Abstract
Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people’s work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.
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Moradianzadeh, P., Mohi, M., Sadighi Moshkenani, M. (2008). Digital Social Network Mining for Topic Discovery. In: Sarbazi-Azad, H., Parhami, B., Miremadi, SG., Hessabi, S. (eds) Advances in Computer Science and Engineering. CSICC 2008. Communications in Computer and Information Science, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89985-3_152
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DOI: https://doi.org/10.1007/978-3-540-89985-3_152
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89984-6
Online ISBN: 978-3-540-89985-3
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