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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing s. 132 - 136
Hlavní autoři:	Changhyun Byun, Hyeoncheol Lee, Jongsung You, Yanggon Kim
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.07.2013
Témata:	Algorithm design and analysis Data mining Heuristic algorithms Knowledge discovery Media social networks; twitter; seed analysis; initial nodes; crawling; presidential election 2012 Twitter
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
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