Skip to main content

Advertisement

SpringerLink
Log in

Dynamic simulation of social media challenge participation to examine intervention strategies

  • Research Article
  • Published:
Journal of Computational Social Science Aims and scope Submit manuscript

Abstract

Recently, the use of social media by adolescents and young adults has significantly increased. While this new landscape of cyberspace offers young Internet users many benefits, it also exposes them to numerous risks. One such phenomenon receiving limited research attention is the advent and propagation of viral social media challenges. Several of these challenges entail self-harming behavior, which combined with their viral nature, poses physical and psychological risks for the participants and the viewers. In this paper, we show how agent-based modeling (ABM) can be used to investigate the effect of educational intervention programs to reduce participation in social media challenges at multiple levels—family, school, and community. In addition, we show how the effect of these education-based interventions can be compared to social media-based policy interventions. Our model takes into account the “word of mouth” effect of these interventions which could either decrease participation in social media challenge further than expected or unintentionally cause others to participate. We suggest that educational interventions at combined family and school levels are the most efficient type of long-term intervention, since they target the root of the problem, while social media-based policies act as a retrospective solution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. FastStats—Suicide and Self-Inflicted Injury. (n.d.). https://www.cdc.gov/nchs/fastats/suicide.htm (accessed July 20, 2021).

  2. Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science., 6, 3–17. https://doi.org/10.1177/2167702617723376

    Article  Google Scholar 

  3. NIMH » Suicide. (n.d.). https://www.nimh.nih.gov/health/statistics/suicide (accessed July 20, 2021).

  4. Mitchell, K. J., Wells, M., Priebe, G., & Ybarra, M. L. (2014). Exposure to websites that encourage self-harm and suicide: Prevalence rates and association with actual thoughts of self-harm and thoughts of suicide in the United States. Journal of Adolescence, 37, 1335–1344. https://doi.org/10.1016/j.adolescence.2014.09.011

    Article  Google Scholar 

  5. Zhu, L., Westers, N. J., Horton, S. E., King, J. D., Diederich, A., Stewart, S. M., & Kennard, B. D. (2016). Frequency of exposure to and engagement in nonsuicidal self-injury among inpatient adolescents. Archives of Suicide Research, 20, 580–590. https://doi.org/10.1080/13811118.2016.1162240

    Article  Google Scholar 

  6. Pater, J., Mynatt, E. (2017). Defining digital self-harm. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing—CSCW’17 (pp. 1501–1513). ACM Press. https://doi.org/10.1145/2998181.2998224

  7. Rachmi, C. N., Agho, K. E., Li, M., & Baur, L. A. (2016). Stunting, underweight and overweight in children aged 2.0–4.9 years in Indonesia: Prevalence trends and associated risk factors. PLoS ONE, 11, e0154756. https://doi.org/10.1371/journal.pone.0154756

    Article  Google Scholar 

  8. Luxton, D. D., June, J. D., & Fairall, J. M. (2012). Social media and suicide: A public health perspective. American Journal of Public Health, 102(Suppl 2), S195–S200. https://doi.org/10.2105/AJPH.2011.300608

    Article  Google Scholar 

  9. Grant-Alfieri, A., Schaechter, J., & Lipshultz, S. E. (2013). Ingesting and aspirating dry cinnamon by children and adolescents: The “cinnamon challenge.” Pediatrics, 131, 833–835. https://doi.org/10.1542/peds.2012-3418

    Article  Google Scholar 

  10. 48-Hour Challenge: Is the Disappearing “Game” a Hoax? | Miami Herald. (n.d.). https://www.miamiherald.com/news/nation-world/national/article180181516.html (accessed July 20, 2021).

  11. Social Media Contests: A Timeline of Viral Challenges. (n.d.). https://growingsocialmedia.com/social-media-contests-a-timeline-of-viral-challenges/ (accessed July 20, 2021).

  12. Khasawneh, A., Madathil, K. C., Zinzow, H., Rosopa, P., Natarajan, G., Achuthan, K., & Narasimhan, M. (2021). Factors contributing to adolescents’ and young adults’ participation in web-based challenges: Survey study. JMIR Pediatrics and Parenting, 4, e24988. https://doi.org/10.2196/24988

    Article  Google Scholar 

  13. Khasawneh, A., Madathil, K. C., Zinzow, H., Wisniewski, P., Ponathil, A., Rogers, H., Agnisarman, S., Roth, R., & Narasimhan, M. (2021). An investigation of the portrayal of social media challenges on youtube and twitter. Transactions on Social Computational, 4, 1–23. https://doi.org/10.1145/3444961

    Article  Google Scholar 

  14. ALS Ice Bucket Challenge Commitments | The ALS Association. (n.d.). https://www.als.org/ice-bucket-challenge-spending (accessed July 20, 2021).

  15. Khasawneh, A., Madathil, K. C., Dixon, E., Wisniewski, P., Zinzow, H., & Roth, R. (2019). An investigation on the portrayal of blue whale challenge on Youtube and twitter. Proceedings of the Human Factors and Ergonomics Society Annual Meeting., 63, 887–888. https://doi.org/10.1177/1071181319631179

    Article  Google Scholar 

  16. Khasawneh, A., Madathil, K. C., Dixon, E., Wiśniewski, P., Zinzow, H., & Roth, R. (2020). Examining the self-harm and suicide contagion effects of the blue whale challenge on YouTube and Twitter: Qualitative study. JMIR Mental Health, 7, e15973. https://doi.org/10.2196/15973

    Article  Google Scholar 

  17. Roth, R., Abraham, J., Zinzow, H., Wisniewski, P., Khasawneh, A., & Madathil, K. C. (2020). Evaluating news media reports on the “blue whale challenge” for adherence to suicide prevention safe messaging guidelines. Proceedings of the ACM on Human–Computer Interaction, 4, 1–27. https://doi.org/10.1145/3392831

    Article  Google Scholar 

  18. Tide Pod Challenge: Tide Tells People to Call Poison Control | Time. (n.d.). https://time.com/5108478/tide-pod-challenge-poison-control/ (accessed July 20, 2021).

  19. P.M.C. Europe, Europe PMC. (n.d.).

  20. Khasawneh, A., Narasimha, S., Madathil, K. C., Zinzow, H., & Rosopa, P. (2020). An investigation of the factors predicting participation in social media challenge. Proceedings of the Human Factors and Ergonomics Society Annual Meeting., 64, 810–811. https://doi.org/10.1177/1071181320641188

    Article  Google Scholar 

  21. Mukhra, R., Baryah, N., Krishan, K., & Kanchan, T. (2019). “blue whale challenge”: A game or crime? Science and Engineering Ethics, 25, 285–291. https://doi.org/10.1007/s11948-017-0004-2

    Article  Google Scholar 

  22. Agnisarman, S., Madathil, K. C., Khasawneh, A., Ponathil, A., Lopes, S., Piratla, K., & Gajjar, D. (2022). Sensemaking perspective on infrastructure risk-related mental model development of windstorm loss prevention engineers. Journal of Building Engineering., 52, 104314. https://doi.org/10.1016/j.jobe.2022.104314

    Article  Google Scholar 

  23. Publications Database—Center for Public Health Practice—Minnesota Dept. of Health. (n.d.). https://www.health.state.mn.us/communities/practice/resources/publications/ (accessed July 20, 2021).

  24. Ponathil, A. P., Khasawneh, A., Byrne, K., & Madathil, K. C. (2020). Factors affecting the choice of a dental care provider by older adults based on online consumer reviews. IISE Transactions on Healthcare Systems Engineering. https://doi.org/10.1080/24725579.2020.1854394

    Article  Google Scholar 

  25. Friedman, J., Cosby, R., Boyko, S., Hatton-Bauer, G., Turnbull, Panel, T. (n.d.). Effective teaching strategies and methods of delivery for patient education.

  26. Friedman, A. J., Cosby, R., Boyko, S., Hatton-Bauer, J., & Turnbull, G. (2011). Effective teaching strategies and methods of delivery for patient education: A systematic review and practice guideline recommendations. Journal of Cancer Education, 26, 12–21. https://doi.org/10.1007/s13187-010-0183-x

    Article  Google Scholar 

  27. Keller, L. O., Strohschein, S., Lia-Hoagberg, B., & Schaffer, M. A. (2004). Population-based public health interventions: Practice-based and evidence-supported. Part I. Public Health Nurse, 21, 453–468. https://doi.org/10.1111/j.0737-1209.2004.21509.x

    Article  Google Scholar 

  28. MADD | Mothers Against Drunk Driving. (n.d.). https://www.madd.org/ (accessed July 20, 2021).

  29. Wang, S., Wang, J., Wang, X., Qiu, T., Yuan, Y., Ouyang, L., Guo, Y., & Wang, F.-Y. (2018). Blockchain-powered parallel healthcare systems based on the ACP approach. IEEE Transactions on Computational Social Systems, 5, 942–950. https://doi.org/10.1109/TCSS.2018.2865526

    Article  Google Scholar 

  30. Ponathil, A., Khasawneh, A., Piratla, K., Hegde, S., Sharma, V., & Madathil, K. C. (2021). Does construction service provider’s response matter? Understanding the influence of anecdotal information on online consumer decisions. Developments in the Built Environment. https://doi.org/10.1016/j.dibe.2021.100061

    Article  Google Scholar 

  31. Lee, B. Y., Brown, S. T., Bailey, R. R., Zimmerman, R. K., Potter, M. A., McGlone, S. M., Cooley, P. C., Grefenstette, J. J., Zimmer, S. M., Wheaton, W. D., Quinn, S. C., Voorhees, R. E., & Burke, D. S. (2011). The benefits to all of ensuring equal and timely access to influenza vaccines in poor communities. Health Aff (Millwood)., 30, 1141–1150. https://doi.org/10.1377/hlthaff.2010.0778

    Article  Google Scholar 

  32. Joseph, A., Madathil, K. C., Jafarifiroozabadi, R., Rogers, H., Mihandoust, S., Khasawneh, A., McNeese, N., Holmstedt, C., & McElligott, J. T. (2022). Communication and teamwork during telemedicine-enabled stroke care in an ambulance. Human Factors, 64, 21–41. https://doi.org/10.1177/0018720821995687

    Article  Google Scholar 

  33. Epstein, J. M. (2002). Modeling civil violence: An agent-based computational approach. Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl 3), 7243–7250. https://doi.org/10.1073/pnas.092080199

    Article  Google Scholar 

  34. Yonas, M. A., Burke, J. G., Brown, S. T., Borrebach, J. D., Garland, R., Burke, D. S., & Grefenstette, J. J. (2013). Dynamic simulation of crime perpetration and reporting to examine community intervention strategies. Health Education and Behavior, 40, 87S-97S. https://doi.org/10.1177/1090198113493090

    Article  Google Scholar 

  35. Tyshchuk, Y., & Wallace, W. A. (2018). Modeling human behavior on social media in response to significant events. IEEE Transactions on Computational Social Systems, 5, 444–457. https://doi.org/10.1109/TCSS.2018.2815786

    Article  Google Scholar 

  36. Glenski, M., Weninger, T., & Volkova, S. (2018). Propagation from deceptive news sources who shares, how much, how evenly, and how quickly? IEEE Transactions on Computational Social Systems, 5, 1071–1082. https://doi.org/10.1109/TCSS.2018.2881071

    Article  Google Scholar 

  37. AnyLogic: Simulation Modeling Software Tools & Solutions for Business. (n.d.). https://www.anylogic.com/ (accessed July 21, 2021).

  38. Health Behavior: Theory, Research, and Practice—Google Books. (n.d.).

  39. Henze, N. (1994). On Mardia’s kurtosis test for multivariate normality. Communications in Statistics—Theory and Methods., 23, 1031–1045. https://doi.org/10.1080/03610929408831303

    Article  Google Scholar 

  40. Sargent, R. G., & Balci, O. (2017). History of verification and validation of simulation models. In 2017 winter simulation conference (WSC) (pp. 292–307). IEEE. https://doi.org/10.1109/WSC.2017.8247794

  41. Kleijnen, J. P. C. (1995). Verification and validation of simulation models. European Journal of Operational Research, 82, 145–162. https://doi.org/10.1016/0377-2217(94)00016-6

    Article  Google Scholar 

  42. Sargent, R. G. (2013). Verification and validation of simulation models. Journal of Simulation, 7, 12–24. https://doi.org/10.1057/jos.2012.20

    Article  Google Scholar 

  43. Wu, Y., Stanton, B. F., Galbraith, J., Kaljee, L., Cottrell, L., Li, X., Harris, C. V., D’Alessandri, D., & Burns, J. M. (2003). Sustaining and broadening intervention impact: A longitudinal randomized trial of 3 adolescent risk reduction approaches. Pediatrics, 111, e32–e38.

    Article  Google Scholar 

  44. Rosopa, P. J., Schaffer, M. M., & Schroeder, A. N. (2013). Managing heteroscedasticity in general linear models. Psychological Methods, 18, 335–351. https://doi.org/10.1037/a0032553

    Article  Google Scholar 

  45. Ng, J. Y.-H., Yang, F., & Davis, L. S. (2015). Exploiting local features from deep networks for image retrieval. In 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 53–61). IEEE. https://doi.org/10.1109/CVPRW.2015.7301272

  46. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. ArXiv.

  47. Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In 2015 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 427–436). IEEE. https://doi.org/10.1109/CVPR.2015.7298640

  48. P.M.C. Europe,—Abstract—Europe PMC. (n.d.).

  49. Silverman, B. G., Hanrahan, N., Bharathy, G., Gordon, K., & Johnson, D. (2015). A systems approach to healthcare: Agent-based modeling, community mental health, and population well-being. Artificial Intelligence in Medicine, 63, 61–71. https://doi.org/10.1016/j.artmed.2014.08.006

    Article  Google Scholar 

  50. Nikolov, D., Oliveira, D. F. M., Flammini, A., & Menczer, F. (2015). Measuring online social bubbles. PeerJ Computer Science, 1, e38. https://doi.org/10.7717/peerj-cs.38

    Article  Google Scholar 

  51. APA PsycNet. (n.d.). https://psycnet.apa.org/buy/2009-09537-004 (accessed July 21, 2021).

  52. Kastenmüller, A., Fischer, P., Jonas, E., Greitemeyer, T., Frey, D., Köppl, J., & Aydin, N. (2009). Selective exposure: The impact of framing information search instructions as gains and losses. European Journal of Social Psychology. https://doi.org/10.1002/ejsp.653

    Article  Google Scholar 

  53. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Political science. Exposure to ideologically diverse news and opinion on Facebook. Science, 348, 1130–1132. https://doi.org/10.1126/science.aaa1160

    Article  Google Scholar 

  54. Safe and Effective Messaging and Reporting | Suicide Prevention Resource Center. (n.d.). https://sprc.org/keys-success/safe-messaging-reporting (accessed July 21, 2021).

Download references

Acknowledgements

This material is based on work supported by the National Science Foundation under Grant No. 1832904.

Author information

Authors and Affiliations

  1. Industrial Engineering Department, School of Engineering, Mercer University, Macon, GA, USA

    Amro Khasawneh

  2. Glenn Department of Civil Engineering, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, USA

    Kapil Chalil Madathil

  3. Industrial Engineering Department, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, USA

    Kapil Chalil Madathil & Kevin M. Taaffe

  4. Department of Psychology, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC, USA

    Heidi Zinzow & Patrick J. Rosopa

  5. Glenn Department of Civil Engineering, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, USA

    Amal Ponathil

  6. Systems Science and Industrial Engineering Department, Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Sreenath Chalil Madathil

  7. North Carolina State University, Raleigh, NC, USA

    Siddhartha Nambiar

  8. Purdue University, West Lafayette, IN, USA

    Gaurav Nanda

Authors
  1. Amro Khasawneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kapil Chalil Madathil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin M. Taaffe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heidi Zinzow
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amal Ponathil
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sreenath Chalil Madathil
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Siddhartha Nambiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gaurav Nanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Patrick J. Rosopa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amro Khasawneh.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khasawneh, A., Chalil Madathil, K., Taaffe, K.M. et al. Dynamic simulation of social media challenge participation to examine intervention strategies. J Comput Soc Sc 5, 1637–1662 (2022). https://doi.org/10.1007/s42001-022-00183-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42001-022-00183-7

Keywords

Search

Navigation