A novel embedding method for news diffusion prediction

Ruoran Liu, Qiudan Li, Can Wang, Lei Wang, Dajun Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

News diffusion prediction aims to predict a sequence of news sites which will quote a particular piece of news. Most of previous propagation models make efforts to estimate propagation probabilities along observed links and ignore the characteristics of news diffusion processes, and they fail to capture the implicit relationships between news sites. In this paper, we propose an algorithm to model the news diffusion processes in a continuous space and take the attributes of news into account. Experiments performed on a real-world news dataset show that our model can take advantage of news' attributes and predict news diffusion accurately.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages8111-8112
Number of pages2
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Liu, R., Li, Q., Wang, C., Wang, L., & Zeng, D. (2018). A novel embedding method for news diffusion prediction. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 8111-8112). AAAI press.