Dynamic Seed Analysis in a Social Network for Maximizing Efficiency of Data Collection

Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteri...

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Vydané v:	2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing s. 132 - 136
Hlavní autori:	Changhyun Byun, Hyeoncheol Lee, Jongsung You, Yanggon Kim
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2013
Predmet:	Algorithm design and analysis Data mining Heuristic algorithms Knowledge discovery Media social networks; twitter; seed analysis; initial nodes; crawling; presidential election 2012 Twitter
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Shrnutí:	Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes dynamically during the gathering process. Furthermore, we compare two data sets collected by the algorithm and existing approaches.
DOI:	10.1109/SNPD.2013.45