A mathematical measure of surprise explains why we fall for clickbait
Why are we so susceptible to clickbait? Columnist Jacob Aron traces it to a surprising source: a mathematical framework that paved the way for much of how the modern world works
Claude Shannon's information theory, developed in 1948, elucidates why certain phrases captivate us, particularly in the realm of clickbait. The mathematician from Bell Labs in New Jersey aimed to address communication challenges, especially with poor signals. Shannon's theory posits that the specific meaning of a message is irrelevant; instead, it focuses on the surprise or novelty of a message.
This concept is mathematically represented by Shannon entropy, calculated using the formula H = -∑p(x)log(p(x)), where x is the message, p(x) is the probability of x occurring, and the logarithm represents a reverse exponent, with base 2 yielding units called bits, or binary digits. A fair coin flip, with a 50% chance of heads or tails, transmits 1 bit of information, while a doctored coin that always lands heads conveys no new information.
Applying this to clickbait, phrases like "Trump wins election" convey more information than "You’ll never believe who just won the election" despite having fewer words. The former is more surprising, hence more engaging. However, the theory doesn't account for the accuracy or relevance of the conveyed information. Shannon's work laid the foundation for modern digital communications, with the binary system underpinning all our digital interactions today.
Written by urgent.news from New Scientist's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
