Mapping the Russian Internet Troll Network on Twitter using a Predictive Model

Sachith Dassanayaka, Ori Swed, Dimitri Volchenkov
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Abstract

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.
利用预测模型绘制推特上的俄罗斯互联网巨魔网络图
俄罗斯网络巨魔利用虚假角色通过多个社交媒体流传播虚假信息。鉴于这种威胁在社交媒体平台上出现的频率越来越高,了解这些行动对于打击其影响力至关重要。利用被认定为俄罗斯影响力网络一部分的 Twitter 内容,我们创建了一个预测模型来绘制网络运营图。通过引入逻辑类别和训练预测模型来识别整个网络中的类似行为模式,我们根据账户子样本的真实性功能对账户类型进行了分类。我们的模型对测试集的预测准确率达到 88%。通过与 300 万条俄罗斯巨魔推文数据集的相似性比较进行验证。结果表明,两个数据集的相似度为 90.7%。此外,我们还将模型预测结果与俄罗斯推文数据集进行了比较,结果表明预测结果与实际类别之间的对应率为 90.5%。预测和验证结果表明,我们的预测模型可以帮助绘制此类网络中的行为者。
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