Abstract
How to predict the topic diffusion is a challenging research work in social media data mining. The classical research works in Twitter and Micorblog mainly focus on diffusion links that ignore the importance of diffusion content. In this paper, we propose a Link Information Flow-based topic diffusion prediction model, which combines the link view and content view in diffusion. The experiment results show that our model achieves good performance in topic diffusion prediction.
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Acknowledgments
This work is supported by the Nature Science Foundation of China (No. 61202143 and No. 61305061), the Natural Science Foundation of Fujian Province (No. 2013J05100, No. 2010J01345 and No. 2011J01367), the Key Projects Fund of Science and Technology in Xiamen (No. 3502Z20123017), the Fundamental Research Funds for the Central Universities (No. 2013121026 and No. 2011121052), the Research Fund for the Doctoral Program of Higher Education of China (No. 201101211120024), the Special Fund for Developing Shenzhen’s Strategic Emerging Industries (No. JCYJ20120614164600201), the Hunan Provincial Natural Science Foundation (12JJ2040), and the Hunan Province Research Foundation of Education Committee(09A046).
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Lin, D., Cao, D. (2014). Blog Topic Diffusion Prediction Model Based on Link Information Flow. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_8
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DOI: https://doi.org/10.1007/978-3-642-54930-4_8
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