Search

Your search keyword '"Pik-Mai Hui"' showing total 2 results
Start Over Author "Pik-Mai Hui" Journal applied network science
2 results on '"Pik-Mai Hui"'

1. On the challenges of predicting microscopic dynamics of online conversations

Author

John Bollenbacher, Diogo Pacheco, Pik-Mai Hui, Yong-Yeol Ahn, Alessandro Flammini, and Filippo Menczer

Subjects
Information cascades, Conversation trees, Prediction, Social media, Machine learning, Applied mathematics. Quantitative methods, T57-57.97
Abstract

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.

Published
2021
Full Text
View/download PDF

2. On the challenges of predicting microscopic dynamics of online conversations

Author

Alessandro Flammini, John Bollenbacher, Pik-Mai Hui, Diogo Pacheco, Filippo Menczer, and Yong-Yeol Ahn

Subjects
Cryptocurrency, Computer Networks and Communications, Computer science, media_common.quotation_subject, Information cascades, 02 engineering and technology, Machine learning, computer.software_genre, Social media, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Conversation, media_common, Structure (mathematical logic), Multidisciplinary, business.industry, lcsh:T57-57.97, Conversation trees, Computational Mathematics, Generative model, Dynamics (music), lcsh:Applied mathematics. Quantitative methods, 020201 artificial intelligence & image processing, Artificial intelligence, business, Prediction, computer, Generative grammar
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.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results