TY - GEN
T1 - Collecting data of SNS user behavior to detect symptoms of excessive usage
T2 - International Conference on Ergonomics Modeling, Usability and Special Populations, AHFE 2016
AU - Intapong, Ploypailin
AU - Achalakul, Tiranee
AU - Ohkura, Michiko
PY - 2017
Y1 - 2017
N2 - Worldwide use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. However, many studies have warned about the negative consequences of excessive SNS usage, including the potential of addictive behavior. Therefore, detecting the symptoms of excessive SNS usage is necessary. Data collection is an important first step for analyzing the usage behavior of SNSs. This article describes the development of a data collection application. We employed questionnaires to gather user experiences of SNS and APIs to retrieve SNS data by focusing on Twitter and Facebook. Unfortunately, these methods are limited. Self-report data might be inaccurate. Also, some data on SNSs might not be collectable by APIs. Thus, we will collect more data from internet service providers (ISPs). The obtained data from our application will be applied to detect the symptoms of excessive use of SNSs and develop prevention strategies.
AB - Worldwide use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. However, many studies have warned about the negative consequences of excessive SNS usage, including the potential of addictive behavior. Therefore, detecting the symptoms of excessive SNS usage is necessary. Data collection is an important first step for analyzing the usage behavior of SNSs. This article describes the development of a data collection application. We employed questionnaires to gather user experiences of SNS and APIs to retrieve SNS data by focusing on Twitter and Facebook. Unfortunately, these methods are limited. Self-report data might be inaccurate. Also, some data on SNSs might not be collectable by APIs. Thus, we will collect more data from internet service providers (ISPs). The obtained data from our application will be applied to detect the symptoms of excessive use of SNSs and develop prevention strategies.
KW - Social network addiction
KW - Social networking sites
KW - User behavior
UR - http://www.scopus.com/inward/record.url?scp=84992645537&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992645537&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-41685-4_9
DO - 10.1007/978-3-319-41685-4_9
M3 - Conference contribution
AN - SCOPUS:84992645537
SN - 9783319416847
VL - 486
T3 - Advances in Intelligent Systems and Computing
SP - 89
EP - 99
BT - Advances in Ergonomics Modeling, Usability
PB - Springer Verlag
Y2 - 27 July 2016 through 31 July 2016
ER -