Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs

Ploypailin Intapong, Saromporn Charoenpit, Tiranee Achalakul, Michiko Ohkura

Research output: ResearchConference contribution

  • 1 Citations

Abstract

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.

LanguageEnglish
Title of host publicationSocial Computing and Social Media
Subtitle of host publicationHuman Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings
PublisherSpringer Verlag
Pages71-83
Number of pages13
Volume10282 LNCS
ISBN (Print)9783319585581
DOIs
StatePublished - 2017
Event9th 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 - Vancouver, Canada
Duration: 2017 Jul 92017 Jul 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10282 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th 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
CountryCanada
CityVancouver
Period17/7/917/7/14

Fingerprint

Application programming interfaces (API)

Keywords

  • SNS
  • Social network addiction
  • Social Networking Sites
  • User behavior

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Intapong, P., Charoenpit, S., Achalakul, T., & Ohkura, M. (2017). Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs. In Social Computing and Social Media: Human Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings (Vol. 10282 LNCS, pp. 71-83). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10282 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-58559-8_7

Assessing symptoms of excessive sns usage based on user behavior and emotion : Analysis of data obtained by sns APIs. / Intapong, Ploypailin; Charoenpit, Saromporn; Achalakul, Tiranee; Ohkura, Michiko.

Social Computing and Social Media: Human Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings. Vol. 10282 LNCS Springer Verlag, 2017. p. 71-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10282 LNCS).

Research output: ResearchConference contribution

Intapong, P, Charoenpit, S, Achalakul, T & Ohkura, M 2017, Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs. in Social Computing and Social Media: Human Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings. vol. 10282 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10282 LNCS, Springer Verlag, pp. 71-83, 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, Vancouver, Canada, 17/7/9. DOI: 10.1007/978-3-319-58559-8_7
Intapong P, Charoenpit S, Achalakul T, Ohkura M. Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs. In Social Computing and Social Media: Human Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings. Vol. 10282 LNCS. Springer Verlag. 2017. p. 71-83. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-58559-8_7
Intapong, Ploypailin ; Charoenpit, Saromporn ; Achalakul, Tiranee ; Ohkura, Michiko. / Assessing symptoms of excessive sns usage based on user behavior and emotion : Analysis of data obtained by sns APIs. Social Computing and Social Media: Human Behavior - 9th International Conference, SCSM 2017 Held as Part of HCI International 2017, Proceedings. Vol. 10282 LNCS Springer Verlag, 2017. pp. 71-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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