On the challenges of predicting microscopic dynamics of online conversations

Applied Network Science

Pik-Mai Hui¹  Yong-Yeol Ahn¹  Diogo Pacheco¹  John Bollenbacher¹  Alessandro Flammini¹  Filippo Menczer¹ 

[1] Center for Complex Networks and Systems Research, Indiana University, Bloomington, USA;

关键词: Information cascades;    Conversation trees;    Prediction;    Social media;    Machine learning;   

DOI  :  10.1007/s41109-021-00357-8

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY