The Rules of Contagion

by Adam Kucharski

  70. Camacho A. et al., ‘Potential for large outbreaks of Ebola virus disease’, Epidemics, 2014.

  71. Heymann D.L., ‘Ebola: transforming fear into appropriate action’, The Lancet, 2017.

  72. Widely attributed, but no clear primary source.

  73. By early December, the average reporting delay was 2–3 days. Source: Finger F. et al., ‘Real-time analysis of the diphtheria outbreak in forcibly displaced Myanmar nationals in Bangladesh’, BMC Medicine, 2019.

  74. Statistics from: Katz J. and Sanger-Katz M., ‘“The Numbers Are So Staggering.” Overdose Deaths Set a Record Last Year’, New York Times, 29 November 2018; Ahmad F.B. et al., ‘Provisional drug overdose death counts’, National Center for Health Statistics, 2018; Felter C., ‘The U.S. Opioid Epidemic’, Council on Foreign Relations, 26 December 2017; ‘Opioid painkillers “must carry prominent warnings”’. BBC News Online, 28 April 2019.

  75. Goodnough A., Katz J. and Sanger-Katz M., ‘Drug Overdose Deaths Drop in U.S. for First Time Since 1990’, New York Times, 17 July 2019.

  76. Background and quotes about opioid crisis analysis from author interview with Rosalie Liccardo Pacula, May 2018. Additional details from: Pacula R.L., Testimony presented before the House Appropriations Committee, Subcommittee on Labor, Health and Human Services, Education, and Related Agencies on April 5, 2017.

  77. Exponential increase in death rate from 11 per 100,000 in 1979 to 137 per 100,000 in 2015, implying doubling time = 36/log2(137/11) = 10 years.

  78. Jalal H., ‘Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016’, Science, 2018.

  79. Mars S.G. ‘“Every ‘never’ I ever said came true”: transitions from opioid pills to heroin injecting’, International Journal of Drug Policy, 2014.

  80. TCR Staff, ‘America “Can’t Arrest Its Way Out of the Opioid Epidemic”’, The Crime Report, 16 February 2018.

  81. Lum K. and Isaac W., ‘To predict and serve?’ Significance, 7 October 2016.

  82. Quotes from author interview with Kristian Lum, January 2018.

  83. Perry W.L. et al., ‘Predictive Policing’, RAND Corporation Report, 2013.

  84. Whitty C.J.M., ‘What makes an academic paper useful for health policy?’, BMC Medicine, 2015.

  85. Dumke M. and Main F., ‘A look inside the watch list Chicago police fought to keep secret’, Associated Press, 18 June 2017.

  86. Background on SSL algorithm: Posadas B., ‘How strategic is Chicago’s “Strategic Subjects List”? Upturn investigates’, Medium, 22 June 2017; Asher J. and Arthur R., ‘Inside the Algorithm That Tries to Predict Gun Violence in Chicago’, New York Times, 13 June 2017; Kunichoff Y. and Sier P., ‘The Contradictions of Chicago Police’s Secretive List’, Chicago Magazine, 21 August 2017.

  87. According to Posadas (Medium, 2017), proportion high risk: 287,404/398,684 = 0.72. 88,592 of these (31 per cent) have never been arrested or a victim of crime.

  88. Hemenway D., While We Were Sleeping: Success Stories in Injury and Violence Prevention, (University of California Press, 2009).

  89. Background on broken windows approach: Kelling G.L. and Wilson J.Q., ‘Broken Windows’, The Atlantic, March 1982; Harcourt B.E. and Ludwig J., ‘Broken Windows: New Evidence from New York City and a Five-City Social Experiment’, University of Chicago Law Review, 2005.

  90. Childress S., ‘The Problem with “Broken Windows” Policing’, Public Broadcasting Service, 28 June 2016.

  91. Keizer K. et al., ‘The Spreading of Disorder’, Science, 2008.

  92. Keizer K. et al., ‘The Importance of Demonstratively Restoring Order’, PLOS ONE, 2013.

  93. Tcherni-Buzzeo M., ‘The “Great American Crime Decline”: Possible explanations’, In Krohn M.D. et al., Handbook on Crime and Deviance, 2nd edition, (Springer, New York 2019).

  94. Alternative hypotheses for decline, and accompanying criticism: Levitt S.D., ‘Understanding Why Crime Fell in the 1990s: Four Factors that Explain the Decline and Six that Do Not’, Journal of Economic Perspectives, 2004; Nevin R., ‘How Lead Exposure Relates to Temporal Changes in IQ, Violent Crime, and Unwed Pregnancy’, Environmental Research Section A, 2000; Foote C.L. and Goetz C.F., ‘The Impact of Legalized Abortion on Crime: Comment’, Quarterly Journal of Economics, 2008; Casciani D., ‘Did removing lead from petrol spark a decline in crime?’, BBC News Online, 21 April 2014.

  95. Author interview with Melissa Tracy, August 2018.

  96. Lowrey A., ‘True Crime Costs’, Slate, 21 October 2010.

  5. Going viral

  1. Background on Buzzfeed from: Peretti J., ‘My Nike Media Adventure’, The Nation, 9 April 2001; Email correspondence with customer service representatives at Nike iD. http://www.yorku.ca/dzwick/niked.html Accessed: January 2018; Salmon F., ‘BuzzFeed’s Jonah Peretti Goes Long’, Fusion, 11 June 2014; Lagorio-Chafkin C., ‘The Humble Origins of Buzzfeed’, Inc., 3 March 2014; Rice A., ‘Does BuzzFeed Know the Secret?’, New York Magazine, 7 April 2013.

  2. Peretti J., ‘My Nike Media Adventure’, The Nation, 9 April 2001.

  3. Background and quotes from author interview with Duncan Watts, February 2018. There is also a more detailed discussion of this research in: Watts D., Everything is Obvious: Why Common Sense is Nonsense (Atlantic Books, 2011).

  4. Milgram S., ‘The small-world problem’, Psychology Today, 1967.

  5. Dodds P.S. et al., ‘An Experimental Study of Search in Global Social Networks’, Science, 2003.

  6. Bakshy E. et al., ‘Everyone’s an Influencer: Quantifying Influence on Twitter’, Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM’11), 2011.

  7. Aral S. and Walker D., ‘Identifying Influential and Susceptible Members of Social Networks’, Science, 2012.

  8. Aral S. and Dillon P., ‘Social influence maximization under empirical influence models’, Nature Human Behaviour, 2018.

  9. Data from: Ugander J. et al., ‘The Anatomy of the Facebook Social Graph’, arXiv, 2011; Kim D.A. et al., ‘Social network targeting to maximise population behaviour change: a cluster randomised controlled trial’, The Lancet, 2015; Newman M.E., ‘Assortative mixing in networks’, Physical Review Letters, 2002; Apicella C.L. et al., ‘Social networks and cooperation in hunter-gatherers’, Nature, 2012.

  10. Conclusion supported by: Aral S. and Dillon P., Nature Human Behaviour, 2018; Bakshy E. et al., WSDM, 2011; Kim D.A. et al., The Lancet, 2015.

  11. Buckee C.O.F. et al., ‘The effects of host contact network structure on pathogen diversity and strain structure’, PNAS, 2004; Kucharski A., ‘Study epidemiology of fake news’, Nature, 2016.

  12. Bessi A. et al., ‘Science vs Conspiracy: Collective Narratives in the Age of Misinformation’, PLOS ONE, 2015; Garimella K. et al., ‘Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship’, Proceedings of the World Wide Web Conference 2018, 2018.

  13. Background from: Goldacre B., Bad Science (Fourth Estate, 2008); The Editors of The Lancet, ‘Retraction – Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children’, The Lancet, 2010.

  14. Finnegan G., ‘Rise in vaccine hesitancy related to pursuit of purity’, Horizon Magazine, 26 April 2018; Larson H.J., ‘Maternal immunization: The new “normal” (or it should be)’, Vaccine, 2015; Larson H.J. et al., ‘Tracking the global spread of vaccine sentiments: The global response to Japan’s suspension of its HPV vaccine recommendation’, Human Vaccines & Immunotherapeutics, 2014.

  15. Background on variolation from: ‘Variolation – an overview’, ScienceDirect Topics, 2018.

  16. Voltaire., ‘Letter XI’ from Letters on the English. (1734).

  17. Background on Bernoulli’s work from: Dietz K. and Heesterbeek J.A.P., ‘Daniel Bernoulli’s epidemiological model revisited’, Mathematical Biosciences, 2002; Colombo C. and Diamanti M., ‘The smallpox vaccine: the dispute between Bernoulli and d’Alembert and the calculus of probabi
lities’, Lettera Matematica International, 2015.

  18. There is a large literature on MMR and measles vaccine safety and efficacy, e.g. Smeeth L. et al., ‘MMR vaccination and pervasive developmental disorders: a case-control study’, The Lancet, 2004; A. Hviid, J.V. Hansen, M. Frisch, et al., ‘Measles, Mumps, Rubella Vaccination and Autism: A Nationwide Cohort Study’, Annals of Internal Medicine, 2019; LeBaron C.W. et al., ‘Persistence of Measles Antibodies After 2 Doses of Measles Vaccine in a Postelimination Environment’, JAMA Pediatrics, 2007.

  19. Wellcome Global Monitor 2018, 19 June 2019.

  20. Finnegan G., ‘Rise in vaccine hesitancy related to pursuit of purity’, Horizon Magazine, 26 April 2018.

  21. Funk S. et al., ‘Combining serological and contact data to derive target immunity levels for achieving and maintaining measles elimination’, BioRxiv, 2019.

  22. ‘Measles: Europe sees record number of cases and 37 deaths so far this year’, British Medical Journal, 2018.

  23. Bakshy E. et al., ‘Exposure to ideologically diverse news and opinion on Facebook’, Science, 2015; Tufekci Z., ‘How Facebook’s Algorithm Suppresses Content Diversity (Modestly) and How the Newsfeed Rules Your Clicks’, Medium, 7 May 2015.

  24. Flaxman S. et al., ‘Filter bubbles, echo chambers and online news consumption’, Public Opinion Quarterly, 2016.

  25. Bail C.A. et al., ‘Exposure to opposing views on social media can increase political polarization’, PNAS, 2018.

  26. Duggan M. and Smith A., ‘The Political Environment on Social Media’, Pew Research Center, 2016.

  27. boyd dm., ‘Taken Out of Context: American Teen Sociality in Networked Publics’, University of California, Berkeley PhD Dissertation, 2008.

  28. Early example: ‘Dead pet UL?’ Posted on alt.folklore.urban, 10 July 1992.

  29. Letter to Étienne Noël Damilaville, 16 May 1767.

  30. Suler J., ‘The Online Disinhibition Effect’, Cyberpsychology and Behavior, 2004.

  31. Cheng J. et al., ‘Antisocial Behavior in Online Discussion Communities’, Association for the Advancment of Artificial Intelligence, 2015; Cheng J. et al., ‘Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions’, Computer-Supported Cooperative Work, 2017.

  32. Background on Facebook study from: Kramer A.D.I. et al., ‘Experimental evidence of massive-scale emotional contagion through social networks’, PNAS, 2014; D’Onfro J., ‘Facebook Researcher Responds To Backlash Against “Creepy” Mood Manipulation Study’, Insider, 29 June 2014.

  33. Griffin A., ‘Facebook manipulated users’ moods in secret experiment’, The Independent, 29 June 2014; Arthur C., ‘Facebook emotion study breached ethical guidelines, researchers say’, The Guardian, 30 June 2014.

  34. Examples: Raine R. et al., ‘A national cluster-randomised controlled trial to examine the effect of enhanced reminders on the socioeconomic gradient in uptake in bowel cancer screening’, British Journal of Cancer, 2016; Kitchener H.C. et al., ‘A cluster randomised trial of strategies to increase cervical screening uptake at first invitation (STRATEGIC)’, Health Technology Assessment, 2016. It’s worth noting that despite their widespread use, the concept of randomised experiments (often called ‘A/B tests’) seems to make many people uncomfortable – even if the individual options are innocuous and the study is ethically designed. One 2019 study found that ‘people frequently rate A/B tests designed to establish the comparative effectiveness of two policies or treatments as inappropriate even when universally implementing either A or B, untested, is seen as appropriate’. Source: Meyer M.N. et al., ‘Objecting to experiments that compare two unobjectionable policies or treatments’, PNAS, 2019.

  35. Berger J. and Milkman K.L., ‘What Makes online Content Viral?’, Journal of Marketing Research, 2011.

  36. Heath C. et al., ‘Emotional selection in memes: the case of urban legends’, Journal of Personality and Social Psychology, 2001.

  37. Tufekci Z., ‘YouTube, the Great Radicalizer’, New York Times, 10 March 2018.

  38. Baquero F. et al., ‘Ecology and evolution of antibiotic resistance’, Environmental Microbiology Reports, 2009.

  39. Background from: De Domenico M. et al., ‘The Anatomy of a Scientific Rumor’, Scientific Reports, 2013.

  40. Goel S. et al., ‘The Structural Virality of Online Diffusion’, Management Science, 2016.

  41. Goel S. et al., ‘The Structure of Online Diffusion Networks’, EC’12 Proceedings of the 13th ACM Conference on Electronic Commerce, 2012; Tatar A. et al., ‘A survey on predicting the popularity of web content’, Journal of Internet Services and Applications, 2014.

  42. Watts D.J. et al., ‘Viral Marketing for the Real World’, Harvard Business Review, 2007.

  43. Method from: Blumberg S. and Lloyd-Smith J.O., PLOS Computational Biology, 2013. This calculation works even if there is potential for superspreading events.

  44. Chowell G. et al., ‘Transmission potential of influenza A/H7N9, February to May 2013, China’, BMC Medicine, 2013.

  45. Watts D.J. et al., ‘Viral Marketing for the Real World’, Harvard Business Review, 2007. Note that technical issues with the e-mail campaign may have artificially reduced the reproduction number for Tide to some extent.

  46. Breban R. et al., ‘Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk’, The Lancet, 2013.

  47. Geoghegan J.L. et al., ‘Virological factors that increase the transmissibility of emerging human viruses’, PNAS, 2016.

  48. García-Sastre A., ‘Influenza Virus Receptor Specificity’, American Journal of Pathology, 2010.

  49. Adamic L.A. et al., ‘Information Evolution in Social Networks’, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (WSDM’16), 2016.

  50. Cheng J. et al., ‘Do Diffusion Protocols Govern Cascade Growth?’, AAAI Publications, 2018.

  51. Background on early BuzzFeed transmission: Rice A., ‘Does BuzzFeed Know the Secret?’, New York Magazine, 7 April 2013.

  52. Watts D.J. et al., ‘Viral Marketing for the Real World’, Harvard Business Review, 2007. For ease of reading, the shorthand ‘<’ has been replaced by ‘less than’ in the text.

  53. Guardian Datablog, ‘Who are the most social publishers on the web?’, The Guardian Online, 3 October 2013.

  54. Salmon F., ‘BuzzFeed’s Jonah Peretti Goes Long’, Fusion, 11 June 2014.

  55. Martin T. et al., ‘Exploring Limits to Prediction in Complex Social Systems’, Proceedings of the 25th International Conference on World Wide Web, 2016.

  56. Shulman B. et al., ‘Predictability of Popularity: Gaps between Prediction and Understanding’, International Conference on Web and Social Media, 2016.

  57. Cheng J. et al., ‘Can cascades be predicted?’, Proceedings of the 23rd International Conference on World Wide Web, 2014.

  58. Yucesoy B. et al., ‘Success in books: a big data approach to bestsellers’, EPJ Data Science, 2018.

  59. McMahon V., ‘#Neknominate girl’s shame: I’m sorry for drinking a goldfish’, Irish Mirror, 5 February 2014.

  60. Many Neknomination videos can be seen on YouTube; Fricker M., ‘RSPCA hunt yob who downed NekNomination cocktail containing cider, eggs, battery fluid, urine and THREE goldfish’, Mirror, 5 February 2014.

  61. Example coverage: Fishwick C., ‘NekNominate: should Facebook ban the controversial drinking game?’, The Guardian, 11 February 2014; ‘“Neknomination”: Facebook ignores calls for ban after two deaths’, Evening Standard, 3 February 2014.

  62. More or Less: ‘Neknomination Outbreak’, BBC World Service Online, 22 February 2014.

  63. Kucharski A.J., ‘Modelling the transmission dynamics of online social contagion’, arXiv, 2016.

  64. Researchers at the University of Warwick found a similar level of predictability. Based on the dynamics of neknomination, they correctly forecast the four-week duration of the ice bucket challenge shortly after it emerged a few months later. Sprague D
.A. and House T., ‘Evidence for complex contagion models of social contagion from observational data’, PLOS ONE, 2017.

  65. Cheng J. et al., ‘Do Cascades Recur?’, Proceedings of the 25th International Conference on World Wide Web, 2016.

  66. Crane R. and Sornette D., ‘Robust dynamic classes revealed by measuring the response function of a social system’, PNAS, 2008.

  67. Tan C. et al., ‘Lost in Propagation? Unfolding News Cycles from the Source’, Association for the Advancement of Artificial Intelligence, 2016; Tatar A. et al., ‘A survey on predicting the popularity of web content’, Journal of Internet Services and Applications, 2014.

  68. Vosoughi S. et al., ‘The spread of true and false news online’, Science, 2018.

  69. Examples from: Romero D.M., ‘Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter’, Proceedings of the 20th International Conference on World Wide Web, 2011; State B. and Adamic L.A., ‘The Diffusion of Support in an Online Social Movement: Evidence from the Adoption of Equal-Sign Profile Pictures’, Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 2015; Guilbeault D. et al., ‘Complex Contagions: A Decade in Review’, in Lehmann S. and Ahn Y. (eds.), Spreading Dynamics in Social Systems (Springer Nature, 2018).

  70. Weng L. et al., ‘Virality Prediction and Community Structure in Social Networks’, Scientific Reports, 2013.

  71. Centola D., How Behavior Spreads: The Science of Complex Contagions (Princeton University Press, 2018).

  72. Anderson C., ‘The End of Theory: The Data Deluge Makes the Scientific Method Obsolete’, Wired, 23 June 2008.

  73. ‘Big Data, for better or worse: 90 per cent of world’s data generated over last two years’, Science Daily, 22 May 2013.

  74. Widely attributed to Goodhart in this form. Original statement: ‘Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes’. Goodhart C., ‘Problems of Monetary Management: The U.K. Experience’, in Courakis, A. S. (ed.), Inflation, Depression, and Economic Policy in the West (Springer 1981).