MODELING THE SPREAD OF FAKE NEWS ON SOCIAL NETWORKING SITES USING THE SYSTEM DYNAMICS APPROACH
DOI:
https://doi.org/10.11113/aej.v13.19251Keywords:
System Dynamics, Social Media, Fake news, Disinformation, SimulationAbstract
The problem of false news online has continued to worsen, especially after witnessing significant events around the world unfold, such as the 2018 Cambridge Analytica scandal, COVID-19 pandemic, to the 2021 January 6th Insurrection at the US Capitol. False information online has distorted online users’ perception of the real world. As daily life is more intertwined with the digital world, false news becomes a more urgent concern because of the way it can shape public opinion. This study presents a rumor propagation model, which was based on epidemiological models, to address the spread of false news on social networking sites. The existing model was expanded on the STELLA software to consider the cognitive process of users when encountering false news, the platform in which the false news spreads, and the relationship of false news with online users. Simulations showed that Confirmation Bias, Sharing of Posts, and Algorithmic Ranking were the three critical variables of the model. It was found that possible interventions include a mix of reducing the bias of users at a wide-scale level and restructuring the SNS algorithm.
References
Schroeder, R. 2018. Media systems, digital media and politics, Social Theory after the Internet. 28–59.
McNair, B. 2017. The decline of trust in journalism, In Fake News: Falsehood, Fabrication and Fantasy in Journalism. Routledge, London.
Törnberg, P. 2018. Echo chambers and viral misinformation: Modeling fake news as complex contagion, PLoS ONE. 13(9): 1–21.
Oktaviansyah, E. and Rahman, A. 2020. Predicting hoax spread in Indonesia using SIRS model, Journal of Physics: Conference Series. 1490(1): 1-5. DOI: https://doi.org/10.1088/1742-6596/1490/1/012059.
Yao, Y., Xiao, X., Zhang, C., Dou, C., and Xia, S. 2019. Stability analysis of an SDILR model based on rumor recurrence on social media, Physica A: Statistical Mechanics and Its Applications. 535: 122236. DOI: https://doi.org/10.1016/j.physa.2019.122236.
Campan, A., Cuzzocrea, A. and Truta, T. 2017. Fighting fake news spread in online social networks: actual trends and future research directions. IEEE International Conference on Big Data. 4453–4457. DOI: https://doi.org/9781538627150.
Fan, D., Jiang, G. P., Song, Y. R. and Li, Y. W. 2020. Novel fake news spreading model with similarity on PSO-based networks. Physica A: Statistical Mechanics and Its Applications. 549: 124319. DOI: https://doi.org/10.1016/j.physa.2020.124319.
Suntwal, S., Brown, S., and Patton, M. 2020. How does Information Spread? An Exploratory Study of True and Fake News, Proceedings of the 53rd Hawaii International Conference on System Sciences. 3: 5893–5902.
Oyibo, K., Adaji, I., Orji, R., and Vassileva, J. 2018. What drives the perceived credibility of mobile websites: classical or expressive aesthetics? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10902 LNCS(1): 576–594.
Fogg, B. J., Soohoo, C., Danielson, D. R., Marable, L., Stanford, J., and Tauber, E. R. 2003. How do users evaluate the credibility of Websites?: A study with over 2,500 participants, Proceedings of the 2003 Conference on Designing for User Experiences, DUX ’03. 1–15.
Ireton, C. and Posetti, J. 2018. Journalism, ‘Fake News’ & Disinformation, UNESCO, Paris.
Wobbrock, J. O., Hsu, A. K., Burger, M. A., and Magee, M. J. 2019. Isolating the effects of web page visual appearance on the perceived credibility of online news among college students, HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media. 191–200, DOI: https://doi.org/10.1145/3342220.3343663.
Szabó, K. 2016. Online Visuality. In A. Benedek & Á. Veszelszki (Eds.), In The Beginning was the Image: The Omnipresence of Pictures, 103-150, Peter Lang, New York.
Gillespie, T. 2018. Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. New Haven.
Lara-Navarra, P., López-Borrull, A., Sánchez-Navarro, J., and Yànez, P. 2018. Medición de la influencia de usuarios en redes sociales: Propuesta socialengagement. Profesional de La Informacion. 27(4): 899–908.
Meel, P. and Vishwakarma, D. K. 2020. Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Expert Systems with Applications. 153: 112986.
Zhu, L. and Wang, B. 2020. Stability analysis of a SAIR rumor spreading model with control strategies in online social networks. Information Sciences. 526: 1–19.
Deters, J., Aguiar, I., and Feuerborn, J. 2019. The Mathematics of Gossip. CODEE Journal. 12(1): 73–82.
Piqueira, J. R. C., Zilbovicius, M., and Batistela, C. M. 2020. Daley–Kendal models in fake-news scenario. Physica A: Statistical Mechanics and Its Applications. 548: 123406.
Li, J., Jiang, H., Yu, Z., and Hu, C. 2019. Dynamical analysis of rumor spreading model in homogeneous complex networks. Applied Mathematics and Computation. 359: 374–385.
Hartley, K. and Vu, M. K. 2020. Fighting fake news in the COVID-19 era: policy insights from an equilibrium model. Policy Sciences. 53(4): 735–758.
Sterman, J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin McGraw-Hill, Boston.
Sy, C. 2017. A policy development model for reducing bullwhips in hybrid production-distribution systems, International Journal of Production Economics. 190: 67-79.
Vosoughi, S., Roy, D., and Aral, S. 2018. The Spread of True and False News Online, Science. 1151: 1146–1151.
McCombs, M. 2011. The Agenda-Setting Role of the Mass Media in the Shaping of Public Opinion, University of Texas: Austin, https://doi.org/10.13245/j.hust.15S1016.
