Zhiyu Wang
ABSTRACT
Microblog is a prominent information platform for sharing experiences, discussing current events, and exchanging ideas. Many events are first reported in social media, and increasing amounts of rich-media content are associated with the posts, making them more credible and attractive. We design a rich-media analysis system to address the important challenge of sensing and exploring events from social media in real-time. The system includes a novel bilateral correspondence topic model to extract representative content and meaningful facets about events over time. It also includes a digital magazine that anchors user interactions with event facets. We demonstrate several examples from more than 4 million rich media microblogs, showing the effectiveness of key content extraction and natrual interactions with facets.
Supplemental Material
- K. Barnard, P. Duygulu, D. Forsyth, N. De Freitas, D. Blei, and M. Jordan. Matching words and pictures. The Journal of Machine Learning Research, 3:1107--1135, 2003. Google Scholar
Digital Library
- M. Wang, R. Hong, G. Li, Z. Zha, S. Yan, and T. Chua. Event driven web video summarization by tag localization and key-shot identification. Multimedia, IEEE Transactions on, (99):1--1, 2011.Google Scholar
- J. Weng and B. Lee. Event detection in twitter. In Fifth International AAAI Conference on Weblogs and Social Media, 2011.Google Scholar
Index Terms
- Analyzing social media via event facets
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