Why Do Urban Legends Go Viral?
This work addresses the problem of understanding viral misinformation for researchers in computational social science and NLP, but it is incremental as it builds on existing ideas about text characteristics.
The paper tackled the problem of why urban legends go viral by analyzing them as deceptive texts that balance credibility and incredibility, and found that using NLP tools to quantify characteristics like mimicking news details and emotional readability enables machine learning models to recognize urban legends with simple features.
Urban legends are a genre of modern folklore, consisting of stories about rare and exceptional events, just plausible enough to be believed, which tend to propagate inexorably across communities. In our view, while urban legends represent a form of "sticky" deceptive text, they are marked by a tension between the credible and incredible. They should be credible like a news article and incredible like a fairy tale to go viral. In particular we will focus on the idea that urban legends should mimic the details of news (who, where, when) to be credible, while they should be emotional and readable like a fairy tale to be catchy and memorable. Using NLP tools we will provide a quantitative analysis of these prototypical characteristics. We also lay out some machine learning experiments showing that it is possible to recognize an urban legend using just these simple features.