An Information Diffusion-Based Recommendation Framework for Micro-Blogging
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...
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| Veröffentlicht in: | Journal of the Association for Information Systems 2011-07, Vol.12 (7), p.463-486 |
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| container_title | Journal of the Association for Information Systems |
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| creator | Cheng, Jiesi Sun, Aaron Hu, Daning Zeng, Daniel |
| description | 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. [PUBLICATION ABSTRACT] |
| doi_str_mv | 10.17705/1jais.00271 |
| format | Article |
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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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| fulltext | fulltext |
| identifier | ISSN: 1536-9323 |
| ispartof | Journal of the Association for Information Systems, 2011-07, Vol.12 (7), p.463-486 |
| issn | 1536-9323 1536-9323 |
| language | eng |
| recordid | cdi_proquest_journals_884332793 |
| source | Business Source Complete; Digital Commons Online Journals |
| subjects | Blogs Information retrieval Information systems Real time Social networks Studies Swine flu Traffic flow |
| title | An Information Diffusion-Based Recommendation Framework for Micro-Blogging |
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