To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. Our model’s limited performance in long-range predictions highlights the challenges faced by generative models due to the accumulation of errors.

我们可以在多大程度上预测在线对话树的结构？我们提出了一种生成模型，通过结合机器学习算法来预测社交媒体上线程对话的大小和演变。使用跨越两个主题域（加密货币和网络安全）和两个平台（Reddit和Twitter）的数据集对模型进行评估。我们表明，它能够以中等精度预测最终树木的宏观特征和近期的微观事件。但是，预测对话的宏观结构并不能保证其微观演变的准确重建。我们的模型在长期预测中的局限性凸显了生成模型由于错误的累积而面临的挑战。