Collecting data of SNS user behavior to detect symptoms of excessive usage: Development of data collection application

Ploypailin Intapong, Tiranee Achalakul, Michiko Ohkura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • 1 Citations

Abstract

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.

LanguageEnglish
Title of host publicationAdvances in Ergonomics Modeling, Usability
PublisherSpringer Verlag
Pages89-99
Number of pages11
Volume486
ISBN (Print)9783319416847
DOIs
StatePublished - 2017
EventInternational Conference on Ergonomics Modeling, Usability and Special Populations, AHFE 2016 - Walt Disney World, United States
Duration: 2016 Jul 272016 Jul 31

Publication series

NameAdvances in Intelligent Systems and Computing
Volume486
ISSN (Print)21945357

Other

OtherInternational Conference on Ergonomics Modeling, Usability and Special Populations, AHFE 2016
CountryUnited States
CityWalt Disney World
Period16/7/2716/7/31

Fingerprint

Application programming interfaces (API)
Internet service providers

Keywords

  • Social network addiction
  • Social networking sites
  • User behavior

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Intapong, P., Achalakul, T., & Ohkura, M. (2017). Collecting data of SNS user behavior to detect symptoms of excessive usage: Development of data collection application. In Advances in Ergonomics Modeling, Usability (Vol. 486, pp. 89-99). (Advances in Intelligent Systems and Computing; Vol. 486). Springer Verlag. DOI: 10.1007/978-3-319-41685-4_9

Collecting data of SNS user behavior to detect symptoms of excessive usage : Development of data collection application. / Intapong, Ploypailin; Achalakul, Tiranee; Ohkura, Michiko.

Advances in Ergonomics Modeling, Usability. Vol. 486 Springer Verlag, 2017. p. 89-99 (Advances in Intelligent Systems and Computing; Vol. 486).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Intapong, P, Achalakul, T & Ohkura, M 2017, Collecting data of SNS user behavior to detect symptoms of excessive usage: Development of data collection application. in Advances in Ergonomics Modeling, Usability. vol. 486, Advances in Intelligent Systems and Computing, vol. 486, Springer Verlag, pp. 89-99, International Conference on Ergonomics Modeling, Usability and Special Populations, AHFE 2016, Walt Disney World, United States, 16/7/27. DOI: 10.1007/978-3-319-41685-4_9
Intapong P, Achalakul T, Ohkura M. Collecting data of SNS user behavior to detect symptoms of excessive usage: Development of data collection application. In Advances in Ergonomics Modeling, Usability. Vol. 486. Springer Verlag. 2017. p. 89-99. (Advances in Intelligent Systems and Computing). Available from, DOI: 10.1007/978-3-319-41685-4_9
Intapong, Ploypailin ; Achalakul, Tiranee ; Ohkura, Michiko. / Collecting data of SNS user behavior to detect symptoms of excessive usage : Development of data collection application. Advances in Ergonomics Modeling, Usability. Vol. 486 Springer Verlag, 2017. pp. 89-99 (Advances in Intelligent Systems and Computing).
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