Mikko Laitinen , Paula Rautionaho , Masoud Fatemi , Mikko Halonen
{"title":"Do we swear more with friends or with acquaintances? F#ck in social networks","authors":"Mikko Laitinen , Paula Rautionaho , Masoud Fatemi , Mikko Halonen","doi":"10.1016/j.lingua.2025.103931","DOIUrl":null,"url":null,"abstract":"<div><div>We investigate the uses of <span>fuck</span> in digital social networks from social media, Twitter/X in this case. Social media outlets have so far been predominantly treated as massive text collections, but they can be effectively used to investigate the role of social networks in shaping human communication. We use user-generated texts from 5,660 social networks (with 435,345 users and 7.8 billion words) from three settings (UK, US, and Australia). With embedded network information, this massive dataset enables us to investigate how network properties, that of the size and the strength of the network, influence the use of offensive words in these three settings. Our findings show that Americans use <span>fuck</span> most frequently, while Australians least frequently but they are highly creative with spelling variants of the word. Contrary to prior studies, we observe that people on this social media application swear more with acquaintances than with friends, but only in smaller networks − in larger networks of >100 people, the differences level out. Overall, this study highlights the benefits of using social media data that can be enriched to allow access to the social networks that people interact in.</div></div>","PeriodicalId":47955,"journal":{"name":"Lingua","volume":"320 ","pages":"Article 103931"},"PeriodicalIF":1.1000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lingua","FirstCategoryId":"98","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0024384125000567","RegionNum":3,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 0
Abstract
We investigate the uses of fuck in digital social networks from social media, Twitter/X in this case. Social media outlets have so far been predominantly treated as massive text collections, but they can be effectively used to investigate the role of social networks in shaping human communication. We use user-generated texts from 5,660 social networks (with 435,345 users and 7.8 billion words) from three settings (UK, US, and Australia). With embedded network information, this massive dataset enables us to investigate how network properties, that of the size and the strength of the network, influence the use of offensive words in these three settings. Our findings show that Americans use fuck most frequently, while Australians least frequently but they are highly creative with spelling variants of the word. Contrary to prior studies, we observe that people on this social media application swear more with acquaintances than with friends, but only in smaller networks − in larger networks of >100 people, the differences level out. Overall, this study highlights the benefits of using social media data that can be enriched to allow access to the social networks that people interact in.
期刊介绍:
Lingua publishes papers of any length, if justified, as well as review articles surveying developments in the various fields of linguistics, and occasional discussions. A considerable number of pages in each issue are devoted to critical book reviews. Lingua also publishes Lingua Franca articles consisting of provocative exchanges expressing strong opinions on central topics in linguistics; The Decade In articles which are educational articles offering the nonspecialist linguist an overview of a given area of study; and Taking up the Gauntlet special issues composed of a set number of papers examining one set of data and exploring whose theory offers the most insight with a minimal set of assumptions and a maximum of arguments.