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
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10282)

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.

Keywords

Social Networking Sites SNS Social network addiction User behavior 
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Notes

Acknowledgements

We thank Pannee Lumwanwong and Sirirat Weerachatyanukul, the lecture of School of Information Technology and Innovation, Bangkok University, Thailand for IAT-Thai version. We also thank the students of Thai-Nichi Institute of Technology for the participants

References

  1. 1.
    Kuss, D.J., Griffiths, M.D.: Online social networking and addiction-a review of the psychological literature. Int. J. Environ. Res. Public Health 8(9), 3528–3552 (2011)CrossRefGoogle Scholar
  2. 2.
    We Are Social (2016). http://wearesocial.net
  3. 3.
    Electronic Transactions Development Agency (ETDA), Ministry of Digital Economy and Society, Thailand. Thailand Internet User Profile (2016). https://www.etda.or.th/publishing-detail/thailand-internet-user-profile-2016-th.html
  4. 4.
    Intapong, P., Achalakul, T., Ohkura, M.: Collecting data of SNS user behavior to detect symptoms of excessive usage: design of data collection application. In: International Symposium on Affective Science and Engineering (ISASE), pp. 1–7 (2016)Google Scholar
  5. 5.
    Intapong, P., Achalakul, T., Ohkura, M.: Collecting data of SNS user behavior to detect symptoms of excessive usage: development of data collection application. In: Soares, M., Falcão, C., Ahram, T. (eds.) Advances in Ergonomics Modeling, Usability & Special Populations, vol. 468, pp. 88–99. Springer, Cham (2016)Google Scholar
  6. 6.
    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 questionnaire. In: International Symposium on Affective Science and Engineering (ISASE) (2016, in press)Google Scholar
  7. 7.
    Andreassen, C.S.: Online social network site addiction: a comprehensive review. Curr. Addict. Rep. 2(2), 175–184 (2015)CrossRefGoogle Scholar
  8. 8.
    Young, K.: The research and controversy surrounding internet addiction. Cyberpshchol. Behav. 2, 381–383 (1999)CrossRefGoogle Scholar
  9. 9.
    Young, K.,: Internet addiction: symptoms, evaluation, and treatment. In: Innovations in Clinical Practice: A Source Book, vol. 17, pp. 19–31 (1999)Google Scholar
  10. 10.
    Andreassen, C.S., Torsheim, T., Brunborg, G.S., Pallesen, S.: Development of a Facebook addition scale. Psychol. Rep. 110(2), 501–517 (2012)CrossRefGoogle Scholar
  11. 11.
    Young, K.: The emergence of a new clinical disorder. Cyberpshchol. Behav. 1(3), 237–244 (1998)CrossRefGoogle Scholar
  12. 12.
    Weerachatyanukul, S.: Effect of internet addiction on students’ academic performance of the second year students, faculty of business administration Bangkok University. HCU J. 18(36), 47–63 (2015)Google Scholar
  13. 13.
    Phanasathit, M., Manwong, M., Hanprathet, N., Khumsri, J., Yingyeun, R.: Validation of the Thai version of Bergen Facebook Addiction Scale (Thai-BFAS). J. Med. Assoc. Thai. = Chotmaihet Thangphaet 98, S108–S117 (2015)Google Scholar
  14. 14.
    Facebook Developers. https://developers.facebook.com/
  15. 15.
    Twitter Deveopers. https://dev.twitter.com/
  16. 16.
    Intapong, P., Achalakul, T., Ohkura, M.: Collecting data of SNS user behavior to detect symptoms of excessive usage: technique for retrieving SNS data. In: International Conference on Business and Industrial Research, pp. 275–282 (2016)Google Scholar
  17. 17.
    Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A framework for the evaluation of session reconstruction heuristics in web-usage analysis. Informs J. Comput. 15(2), 171–190 (2013)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ploypailin Intapong
    • 1
  • Saromporn Charoenpit
    • 2
  • Tiranee Achalakul
    • 3
  • Michiko Ohkura
    • 4
  1. 1.Graduate School of Engineering and ScienceShibaura Institute of TechnologyTokyoJapan
  2. 2.Faculty of Information TechnologyThai-Nichi Institute of TechnologyBangkokThailand
  3. 3.Department of Computer EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  4. 4.College of EngineeringShibaura Institute of TechnologyTokyoJapan

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