Marcella Tambuscio
Filippo Menczer
ABSTRACT
spread of misinformation, rumors and hoaxes. The goal of this work is to introduce a simple modeling framework to study the diffusion of hoaxes and in particular how the availability of debunking information may contain their diffusion. As traditionally done in the mathematical modeling of information diffusion processes, we regard hoaxes as viruses: users can become infected if they are exposed to them, and turn into spreaders as a consequence. Upon verification, users can also turn into non-believers and spread the same attitude with a mechanism analogous to that of the hoax-spreaders. Both believers and non-believers, as time passes, can return to a susceptible state. Our model is characterized by four parameters: spreading rate, gullibility, probability to verify a hoax, and that to forget one's current belief. Simulations on homogeneous, heterogeneous, and real networks for a wide range of parameters values reveal a threshold for the fact-checking probability that guarantees the complete removal of the hoax from the network. Via a mean field approximation, we establish that the threshold value does not depend on the spreading rate but only on the gullibility and forgetting probability. Our approach allows to quantitatively gauge the minimal reaction necessary to eradicate a hoax.
- D. Acemoglu, A. Ozdaglar, and A. ParandehGheibi. Spread of (mis) information in social networks. Games and Economic Behavior, 70(2):194--227, 2010.Google Scholar
Cross Ref
- N. Bailey. The Mathematical Theory of Infectious Diseases and its Applications. Griffin, London, 2nd edition, 1975.Google Scholar
- C. Budak, D. Agrawal, and A. El Abbadi. Limiting the spread of misinformation in social networks. In Proceedings of the 20th international conference on World wide web, pages 665--674. ACM, 2011. Google ScholarDigital Library
- F. Chierichetti, S. Lattanzi, and A. Panconesi. Rumor spreading in social networks. In Automata, Languages and Programming, pages 375--386. Springer, 2009. Google ScholarDigital Library
- A. Friggeri, L. A. Adamic, D. Eckles, and J. Cheng. Rumor cascades. In Proc. Eighth Intl. AAAI Conf. on Weblogs and Social Media (ICWSM), 2014.Google Scholar
- S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang. Prominent features of rumor propagation in online social media. In Data Mining (ICDM), 2013 IEEE 13th International Conference on, pages 1103--1108. IEEE, 2013.Google ScholarCross Ref
- K. Lerman and R. Ghosh. Information contagion: An empirical study of the spread of news on digg and twitter social networks. ICWSM, 10:90--97, 2010.Google Scholar
- J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014.Google Scholar
- S. Lewandowsky, U. K. Ecker, C. M. Seifert, N. Schwarz, and J. Cook. Misinformation and its correction continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3):106--131, 2012.Google ScholarCross Ref
- Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: model and implications. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 6--14. ACM, 2012. Google ScholarDigital Library
- D. Mocanu, L. Rossi, Q. Zhang, M. Karsai, and W. Quattrociocchi. Collective attention in the age of (mis) information. arXiv preprint arXiv:1403.3344, 2014.Google Scholar
- Y. Moreno, M. Nekovee, and A. F. Pacheco. Dynamics of rumor spreading in complex networks. Physical Review E, 69(6):066130, 2004.Google ScholarCross Ref
- M. Nekovee, Y. Moreno, G. Bianconi, and M. Marsili. Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and its Applications, 374(1):457--470, 2007.Google ScholarCross Ref
- N. P. Nguyen, G. Yan, M. T. Thai, and S. Eidenbenz. Containment of misinformation spread in online social networks. In Proceedings of the 3rd Annual ACM Web Science Conference, pages 213--222. ACM, 2012. Google ScholarDigital Library
- B. Nyhan, J. Reifler, and P. A. Ubel. The hazards of correcting myths about health care reform. Medical Care, 51(2):127--132, 2013.Google ScholarCross Ref
- R. Pastor-Satorras, C. Castellano, P. Van Mieghem, and A. Vespignani. Epidemic processes in complex networks. arXiv preprint arXiv:1408.2701, 2014.Google Scholar
- R. Pastor-Satorras and A. Vespignani. Epidemic spreading in scale-free networks. Physical review letters, 86(14):3200, 2001.Google Scholar
- J. Ratkiewicz, M. Conover, M. Meiss, B. Gonçalves, A. Flammini, and F. Menczer. Detecting and tracking political abuse in social media. In Proc. Fifth Intl. AAAI Conf. on Weblogs and Social Media (ICWSM), 2011.Google Scholar
- D. Trpevski, W. K. S. Tang, and L. Kocarev. Model for rumor spreading over networks. Phys. Rev. E, 81:056102, May 2010.Google ScholarCross Ref
- L. Weng, A. Flammini, A. Vespignani, and F. Menczer. Competition among memes in a world with limited attention. Scientific Reports, 2, 2012.Google Scholar
- L. Zhao, X. Qiu, X. Wang, and J. Wang. Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks. Physica A: Statistical Mechanics and its Applications, 392(4):987--994, 2013.Google ScholarCross Ref
Index Terms
- Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks
Recommendations
Linguistic Signals under Misinformation and Fact-Checking: Evidence from User Comments on Social Media
Misinformation and fact-checking are opposite forces in the news environment: the former creates inaccuracies to mislead people, while the latter provides evidence to rebut the former. These news articles are often posted on social media and attract ...
The Fact-Checking Observatory: Reporting the Co-Spread of Misinformation and Fact-checks on Social Media
HT '23: Proceedings of the 34th ACM Conference on Hypertext and Social MediaIn the context of the Covid-19 pandemic and the Russian invasion of Ukraine, tracking how misinformation and fact-checks spread on social media is key for understanding where fact-checking efforts need to be focused and what demographics are most likely ...
Co-spread of Misinformation and Fact-Checking Content During the Covid-19 Pandemic
Social InformaticsAbstractIn the context of the Covid-19 pandemic, the consequences of misinformation are a matter of life and death. Correcting misconceptions and false beliefs are important for injecting reliable information about the outbreak. Fact-checking ...
Comments