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Avril Lavigne not more famous than Leonard Cohen: MIT

Avril Lavigne not more famous than Leonard Cohen: MIT

When given a tool that can potentially assess the cultural contribution of entire nations, what does the media do?

Use it to create celebrity “listicles” of course.

Pantheon, the brainchild of three MIT researchers, indexes historical and cultural figures — even pirates! — by their place of birth, Wikipedia pageviews and number of languages their Wikipedia entries are available in. The result allows users to see the cultural footprint of countries in aggregate, dating back to 4,000 B.C.

It can also churn out ranked lists of famous people from different countries. Russia, for example, has Vladimir Lenin and Leo Tolstoy, America gets Martin Luther King Jr. and Greece boasts both Plato and Aristotle.

In Canada, the most historically popular person is Avril Lavigne. The “Sk8er Boi” singer and one half of “Chavril” has Wikipedia entries in 103 languages and nearly 44 million pageviews, edging out the likes of Jim Carrey and Justin Bieber.

However, after CBC reported on Pantheon’s take on Canada, the researchers behind the project were forced to issue a clarification.

“Small differences in ranking are not statistically meaningful and should not be used to draw conclusions about the popularity of similarly ranked individuals,” reads a warning at the top of the site.

“We are NOT claiming that Avril Lavigne is the most famous Canadian, even though the CBC is telling their readers that this is our claim. Technically, her score is statistically indistinguishable from that of Leonard Cohen.”

The researchers also remind visitors that the project is far from complete or objective. Although based primarily on data from Wikipedia, the project aims to incorporate more datasets in the future.

“We do not consider Wikipedia editors to be a representative sample of the world population… Wikipedia editors have an English bias, a Western bias, and they tend to be highly educated and computer savvy,” reads a statement from their methods section.

However, by using data from all Wikipedia language editions, the researchers believe they’re reducing those biases.