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The Left and the Right Speak Different Languages—Literally

A study analyzing patterns in online comments found that liberals and conservatives use different words to express similar ideas.

Researchers at Carnegie Mellon University collected more than 86.6 million comments from more than 6.5 million users on 200,000 YouTube videos, then analyzed them using an AI technique normally employed to translate between two languages.

The researchers found that people on opposing sides of the political divide often use different words to express similar ideas. For instance, the term “mask” among liberal commenters is roughly equivalent to the term “muzzle” for conservatives. Similar pairings were seen for “liberals” and “libtards” as well as “solar” and “fossil.”

“We are practically speaking different languages—that’s a worrisome thing,” KhudaBukhsh says. “If ‘mask’ translates to ‘muzzle,’ you immediately know that there is a huge debate surrounding masks and freedom of speech.”

In the case of politically tinged comments, the researchers found that different words occupy a similar place in the lexicon of each community. The paper, which has been posted online but is not yet peer reviewed, looked at comments posted beneath the videos on four channels spanning left- and right-leaning US news—MSNBC, CNN, Fox News, and OANN.

KhudaBukhsh says social networks might use techniques like the one his team developed to build bridges between warring communities. A network could surface comments that avoid contentious or “foreign” terms, instead showing ones that represent common ground, he suggests. “Go to any social media platform; it has become so toxic, and it’s almost like there is no known interaction” between users with different political viewpoints, he says.

But Morteza Dehghani, an associate professor at the University of Southern California who studies social media using computational methods, finds the approach problematic. He notes that the Carnegie Mellon paper considers “BLM” (Black Lives Matter) and “ALM” (all lives matter) a “translatable” pair, akin to “mask” and “muzzle.”

“BLM and ALM are not translations of each other,” he says. “One makes salient centuries of slavery, abuse, racism, discrimination, and fights for justice, while the other one tries to erase this history.”

Dehghani says it would be a mistake to use computational methods that oversimplify issues and lack nuance. “What we need is not machine translation,” he says. “What we need is perspective-taking and explanation—two things that AI algorithms are notoriously bad at.”