Urban Legends Never Die: The Spread of Urban Legends with a Modified SIR Model
Urban legends are everywhere. Whether a simple joke between friends or a ‘fun fact’ shared among acquaintances, urban legends are hard to escape. You would be hard-pressed to find someone in modern-day society who’s never heard of a single one, especially given the popularity of some, such as Bloody Mary or the Loch Ness Monster. In fact, the latter earns Scotland an estimated $50.6 million in tourism annually (though maybe not this year), still going strong after being started from a sighting in 1933 (though mentions of monsters in Loch Ness have existed since 565 A.D.). So, how did such a legend persist to the modern-day?
In his article “THE TRANSMISSION AND PERSISTENCE OF ‘URBAN LEGENDS’: SOCIOLOGICAL APPLICATION OF AGE-STRUCTURED EPIDEMIC MODELS,“ Andrew Noymer sets out to answer this question and more. Using a modified SIR model, Noymer considers various factors from birth rates and death rates to initial skepticism and learned skepticism from realizing the legend is false. With all of these variables in play, Noymer’s model clarifies how legends such as the Loch Ness Monster have maintained relevance through the many years. When the rumor first enters the population, the plethora of susceptible people leads to a large spike in believers, slowly waning over time as the population grows more skeptical. Eventually, it falls into obscurity, but resurges once the skeptics begin dying from old age. Yet some remain, and the legend never quite reaches the prevalence it had the first time. Though, because of this, the second wave lasts longer. According to the model, this pattern continues over time, approaching a proportion of constant believers in the population, turning from a series of epidemics into an endemic legend.
It’s certainly intriguing how these legends form and spread, but is there any evidence of this happening in real life, outside of Noymer’s model? Using Google Scholar, I searched up the term “loch ness monster” and recorded the number of results that come up for 5-year intervals, starting in 1933:
| Years | Search Result Count |
| 1933-1937 | 45 |
| 1938-1942 | 2 |
| 1943-1947 | 7 |
| 1948-1952 | 12 |
| 1953-1957 | 18 |
| 1958-1962 | 31 |
| 1963-1967 | 54 |
| 1968-1972 | 107 |
| 1973-1977 | 184 |
| 1978-1982 | 238 |
| 1983-1987 | 236 |
| 1988-1992 | 322 |
| 1993-1997 | 450 |
| 1998-2002 | 566 |
| 2003-2007 | 1010 |
| 2008-2012 | 1590 |
| 2013-2017 | 1910 |
| 2018-2020 (incomplete) | 980 |
This data doesn’t seem all that helpful, and at first glance it even appears to refute Noymer’s model. However, upon closer inspection, there is some evidence for the system. It’s easy to see when the legend first took hold of society, having 45 articles written about it from 1933-7, and its subsequent fall into oblivion, with only two results from 1938-1942. Of course, from here on, everything becomes more complicated as the world becomes ever-increasingly connected. It seems to rise again around 1958-62, right before it passes its original article count of 45 in 1963-7. This increase doesn’t necessarily mean that it was more prevalent in 1963-7 than in 1933-7, as other factors could have come into play. It seems to decrease once again in 1983-7, being the only recorded decrease in count after the initial drop, especially noticeable given the considerable increases otherwise. It’s a bit hard to pinpoint where it genuinely starts to increase once more, but it does seem to wane again beginning in 2018. If we extrapolate the data from 2018-20 over the whole 5-year period, we get 980/3*5, approximately equal to 1633, less than 1910. Of course, given both the extrapolation and current circumstances, this last tidbit isn’t the most accurate depiction of the data, but it is interesting.
There’s certainly a lot more to be done here concerning Noymer’s model. Some more data can be collected on mentions of the Loch Ness Monster, or current data could be restricted to limit the effects of growth over time. Other legends can be examined in detail. One could also expand on Noymer’s model by accounting for more factors, such as varying the number of interactions between various age groups. Of course, some urban legends cause excessive amounts of harm to society, and this data could help us arrive at a solution for this, providing us with a way to stop them in their tracks.
Sources:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2846379/
https://www.pbs.org/wgbh/nova/article/legend-loch-ness/