TY - JOUR
T1 - Building social computing system in big data
T2 - From the perspective of social network analysis
AU - Liao, Chien Hsiang
AU - Chen, Mu Yen
N1 - Funding Information:
This study was sponsored by the Ministry of Science and Technology, Taiwan ( MOST107-2410-H-025-011 , MOST107-2634-F-025-001 , MOST-106-2410-H-030-018-MY3 , MOST106-2634-F-025-001 ).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/12
Y1 - 2019/12
N2 - Recently, big data and its applications have drawn the attention of academic researchers and business professionals. However, there are still a number of potential and useful values hidden in large-scale data. For instance, the large volumes of human activity data in social media might reflect people's consumption patterns and preferences. The aim of this study is to adopt social computing to explore valuable patterns or knowledge from social structures. This study develops five algorithms by integrating the notions of anticipatory computing and social network analysis, and also designs an application interface (API) which can be utilized in big data. These analytics can be applied to develop various applications in different contexts, e.g., marketing strategies in business or disease/symptom analysis in healthcare. This study contributes to social computing and discloses intelligent patterns in the social network.
AB - Recently, big data and its applications have drawn the attention of academic researchers and business professionals. However, there are still a number of potential and useful values hidden in large-scale data. For instance, the large volumes of human activity data in social media might reflect people's consumption patterns and preferences. The aim of this study is to adopt social computing to explore valuable patterns or knowledge from social structures. This study develops five algorithms by integrating the notions of anticipatory computing and social network analysis, and also designs an application interface (API) which can be utilized in big data. These analytics can be applied to develop various applications in different contexts, e.g., marketing strategies in business or disease/symptom analysis in healthcare. This study contributes to social computing and discloses intelligent patterns in the social network.
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U2 - 10.1016/j.chb.2018.09.040
DO - 10.1016/j.chb.2018.09.040
M3 - Article
AN - SCOPUS:85072154322
SN - 0747-5632
VL - 101
SP - 457
EP - 465
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -
