Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-60493
Chen, Jiesi; Sun, Runpu; Hu, Daning; Zeng, Dajun (2011). An information diffusion-based recommendation framework for micro-blogging. Journal of the Association for Information Systems, 12(7):463-486.
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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.
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|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||03 Faculty of Economics > Department of Informatics|
|Dewey Decimal Classification:||000 Computer science, knowledge & systems|
|Deposited On:||21 Mar 2012 10:26|
|Last Modified:||05 Apr 2016 15:42|
|Publisher:||Association for Information Systems|
|Other Identification Number:||merlin-id:6373|
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