Tweet ranking based on heterogeneous networks

Hongzhao Huang, Arkaitz Zubiaga, Heng Ji, Hongbo Deng, Dong Wang, Hieu Le, Tarek Abdelzaher, Jiawei Han, Alice Leung, John Hancock, Clare Voss

Research output: Contribution to conferencePaper

Abstract

Ranking tweets is a fundamental task to make it easier to distill the vast amounts of information shared by users. In this paper, we explore the novel idea of ranking tweets on a topic using heterogeneous networks. We construct heterogeneous networks by harnessing cross-genre linkages between tweets and semantically-related web documents from formal genres, and inferring implicit links between tweets and users. To rank tweets effectively by capturing the semantics and importance of different linkages, we introduce Tri-HITS, a model to iteratively propagate ranking scores across heterogeneous networks. We show that integrating both formal genre and inferred social networks with tweet networks produces a higher-quality ranking than the tweet networks alone.

Original languageEnglish (US)
Pages1239-1256
Number of pages18
StatePublished - Dec 1 2012
Event24th International Conference on Computational Linguistics, COLING 2012 - Mumbai, India
Duration: Dec 8 2012Dec 15 2012

Other

Other24th International Conference on Computational Linguistics, COLING 2012
CountryIndia
CityMumbai
Period12/8/1212/15/12

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Keywords

  • Heterogeneous networks
  • Iterative propagation model
  • Tweet ranking

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language

Cite this

Huang, H., Zubiaga, A., Ji, H., Deng, H., Wang, D., Le, H., Abdelzaher, T., Han, J., Leung, A., Hancock, J., & Voss, C. (2012). Tweet ranking based on heterogeneous networks. 1239-1256. Paper presented at 24th International Conference on Computational Linguistics, COLING 2012, Mumbai, India.