A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction

Cascade prediction; Influence; Information diffusion; Popularity prediction; Social media; Cascade representation; Information diffusion process; Computer Science Applications; Computer Networks and Communications

Abstract :

[en] We propose a new deep learning cascade prediction model CasSIM that can simultaneously achieve two most demanded objectives: popularity prediction and final adopter prediction. Compared to existing methods based on cascade representation, CasSIM simulates information diffusion processes by exploring users’ dual roles in information propagation with three basic factors: users’ susceptibilities, influences and message contents. With effective user profiling, we are the first to capture the topic-specific property of susceptibilities and influences. In addition, the use of graph neural networks allows CasSIM to capture the dynamics of susceptibilities and influences during information diffusion. We evaluate the effectiveness of CasSIM on three real-life datasets and the results show that CasSIM outperforms the state-of-the-art methods in popularity and final adopter prediction.

Disciplines :

Computer science

Author, co-author :

CHEN, Ninghan ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Computer Science > Team Jun PANG

CHEN, Xihui ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

ZHONG, Zhiqiang ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Computer Science > Team Jun PANG ; Faculty of Natural Sciences, Aarhus University, Aarhus, Denmark

PANG, Jun ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

External co-authors :

yes

Language :

English

Title :

A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction

Publication date :

2024

Journal title :

Data Mining and Knowledge Discovery

ISSN :

1384-5810

eISSN :

1573-756X

Publisher :

Springer

Volume :

Issue :

Pages :

79 - 109

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://link.springer.com/content/pdf/10.1007/s10618-023-00953-5.pdf

FnR Project :

FNR16281848 - Give Control Back To Users: Personalised Privacy-preserving Data Aggregation From Heterogeneous Social Graphs, 2021 (01/04/2022-31/03/2025) - Sjouke Mauw
FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian

Funders :

Fonds National de la Recherche Luxembourg

Funding text :

This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference C21/IS/16281848 (HETERS) and PRIDE17/12252781 (DRIVEN). For the purpose of open access, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.

Available on ORBilu :

since 25 January 2024

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