属于俄罗斯主导的复杂信息行动的一部分。利用社交媒体平台的匿名性和传播范围，俄罗斯特工创建了一个在线“草根营销（astroturf）”网络，直接与普通美国人接触，宣扬俄罗斯的议程和目标。这类对抗性手段的隐蔽性让安全机构束手无策，凸显了此类干预带来的独特挑战。基于现有关于社交媒体上影响力网络内部功能的学术研究，我们提出了一种绘制这类行动图谱的新方法。我们认为，伪装成合法社会参与者的身份迫使该网络遵循社会预期，从而留下社会足迹。为测试这种社会足迹的稳健性，我们训练人工智能来识别它，并构建了一个预测模型。我们使用被确认为俄罗斯影响力网络一部分的Twitter数据来训练人工智能并测试预测效果。我们的模型在测试集上达到了88%的预测准确率。在另外两个模型上测试我们的预测方法，分别得到了90.7%和90.5%的准确率，验证了我们的模型。预测和验证结果表明，围绕俄罗斯影响力网络内部的社会功能构建机器学习模型，可用于绘制其参与者和功能的图谱。
英文摘要：
In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% prediction accuracy for the test set. Testing our prediction on two additional models results in 90.7% and 90.5% accuracy, validating our model. The predictive and validation results suggest that building a machine learning model around social functions within the Russian influence network can be used to map its actors and functions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
