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.
Cheng, Jiesi; Sun, Aaron; Hu, Daning; and Zeng, Daniel
"An Information Diffusion-Based Recommendation Framework for Micro-Blogging,"
Journal of the Association for Information Systems, 12(7), .
Available at: https://aisel.aisnet.org/jais/vol12/iss7/2
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