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Probabilistic Models for Social Media Mining

Probabilistic Models for Social Media Mining

Flora S. Tsai
Copyright: © 2013 |Pages: 11
ISBN13: 9781466621572|ISBN10: 1466621575|EISBN13: 9781466621589
DOI: 10.4018/978-1-4666-2157-2.ch006
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MLA

Tsai, Flora S. "Probabilistic Models for Social Media Mining." Network and Communication Technology Innovations for Web and IT Advancement, edited by Ghazi I. Alkhatib, IGI Global, 2013, pp. 95-105. https://doi.org/10.4018/978-1-4666-2157-2.ch006

APA

Tsai, F. S. (2013). Probabilistic Models for Social Media Mining. In G. Alkhatib (Ed.), Network and Communication Technology Innovations for Web and IT Advancement (pp. 95-105). IGI Global. https://doi.org/10.4018/978-1-4666-2157-2.ch006

Chicago

Tsai, Flora S. "Probabilistic Models for Social Media Mining." In Network and Communication Technology Innovations for Web and IT Advancement, edited by Ghazi I. Alkhatib, 95-105. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2157-2.ch006

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Abstract

This paper proposes probabilistic models for social media mining based on the multiple attributes of social media content, bloggers, and links. The authors present a unique social media classification framework that computes the normalized document-topic matrix. After comparing the results for social media classification on real-world data, the authors find that the model outperforms the other techniques in terms of overall precision and recall. The results demonstrate that additional information contained in social media attributes can improve classification and retrieval results.

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