Media coverage and firearm acquisition in the aftermath of a mass shooting

Abstract

With an alarming frequency, the United States is experiencing mass shooting events, which often result in heated public debates on firearm control. Whether such events play any role in recent dramatic increases in firearm prevalence remains an open question. This study adopts an information-theoretic framework to analyse the complex interplay between the occurrence of a mass shooting, media coverage on firearm control policies and firearm acquisition at both national and state levels. Through the analysis of time series from 1999 to 2017, we identify a correlation between the occurrence of a mass shooting and the rate of growth in firearm acquisition. More importantly, a transfer entropy analysis pinpoints media coverage on firearm control policies as a potential causal link in a Wiener–Granger sense that establishes this correlation. Our results demonstrate that media coverage may increase public worry about more stringent firearm control and partially drive increases in firearm prevalence.

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Fig. 1: Time series for the study of the relationships between firearm acquisition, mass shootings and media coverage on firearm control policies from January 1999 to December 2017.
Fig. 2: Estimated conditional transfer entropy between each pair of mass shootings, media output and firearm background checks.
Fig. 3: Restrictiveness of firearm-related laws and transfer entropy from media coverage on firearm control polices to background checks, after controlling for mass shootings.

Data availability

All data needed to evaluate the paper’s conclusions are presented in the article and the Supplementary Information, as well as being freely available at https://github.com/Causality-Research/Firearms.

Code availability

The code used to reproduce the results is freely available at https://github.com/Causality-Research/Firearms.

References

  1. 1.

    Krouse, W. J. & Richardson, D. J. Mass Murder with Firearms: Incidents and Victims, 1999–2013 Specialist in Domestic Security and Crime Policy (Congressional Research Service, 2015).

  2. 2.

    Past summary ledgers. G un Violence Archive https://www.gunviolencearchive.org/past-tolls (accessed 15 November 2018).

  3. 3.

    Krug, E., Powell, K. E. & Dahlberg, L. L. Firearm-related deaths in the United States and 35 other high- and upper-middle-income countries. Int. J. Epidemiol. 27, 214–221 (1998).

  4. 4.

    Weiner, J. et al. Reducing firearm violence: a research agenda. Inj. Prev. 13, 80–84 (2007).

  5. 5.

    Weinberger, S. E. et al. Firearm-related injury and death in the United States: a call to action from 8 health professional organizations and the American bar association. Ann. Intern. Med. 162, 513–516 (2015).

  6. 6.

    Grinshteyn, E. & Hemenway, D. Violent death rates: the US compared with other high-income OECD Countries, 2010. Am. J. Med. 129, 266–273 (2016).

  7. 7.

    NICS firearm checks: month/year. FBI https://www.fbi.gov/file-repository/nics_firearm_checks_-_month_year.pdf/view (accessed 15 November 2018).

  8. 8.

    Bureau of Alcohol, Tobacco, Firearms and Explosives. Firearms Commerce in the United States Annual Statistical Update 2017 (United States Department of Justice, 2017).

  9. 9.

    Krouse, W. J. CRS Report for Congress Gun Control Legislation (Congressional Research Service, 2012).

  10. 10.

    Vernick, J. S., Rutkow, L., Webster, D. W. & Teret, S. P. Changing the constitutional landscape for firearms: the US Supreme Court’s recent Second Amendment decisions. Am. J. Public Health 101, 2021–2026 (2011).

  11. 11.

    Kalesan, B., Mobily, M. E., Keiser, O., Fagan, J. A. & Galea, S. Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study. Lancet 387, 1847–1855 (2016).

  12. 12.

    Parker, K., Horowitz, J., Igielnik, R., Oliphant, B. & Browan, A. America’s Complex Relationship with Guns (Pew Research Center, 2017).

  13. 13.

    Kegley, J. Gun rush: weapons sales soar in Kentucky on threat of new federal restrictions. Lexington Herald Leader https://www.kentucky.com/news/local/crime/article44398467.html (12 November 2015).

  14. 14.

    Post Staff Report. Gun sales surging in wake of ‘Dark Knight Rises’ shooting. New York Post https://nypost.com/2012/07/25/gun-sales-surging-in-wake-of-dark-knight-rises-shooting (25 July 2012).

  15. 15.

    Naik, R. Here’s why gun stocks rise after mass shootings. CNN Money https://money.cnn.com/video/news/2017/10/03/gun-stock-sales-rise.cnnmoney/index.html (accessed 15 November 2018).

  16. 16.

    Thompson, M. Why gun sales often rise after mass shootings. CNBC https://www.cnbc.com/id/100321785 (17 December 2012).

  17. 17.

    Depetris-Chauvin, E. Fear of Obama: an empirical study of the demand for guns and the US 2008 presidential election. J. Public Econ. 130, 66–79 (2015).

  18. 18.

    Wallace, L. N. Responding to violence with guns: mass shootings and gun acquisition. Soc. Sci. J. 52, 156–167 (2015).

  19. 19.

    Studdert, D. M., Zhang, Y., Rodden, J. A., Hyndman, R. J. & Wintemute, G. J. Handgun acquisitions in California after two mass shootings. Ann. Intern. Med. 166, 698–706 (2017).

  20. 20.

    Follman, M., Aronsen, G. & Pan, D. US mass shootings, 1982–2019: data from Mother Jones’ investigation. Mother Jones https://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data (2019).

  21. 21.

    Schreiber, T. Measuring information transfer. Phys. Rev. Lett. 85, 461–464 (2000).

  22. 22.

    Bossomaier, T., Barnett, L., Harré, M. & Lizier, J. T. An Introduction to Transfer Entropy: Information Flow in Complex Systems (Springer, 2016).

  23. 23.

    Stetter, O., Battaglia, D., Soriano, J. & Geisel, T. Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Comput. Biol. 8, e1002653 (2012).

  24. 24.

    Vicente, R., Wibral, M., Lindner, M. & Pipa, G. Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 45–67 (2011).

  25. 25.

    Hlinka, J. et al. Reliability of inference of directed climate networks using conditional mutual information. Entropy 15, 2023–2045 (2013).

  26. 26.

    Runge, J., Heitzig, J., Petoukhov, V. & Kurths, J. Escaping the curse of dimensionality in estimating multivariate transfer entropy. Phys. Rev. Lett. 108, 258701 (2012).

  27. 27.

    Anderson, R. P. et al. Understanding policy diffusion in the US: an information-theoretical approach to unveil connectivity structures in slowly evolving complex systems. SIAM J. Appl. Dyn. Syst. 15, 1384–1409 (2016).

  28. 28.

    Grabow, C., Macinko, J., Silver, D. & Porfiri, M. Detecting causality in policy diffusion processes. Chaos 26, 083113 (2016).

  29. 29.

    Porfiri, M. & Ruiz Marín, M. Information flow in a model of policy diffusion: an analytical study. IEEE Trans. Netw. Sci. Eng. 5, 42–54 (2018).

  30. 30.

    Kawachi, I., Kennedy, B. P. & Wilkinson, R. G. Crime: social disorganization and relative deprivation. Soc. Sci. Med. 48, 719–731 (1999).

  31. 31.

    Luca, M., Malhotra, D. & Poliquin, C. The Impact of Mass Shootings on Gun Policy (Harvard Business School, 2016).

  32. 32.

    Goss, K. A. Disarmed: The Missing Movement for Gun Control in America (Princeton Univ. Press, 2010).

  33. 33.

    Spitzer, R. J. Politics of Gun Control (Routledge, 2015).

  34. 34.

    Towers, S., Gomez-Lievano, A., Khan, M., Mubayi, A. & Castillo-Chavez, C. Contagion in mass killings and school shootings. PLoS One 10, e0117259 (2015).

  35. 35.

    Reeping, P. M. et al. State gun laws, gun ownership, and mass shootings in the US: cross sectional time series. BMJ 364, l542 (2019).

  36. 36.

    Schildkraut, J. & Jaymi Elsass, H. in The Wiley Handbook of the Psychology of Mass Shootings (ed. Wilson, L. C.) 115–135 (Wiley, 2016).

  37. 37.

    Scherer, K. R. in Handbook of Cognition and Emotion (eds Dalgleish, T. & Power, M. J.) 637–663 (1999).

  38. 38.

    Large, J. When fear outweighs reality. The Seattle Times https://www.seattletimes.com/seattle-news/when-fear-outweighs-reality (23 October 2014).

  39. 39.

    America’s top fears 2017. Chapman University https://blogs.chapman.edu/wilkinson/2017/10/11/americas-top-fears-2017 (2017).

  40. 40.

    Fox, J. A. & Delateur, M. J. Mass shootings in America: moving beyond Newtown. Homicide Stud. 18, 125–145 (2014).

  41. 41.

    Silva, J. R. & Capellan, J. A. The media’s coverage of mass public shootings in America: fifty years of newsworthiness. Int. J. Comp. Appl. Crim. Justice 43, 77–97 (2019).

  42. 42.

    Runge, J. Causal network reconstruction from time series: from theoretical assumptions to practical estimation. Chaos 28, 075310 (2018).

  43. 43.

    Sun, J., Taylor, D. & Bollt, E. M. Causal network inference by optimal causation entropy. SIAM J. Appl. Dyn. Syst. 14, 73–106 (2015).

  44. 44.

    Staniek, M. & Lehnertz, K. Symbolic transfer entropy. Phys. Rev. Lett. 100, 158101 (2008).

  45. 45.

    Wibral, M., Vicente, R. & Lizier, J. T. Directed Information Measures in Neuroscience (Springer, 2014).

  46. 46.

    Porfiri, M. & Ruiz Marín, M. Inference of time-varying networks through transfer entropy, the case of a Boolean network model. Chaos 28, 103123 (2018).

  47. 47.

    Lang, M. Firearm background checks and suicide. Econ. J. 123, 1085–1099 (2013).

  48. 48.

    Follman, M., Aronsen, G. & Pan, D. A guide to mass shootings in America. Mother Jones https://www.motherjones.com/politics/2012/07/mass-shootings-map (2019).

  49. 49.

    Investigative Assistance for Violent Crimes Act of 2012 (US Congress, 2013).

  50. 50.

    Duwe, G. in The Wiley Handbook of the Psychology of Mass Shootings (ed. Wilson, L. C.) 20–35 (Wiley, 2016).

  51. 51.

    Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).

  52. 52.

    Porfiri, M. Inferring causal relationships in zebrafish–robot interactions through transfer entropy: a small lure to catch a big fish. Anim. Behav. Cogn. 5, 341–367 (2018).

  53. 53.

    Hlavackova-Schindler, K., Palus, M., Vejmelka, M. & Bhattacharya, J. Causality detection based on information-theoretic approaches in time series analysis. Phys. Rep. 441, 1–46 (2007).

  54. 54.

    Hood, C. C., Ashley, J. D. & Findley, D. F. An Empirical Evaluation of the Performance of TRAMO/SEATS on Simulated Series (US Census Bureau, 2000).

  55. 55.

    Hao, B.-L. & Zheng, W.-M. Applied Symbolic Dynamics and Chaos Vol. 7 (World Scientific, 1998).

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Acknowledgements

We acknowledge D. Silver from New York University for useful input. M.P. thanks M. Camacho from the University of Murcia, Murcia, Spain, for his kind help with the use of TRAMO-SEATS, and M. Ruiz Marín from Technical University of Cartagena, Murcia, Spain, for stimulating discussions. R.S. acknowledges the hospitality of the Mechanical and Aerospace Engineering Department at New York University, Tandon School of Engineering during his sabbatical leave. Finally, M.P. thanks N. Simons and the Rockefeller Institute for useful discussions on data collection and policy making about firearm violence. This project was supported in whole by a grant from the New York University Research Challenge Fund Program. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

S.N. and M.P. designed the research. R.R.S. collected the data. All authors contributed to the formulation of the hypotheses underlying the study. S.N., M.P., R.R.S. and R.S. conducted the analysis. M.P. wrote the first draft of the manuscript. All authors discussed the results and edited the final version of the manuscript.

Correspondence to Maurizio Porfiri.

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