‘Gaslighting’ is Merriam-Webster’s word of the year
NEW YORK, United States (AP) — “Gaslighting” — behaviour that’s mind-manipulating, grossly misleading and downright deceitful — is Merriam-Webster’s word of the year.
Lookups for the word on merriam-webster.com increased 1,740 per cent in 2022 over the year before. But something else happened. There wasn’t a single event that drove significant spikes in curiosity, as it usually goes with the chosen word of the year.
The gaslighting was pervasive.
“It’s a word that has risen so quickly in the English language, and especially in the last four years, that it actually came as a surprise to me and to many of us,” said Peter Sokolowski, Merriam-Webster‘s editor at large, in an exclusive interview with The Associated Press ahead of Monday’s unveiling.
“It was a word looked up frequently every single day of the year,” he said.
There were deepfakes and the dark web. There were deep states and fake news. And there was a whole lot of trolling.
Merriam-Webster‘s top definition for gaslighting is the psychological manipulation of a person, usually over an extended period of time, that “causes the victim to question the validity of their own thoughts, perception of reality or memories, and typically leads to confusion, loss of confidence and self-esteem, uncertainty of one’s emotional or mental stability, and a dependency on the perpetrator”.
More broadly, the dictionary defines the word thusly: “The act or practise of grossly misleading someone, especially for one’s own advantage.”
Gaslighting is a heinous tool frequently used by abusers in relationships — and by politicians and other newsmakers. It can happen between romantic partners, within a broader family unit, and among friends. It can be a corporate tactic, or a way to mislead the public. There’s also “medical gaslighting”, when a health-care professional dismisses a patient’s symptoms or illness as “all in your head”.
Merriam-Webster, which logs 100 million pageviews a month on its site, chooses its word of the year based solely on data. Sokolowski and his team weed out evergreen words most commonly looked up to gauge which word received a significant bump over the year before.
