There's a Link Between Extroverts and Their Most Common Word Choices

The findings will be used to help create online personality prediction tools.

Psychologists from Nanyang Technological University in Singapore have found a link between extroverts and their word choices, which can help marketers and advertisers better target certain audiences with their messaging. According to the study, an "extrovert consumer" will be attracted to marketing messages that match their personality with more extroverted and creative language. Extraversion is generally defined by psychologists as the tendency to experience positive emotions and enjoy social interactions.

women talking
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"This is the first time a relationship has been established between extroverts and their tendency to use the two categories of words. As it is a small correlation, we believe that stronger linguistic indicators are needed to improve machine learning approaches, amid rising interest in such tools in consumer marketing," Associate Professor Lin Qiu said.

The results of the study found a relationship between extraversion and the use of "positive emotion words" and "social process words." Words that described a pleasant emotional state such as "love," "happy," or "blessed," or that indicate positivity or optimism, such as "beautiful" or "nice," resonated with extroverts. Social process words include words containing personal pronouns except 'I', and words showing social intentions, such as "meet," "share," and "talk."

Researchers hope that their work will provide clarity on the relationship between other categories of words in order to create more accurate machine learning tools for certain personality types. The current models are not completely accurate due to a lack of theoretically sound designs, according to the study. "Current machine learning algorithms for personality prediction can seem like a black box—there are many linguistic indicators that can be included in their design, but many of them are dependent on the type of computer application used. This may lead to biases and overfitting, an error affecting the performance of the machine learning algorithms. This begs the question—how should we create robust and accurate personality predictions?" says Qiu.

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