An information diffusion-based recommendation framework for micro-blogging

Jiesi Cheng, Aaron Sun, Daning Hu, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches.

Original languageEnglish (US)
Pages (from-to)463-486
Number of pages24
JournalJournal of the Association for Information Systems
Volume12
Issue number7
StatePublished - Jan 1 2011

Keywords

  • Information diffusion
  • Micro-blogging
  • Recommender system

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications

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