On the challenges of predicting microscopic dynamics of online conversations
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 15, 2021
- Source ID
- 10.1007/s41109-021-00357-8
Entities
People
- Alessandro Flammini
- Diogo Pacheco
- Filippo Menczer
- John Bollenbacher
- Pik-mai Hui
- Yong-Yeol Ahn
Organizations
- Defense Advanced Research Projects Agency