. DOI: 10.1016/j.lingua.2025.103931“) Correlation between network size and the frequency of the word "fuck." Credit: Lingua (2025). DOI: 10.1016/j.lingua.2025.103931

Americans use the f-word more frequently on social media than Australians or Britons, but Australians are more creative in its use. The f-word is rarely used in social networks of fewer than 15 people, and people tend to swear more with acquaintances than with friends, according to a recent study from the University of Eastern Finland.

The study published in Lingua, based on hundreds of thousands of social media users, examined swearing on social media and looked into whether people swear more when interacting with close friends or with acquaintances. According to lead author, Professor Mikko Laitinen at the University of Eastern Finland, swearing is a natural part of human language, making its study fundamental linguistic research at its best.

New insight from social media

Previous studies have suggested that swearing most commonly occurs with close friends, but those studies have relied on small datasets and surveys. Surveys, in particular, may fail to capture a phenomenon that is socially stigmatized.

In the present study, Professor Laitinen and his research team analyzed social media updates and Twitter networks from 2006–2023, covering nearly half a million individuals in thousands of social networks. The analysis also included metadata such as location and other contextual information.

Using computational methods, the researchers assessed how closely or loosely connected people within these social networks were. In the present study, the data were narrowed down to three English-speaking countries: Australia, the U.K. and the U.S.

Network size determines how much people swear

To account for the heterogeneity of social media communication, the study first identified more than 2,300 spelling variants of the f-word in the dataset. Then these were searched for in the massive dataset. Three interesting patterns emerged: The first is that the tendency to use the f-word clearly increases with acquaintances when compared with close friends. Second, swearing was rare in very small social networks, regardless of how close-knit they were, suggesting that network size is a key determinant of swearing.

Third, the same was also true at the other extreme, as in very large social networks, the importance of social network density faded away. In other words, the distinction between friends and acquaintances became irrelevant when the network size reached around 100–120 people. This finding echoes earlier research that shows, for instance, that trust is stronger in small networks than in larger ones, with the distinction between the two fading away at roughly one hundred members.

Research as a tool for identifying disinformation

Although swearing may seem like a light topic for academic research, profanity analysis could help identify fake profiles and those who intentionally spread dis-/misinformation on social media. According to Professor Laitinen, the findings and methods have broader applications.

"The more we know about language usage in social networks, the more we can learn about the behavior of genuine language users."

Laitinen points out that analyzing language alone is not enough, as generative AI can easily produce text. Instead, researchers should also examine the networks within which language is being used.

"These networks are extremely difficult to fake because they create a digital fingerprint for each user. They reflect a user’s previous social media behavior, making profiles identifiable."

Combining all these data with information on language use, e.g., swear word usage frequency within a particular network, can help determine whether an account is real or a source of disinformation whose profile does not match that of a real language user.

Such research requires both technical expertise and a thorough understanding of language use in social networks. This is why Professor Laitinen’s team includes not only linguists but also computer scientists. For example, understanding the complex processes involved in detecting disinformation calls for multidisciplinary collaboration.

Professor Laitinen believes that combining expertise in the humanities and linguistic research with computational capabilities to identify relevant patterns in vast datasets could underpin numerous future applications.

More information: Mikko Laitinen et al, Do we swear more with friends or with acquaintances? F#ck in social networks, Lingua (2025). DOI: 10.1016/j.lingua.2025.103931

Citation: People swear on social media more with acquaintances than with friends—analysis can help detect fake profiles (2025, December 6) retrieved 6 December 2025 from https://phys.org/news/2025-12-people-social-media-acquaintances-friends.html

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