short-paper

Assessing symptoms of excessive SNS usage based on user behavior and emotion

Authors:

Ploypailin Intapong,

Tipporn Laohakangvalvit,

Tiranee Achalakul,

Michiko OhkuraAuthors Info & Claims

ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction

Pages 559 - 562

https://doi.org/10.1145/2993148.2997620

Published: 31 October 2016 Publication History

Abstract

The 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 issued warnings about the negative consequences of excessive SNS usage, including the risk of addictive behavior. This research is conducted to detect the symptoms of excessive SNS use by studying user behaviors and emotions in SNSs. We employed questionnaires, SNS APIs, and biological signals as methods. The data obtained from the study will characterize SNS usage to detect excessive use. Finally, the analytic results will be applied for developing prevention strategies to increase the awareness of the risks of excessive SNS usage.

References

[1]

Kuss, D. J. and Griffiths, M. D. 2011. Online social networking and addiction-A review of the psychological literature. International Journal of Environmental Research and Public Health. 8, 9: 3528-3552.

[2]

We Are Social. 2016. Retrieved January 1, 2016 from http://wearesocial.net/

[3]

Poh, A., Cheak, C., Guan, G. and Goh, G. 2012. Online social networking addiction: exploring its relationship with social networking dependency and mood modification among undergraduates in Malaysia. In International Conference on Management, Economics and Finance (Sarawak, Malaysia), 247–262.

[4]

Al-Menayes, J. 2015. Dimensions of social media addiction among university students in Kuwait. Psychology and Behavioral Sciences. 4, 1: 23-28.

[5]

Phanasathit, M., Manwong, M., Hanprathet, N., Khumsri, J. and Yingyeun, R. 2015. Validation of the Thai version of Bergen Facebook Addiction Scale (Thai-BFAS). Journal of the Medical Association of Thailand=Chotmaihet thangphaet. 98: S108-S117.

[6]

Andreassen, C. S., Torsheim, T., Brunborg, G. S. and Pallesen, S. 2012. Development of a Facebook addiction scale. Psychological Reports. 110, 2: 501–517.

[7]

Abdesslem, F., Parris, I. and Henderson, T. 2012. Reliable online social network data collection. Computational Social Networks, Springer London. 183–210.

[8]

Burke, M., Marlow, C. and Lento, T. 2010. Social network activity and social well-being. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM.

Digital Library

[9]

1909–1912.

[10]

Geisel, O., Panneck, P., Stickel, A., Schneider, M. and Christian A. Müller. 2015. Characteristics of social network gamers: Results of an online survey. Frontiers in Psychiatry. 6: 1–5.

[11]

Sagioglou, C. and Greitemeyer, T. 2014. Facebook’s emotional consequences: Why Facebook causes a decrease in mood and why people still use it. Computers in Human Behavior. 35: 359–363.

[12]

Junco, R. 2013. Comparing actual and self-reported measures of Facebook use. Computers in Human Behavior. 29, 3: 626– 631.

Digital Library

[13]

Alfantoukh, L. and Durresi, A. 2014. Techniques for collecting data in social networks. In Network-based Information Systems (NBis), 2014 17th International Conference on. IEEE. 336–341.

Digital Library

[14]

Petrillo, U. F. and Consolo, S. 2014. A framework for the efficient collection of big Data from online social networks. Intelligent Networking and Collaborative Systems (INCos), 2014 International Conference on. IEEE. 34–41.

Digital Library

[15]

Facebook Developers. 2015. Retrieved from https://developers.facebook.com/

[16]

Twitter Developers. 2015. Retrieved from https://dev.twitter.com/

[17]

Benevenuto, F., Rodrigues, T., Cha, M and Almeida, V. 2009. Characterizing user behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. 49–62.

Digital Library

[18]

Virmani, C., Pillai, A. and Juneja, D. 2014. Study and analysis of social network aggregator. Optimization, Reliability, and Information Technology (ICROIT), 2014 International Conference on. IEEE. 145–148.

[19]

Schneider, F., Feldmann, A., Krishnamurthy, B. and Willinger, W. 2009. Understanding online social network usage from a network perspective. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, ACM. 35-48.

Digital Library

[20]

Young, K. 1998. Internet Addiction: The Emergence of a New Clinical Disorder. CyberPsychology & Behavior. 1,3: 237–244.

[21]

Intapong, P., Achalakul, T. and Ohkura, M. 2016. 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 (Tokyo, Japan), ISASE’16. 1-7.

[22]

Intapong, P., Achalakul, T. and Ohkura, M. 2016. Collecting Data of SNS User Behavior to Detect Symptoms of Excessive Usage: Development of Data Collection Application. Advances in Ergonomics Modeling, Usability & Special Populations, 468, 88-99.

[23]

Intapong, P., Achalakul, T. and Ohkura, M. 2016. 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 (Bangkok, Thailand), ICBIR’16. 163-168

[24]

Griffiths, M. 2005. A Components Model of Addiction Within a Biopsychosocial Framework. Journal of Substance Use. 10, 4: 191-197.

Cited By

Intapong PCharoenpit SAchalakul TOhkura M(2017)Assessing Symptoms of Excessive SNS Usage Based on User Behavior and EmotionSocial Computing and Social Media. Human Behavior10.1007/978-3-319-58559-8_7(71-83)Online publication date: 13-May-2017
https://doi.org/10.1007/978-3-319-58559-8_7

Index Terms

Assessing symptoms of excessive SNS usage based on user behavior and emotion
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites

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Published In

cover image ACM Conferences

ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction

October 2016

605 pages

ISBN:9781450345569

DOI:10.1145/2993148

General Chairs:
Yukiko I. Nakano
Seikei University, Japan
,
Elisabeth André
Augsburg University, Germany
,
Toyoaki Nishida
Kyoto University, Japan
,
Program Chairs:
Louis-Philippe Morency
Carnegie Mellon University, USA
,
Carlos Busso
University of Texas at Dallas, USA
,
Catherine Pelachaud
ISIR, France / University of Paris6, France

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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ICMI '16

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SIGCHI

ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 12 - 16, 2016

Tokyo, Japan

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Intapong PCharoenpit SAchalakul TOhkura M(2017)Assessing Symptoms of Excessive SNS Usage Based on User Behavior and EmotionSocial Computing and Social Media. Human Behavior10.1007/978-3-319-58559-8_7(71-83)Online publication date: 13-May-2017
https://doi.org/10.1007/978-3-319-58559-8_7

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