Abstract
Online social media exhibits massive social event relevant messages. Some of them contain useful and meaningful information, while others might not worth reading. In this paper, for a given social event, we focus on extracting high quality information from massive social media messages, since the extracted information has valuable textual content, and is widely propagated and posted by authority. We propose an extraction framework to get high quality information by considering different features globally in social media. Specially, in order to reduce computing time and improve extraction precision, some important social media features are employed and transformed into wavelet domain and fused further, to get a weighted ensemble value. A large scale of Sina microblog dataset is used to evaluate the framework’s performance. Experimental results show that the proposed framework is effective to extract high quality information.
This material is based on the work supported by National Science Foundation of China (NSFC) under Award 61070083 as well as the Key Technologies R&D Program of Wuhan under Award 201210421135.
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Peng, M. et al. (2013). High Quality Microblog Extraction Based on Multiple Features Fusion and Time-Frequency Transformation. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_14
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DOI: https://doi.org/10.1007/978-3-642-41154-0_14
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