TY - GEN
T1 - Assessing symptoms of excessive sns usage based on user behavior and emotion
T2 - 9th International Conference on Social Computing and Social Media, SCSM 2017 held as part of the 19th International Conference on Human-Computer Interaction, HCI International 2017
AU - Intapong, Ploypailin
AU - Charoenpit, Saromporn
AU - Achalakul, Tiranee
AU - Ohkura, Michiko
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Excessive and compulsive use of them has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We designed a data collection application and developed a tool for collecting data from questionnaires and SNSs by APIs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. We introduce our analysis of data obtained by SNS APIs by focusing on Facebook and Twitter. We used modified IAT and BFAS to measure SNS addiction. The Facebook and Twitter results, including a combination with questionnaires, were analyzed to identify the factors associated with SNS addiction. Our analytic results identified potential candidates of the key components of SNS addiction.
AB - The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Excessive and compulsive use of them has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We designed a data collection application and developed a tool for collecting data from questionnaires and SNSs by APIs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. We introduce our analysis of data obtained by SNS APIs by focusing on Facebook and Twitter. We used modified IAT and BFAS to measure SNS addiction. The Facebook and Twitter results, including a combination with questionnaires, were analyzed to identify the factors associated with SNS addiction. Our analytic results identified potential candidates of the key components of SNS addiction.
KW - SNS
KW - Social Networking Sites
KW - Social network addiction
KW - User behavior
UR - http://www.scopus.com/inward/record.url?scp=85019732771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019732771&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58559-8_7
DO - 10.1007/978-3-319-58559-8_7
M3 - Conference contribution
AN - SCOPUS:85019732771
SN - 9783319585581
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 83
BT - Social Computing and Social Media
A2 - Meiselwitz, Gabriele
PB - Springer Verlag
Y2 - 9 July 2017 through 14 July 2017
ER -