Jihoesk univerzita v - PDF document

1 Jihočeská univerzita v Českých .ud
Jihočeská univerzita v Českých .udějovicích tedagogická f akulta Katedra anglistiky .akalářská práce English Vulgarisms in Internet Communi cation Anglické vulgarismy v internetové komunikaci Vypracoval: Vojtěc h Kříž Vedouc í práce: Mgr. Jaroslav Emmer České .udějovice 2 0 20 Prohláš ení Prohlašuji, že svoji bakalářskou - diplomovou - rigorózní - disertační práci jsem vypracoval/a samostatně pouze s použitím pramenů a literatury uvedených v seznamu citované lite ra tur y. Prohlašuji, že v souladu s § 47b zákona č. 111/1998 Sb. v platném znění souhlasím se zveřejněním své bakalářské - diplomové - rigorózní - disertační práce, a to v nezkrácené podobě - v úpravě vzniklé vypuštěním vyznačených částí archivovaných ... f ak ult ou elektronickou cestou ve veřejně přístupné části databáze STAG provozova né Jihočeskou univerzitou v Českých Budějovicích na jejích internetových stránkách, a to se zachováním mého autorského práva k odevzdanému textu této kvalifikační práce. Souhlas ím dá le s tím, aby toutéž elektronickou cestou byly v souladu s uvedeným ustano vením zákona č. 111/1998 Sb. zveřejněny posudky školitele a oponentů práce i záznam o průběhu a výsledku obhajoby kvalifikační práce. Rovněž souhlasím s porovnáním textu mé kval if ika ční práce s databází kvalifikačních prací Theses.cz provozovanou Národním registrem vysokoškolských kvalifikačních prací a systémem na odhalování plagiátů. V Českých Budějovicíc h dne 4. 1. 2021 Vojtěch Kříž Poděkování Rád bych touto formou poděkoval Mgr , Jaroslavu Emmerovi za odborné vedení m é

2 práce a jeho cenné rady , bez nichž
práce a jeho cenné rady , bez nichž by tato práce nemohla vzniknout . A NO TACE Tato bakalářská prá ce se zabývá užitím vulgarismů v komunikaci anglicky mluví cího internetu. Internet je specifický svým velmi d ynami ckým prostředím, a tudíž i mnoha způsoby kom unikac e . Teoretic k á část se zabývá vymezením pojmu „ taboo langu age “ a je ho typol ogi í . V rámci této části je bližší pozornost věnována vulgarismům . Závěr teo retické č ásti se zab ývá přiblížením specifik internetové kom unikace . Prakt ická část obsahuje popis zkoumaných stránek a následně obsahuje samotný výzkum a rozbor internetového jazyka na vybraných internetov ých stránkách. Sesbíraná data jsou podro bena analý ze pomocí korpusového so ftwaru # LancsBox. Data jsou pot é vlo žena do přehledných tabulek a grafů, které znázorňují nej častěji používané výrazy a jejich počty. A na lyzované stránky jsou na závěr porovn ány co se týče frekvence u žívání vulgárních vý razů. Práce zkoumá, kdy se vulgární výrazy na internetu používají, a zda - li se používají více než v mimo něj. Klíčová slova: internet, vulgarismy, Reddit, Twitch, on - line forum, live - streaming ABSTRACT This Bac helor ’ s thesis deals with the us age of v ulgarisms in the communicat ion of the English - speaking inter net. The in tern et is well know n for its ver y d ynamic environment , as well as its man y forms of communicat ion. The theoretical part of th e thesis deals with specif ying the term “ taboo language “ and its t ypology. This part als o provides a closer look at vulg arisms spec ific ally. The ending of the theoretical part concentrates on int rodu cing the spe c ifi cs of internet communication . The pra

3 ctical part of the thesi s contains a
ctical part of the thesi s contains a brief overview of the e xamined websites , and furthermore , it contains the analysis of the internet lan guag e of t he chos en w ebs ites . The collected data are analy z ed wit h the help of a corpus toolbox software # Lan csB ox. The data is afterword s pu t into tables an d graphs, t hat show which vulgarisms are used and in what quantities. At the end of this thesis , th e analyzed websites are compared in terms of the frequency of using vulgar isms. The thesis anal yses when are vulgar expressions used on the i nternet, and w h ether they are used more ofte n than in everyday communication. Keywords: internet, vulgarisms, Redd it, Twitch, on - line forum, live - streaming TABLE OF C ONTENT 1 INTRODUCTION ................................ ................................ ................................ ................................ ..... 7 2 THEORETICAL PART ................................ ................................ ................................ ............................... 7 2.1 WHAT IS TABOO? ................................ ................................ ................................ ....................... 7 2.2 TABOO LANGUAGE ................................ ................................ ................................ ................... 8 2.2.1 TYPES OF TABOO LANGUAGE ................................ ................................ ................................ .. 9 2.2.2 VULGARISM ................................ ................................ ................................ ........................... 10 2.2.3 WHY IS TABOO LANGUAGE USED? ................................ ................................ ....................... 11 2.3 SPECIFICS OF INTERNET COMMUNICATION ................................ .............

4 ................... .... 12 3 PRACTI
................... .... 12 3 PRACTICAL PART ................................ ................................ ................................ ................................ .. 13 3.1 TWITCH.TV ................................ ................................ ................................ ................................ 13 3.1.1 METHOD OF RESEARCH ................................ ................................ ................................ ......... 14 3.1.2 INDIVIDUAL SPECIFICS OF TWITCH.TV ................................ ................................ .................. 16 3.1.3 OVERALL SPECIFICS OF TWITCH.TV ................................ ................................ ....................... 18 3.1.4 STREAMERS ................................ ................................ ................................ ........................... 20 3.1.4.1 WOLFSGORAWR ................................ ................................ ................................ ................................ ......... 20 3.1.4.1.1 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 20 3.1.4.1.2 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 21 3.1.4.2 FROSTPRIME ................................ ................................ ................................ ................................ ............... 22 3.1.4.2.1 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 23 3.1.4.2.1 ANALYSIS ................................ ................................ ..........................

5 ...... ................................
...... ................................ ................................ . 23 3.1.4.3 TOBIAS FATE ................................ ................................ ................................ ................................ ............... 25 3.1.4.3.1 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 25 3.1.4.3.2 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 25 3.1.4.4 FOLLOWGRUBBY ................................ ................................ ................................ ................................ ....... 27 3.1.4.4.2 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 28 3.1.4.4.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 28 3.1.4.5 ELAJJAZ ................................ ................................ ................................ ................................ ........................ 30 3.1. 4.5.1 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 30 3.1 .4.5.2 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 31 3.1.4.6 LOLTYLER1 ................................ ................................ ................................ ................................ ................. 3

6 2 3.1.4.6.1 ENVIRONMENT ............
2 3.1.4.6.1 ENVIRONMENT ................................ ................................ ................................ ................................ ........................ 33 3. 1.4 .6.2 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 33 3.1.5 OVERALL RESULTS FOR TWITCH.TV ................................ ................................ ...................... 35 3.2 REDDIT.COM ................................ ................................ ................................ ............................. 37 3.2.1 METHOD OF RESEARCH ................................ ................................ ................................ ......... 38 3.2.2 SPECIFICS OF REDDIT.COM ................................ ................................ ................................ ... 40 3.2.3 SUBREDDITS ................................ ................................ ................................ .......................... 41 3.2.3.1 R/GAMING ................................ ................................ ................................ ................................ .................... 41 3.2.3.1.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 42 3.2.3.2 R/NEWS ................................ ................................ ................................ ................................ ......................... 44 3.2.3.2.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 44 3.2.3.3 R/MEMES ................................ ..............................

7 .. ................................ ....
.. ................................ ................................ ...................... 46 3.2.3.3.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 46 3. 2.3.4 R/TODAYILEARNED ................................ ................................ ................................ ................................ .. 48 3.2.3.4.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 48 3.2.3.5 R/FUNNY ................................ ................................ ................................ ................................ ...................... 50 3 .2. 3.5.1 ANALYSIS ................................ ................................ ................................ ................................ ................................ . 50 3.2.4 OVERALL RESULTS FOR REDDIT.COM ................................ ................................ .................... 52 3.3 COMPARISON OF TW ITCH.TV AND REDDIT.COM ................................ ........................... 53 4. CONCLUSION ................................ ................................ ................................ ................................ ...... 55 5. SOURCES ................................ ................................ ................................ ................................ ............. 56 7 1 I NTRODUCTION Although the terms “ taboo language ” and “ vulgarisms ” are not too often spoke n ab out, the y are a natural part of any spoken language. Emotionally demand ing situations require expressions suited for such occasions, which mi ght often be the exact time, at which people r

8 each for a vulgar expression that w
each for a vulgar expression that would s uit the situation. The inte rnet is a fast - g rowing medium that seeks to outperform any other for m of media that human kind has ever known . It is used in many way s, but one of the most imp ortan t uses of the internet is co mmunication. Extr aordina ry amount s of data are uploaded ont o the internet every d ay, a nd they do not seek to stop in any foreseeable future. Another use of the intern et is enterta inment . Masses of people brows e the interne t eve ry day to entertain themselves in any way they see fit, w h ether it is watching videos, sh arin g pictures, find ing out new information, and so on. Two popular sites used mainly for entertain ment are Twit ch. tv and Reddit.com. Many people visit both si tes d aily to reach the entertainment of their choice, w h ether it is watching a live stream on Twit ch.tv and commun icating with other viewers vi a the live chat room that accompanies every stream , or browsing their favo rite subr eddit to re ach one of t he ma ny for ms of entertainment that Redd it .com provides . Both of the mentioned sites prov ide a for m of communica tion to their users. And where there is language, there are vulgarism s . Re sea rcher s in t h e 200 0 s found out , that taboo words make up approximat ely 0 ,5 % to 0,7% pe rcent of spoken lan guage (Jay, 2009). Th is thesis aims to find out , whether the v eil of at leas t partial internet anon ymity makes people on the internet use vulgar expressions more fre q uently than they w ould in spoken language. 2 THEORE TICAL PART 2. 1 W HAT IS TABOO? According to the Cambridge Dictionary ( Cambridge Di ctionary , 20 20a ) , t aboo is defined as a subject , wor d , or action th at is avoided for r eligious o r socia

9 l reasons. Acco rding to the Oxford L
l reasons. Acco rding to the Oxford Learner ’ s Dictionaries ( Oxford L earne r ’ s Dictionaries , 2020 ) , 8 it is defined as a cultural or religious custom that does not allow peo ple to do, use, or talk about a partic ular matter , as people find it of f ens ive or embarrassing, or a s a gen eral agre e ment not to do or talk about somethi ng. The word itself originates from Polynesian, but the use of the term is now widespread ( Online Etym olog y Dictionary , 2020 ) . Taking this into consideration , it can be said that taboo is something that should be avoided. The pro ble m with this might arise when we consider that what is taboo and what is not i s very often based on culture, o r religion , whic h ma y d iffer significantly in di fferent parts of the world ( W hite, 2014 ) . Examples of ta boos that are present in most p a r ts of the worl d are mu rder, racism, i nc est , pe dophilia , etc. These taboos are commonplace and are best to be avo ided anywhere in the wo rld. Bod y fun ction tabo os such as belching, spitting , or defecation , when p er form ed in fron t of other people , are also very o ften presen t. Taboos that are no t c ommonplace usually relate to culture and religion. As an example of a religious taboo, t he Isla m re ligi on strictly forbids the consumption of alcohol and pork in an y form . Simi larly, it is forbid d en to consume beef in Hi nduis m ( Socio logy Group, 2018 ) . Premarital sex is also considered taboo in many religions. Cultur al tab oos o n the other hand relat e to the specific country or region. To give an exampl e, in Japan i t i s considered taboo to wear shoes indoor s an d considered r ude to point at someon e with your chopsticks ( Socio logy Group, 2018 ) . Taboo is a broad to

10 pic, a nd it can be based on a g
pic, a nd it can be based on a great va r iety of f actors. R anging from where are you from , what you beli eve in , to what your moral standards and belie fs are, taboo can ma ni fest it self in many ways. T his might be o ne of the reaso ns why taboo has been studied from m an y perspective s and by many fiel ds, inclu ding phil osoph y, sociology , psychol ogy , an d linguistics . 2. 2 T ABOO LANGUAGE Taboo langu age , also called linguisti c tab oo or taboo words , are word s , that are to be avoided entirely, or at least in “ polite company ” ( Ak majian, 2 010) . Alternative ly, the ter m “ taboo words ” can be used to “ descri be the lexi con of offen s ive emotional language” (Jay, 200 9) . Same as behavio r a l tab oo, what is or is not considered taboo language is defined by culture, and not by anyth ing inherent in the language itself. The sound of t he 9 expre ss ions in question is not t he reason that they ar e not to be used in polite comp any, the reason is s impl y t hat in the process of lear ning the lang uage, its users we re t aught th at the se are “ swear words ” ( Ak majian, 2 010 ). In today's soci ety, people know very well when it is, and when it is not appropriate to use taboo lan guage . Throughout the pro cess o f people being brought up, they are taught by the ir elders that taboo langu age is not to be used at all ( Jay, 200 9 ). The principle of b e ing pu n ished for utter ing the wrong wo rd a t the wrong ti me is solidly embedded into peoples ’ minds. This is some thing that ev erybody is taught from an early age. But what if that is not the case? What happens when we are not physically present with the peop le that we use taboo lang uage with? Wh en us in g the int

11 ernet , people are very often cove red
ernet , people are very often cove red by at least partial anon y mity . How do people behave in this environment and what instances of taboo language do they use? When the threat of immediate punis hment is no longe r present, the wa y people ex pre ss t hemselves coul d significa ntly change. 2. 2.1 TYPES OF TABOO LANG UAGE Ta boo langu age is quite a wide concept and diffe rent au thors may ca tegori z e it in d ifferent ways. According to Edwin Bat t istella , there a re f our c ate gorie s of taboo language . Name ly, thes e categories ar e e pithets, p rofanity, vulgarity, and o bscenity (Bat tistella, 2007) . In t he cont ext of taboo language , an epithet c an be defined as “a disparaging word o r phrase ” ( Merriam - Webster Dictionary , 20 20 a ) , or as “a word , ph rase o r expression used invectively as a ter m o f ab use or contempt, to express hosti lity, et c. ( Dictionary .c om , 2020a ). Epithe ts are v arious types of slurs. These slurs usually refer to race, ethnicity gender, sexuality , appear an ce , disability , or other ch aracteri stics. Exa mples of epithe ts wo uld be w or ds like bitch, fag, mi dget, retard ( Bat tistella, 2007 ) . Pro fani ty in its core is a term related to religion. To be profane is to show no respect t o god or religi on , specifically th roug h lang uage ( Cambridge D ictionar y , 2020b ) . On a wider scale, t he ter m “prof anity ” can nowadays be considered syn onymous with cussing , cu rsing , or swearing ( Merriam - Webster Dictionary , 2020 b ). Going back to the original meaning, profanity is considered t o be religious curs in g, 10 c o a rsely us ing what is tak en to be sacred . The r a nge o f profanity can be f ro m mild hell or damn to emphatic god damn ( Bat tistella, 2007 ). Both terms, vulg

12 arity , an d obscenity , refer to word
arity , an d obscenity , refer to word s or expressions which characte ri z e sex - di f fere ntiating a natomy or sexu al and e xcretory functions in a crude way , wit h the primary differentiator between the two terms being their deg ree o f prurience ( Bat tistella, 2007 ) . Something obscene can be defined as something that goes strongly agains t the m oral code . It can also refer to the use of taboo lang uage in poli t e speech ( Mer riam - Webster Dictionary , 2020 c ) . Vulgar is not far from t his definit ion, since it refers to something lacking taste, something being morally crude or undeveloped, or it simply d e sc r i bes a term that is offe nsi ve in the context of language ( Merriam - Webster Di c tionary , 2020 d ) . Some terms used in thes e categories are fuck or shit ( Bat tistella, 2007 ) . It is important t o reali z e that the categories mentione d above are not exclusive, a nd c ertain expressions may bel ong to more t han one of the cate g o ries. An example of this fact is the exc lamation God fucking dammit ( Bat tistella, 2007 ) . 2 . 2. 2 VULGARI S M Alth ough the prior part of th is work pr ovi des s ome definitions , there is one definit ion of vulgari s m that ha s be en left out on purpose . This p articular definition sa ys t hat vulgarism is a wor d o r an expression that has or iginated from or is mainly used by a n illiterate pe rson ( Merriam - Webster Dictionary , 2020 e ) . Anoth er defin ition is quit e si milar , although it m entions vulgarisms mainly being used in coarse colloqui al spe ech ( Dictionary .com , 2020 b ) . Be lo w the f irst of these tw o definitions, the online dicti onary provides synonymous words to the term, which contain word s such a s curse, cuss , di rty wo rd, ob scenity, profanity , or swearword . Con

13 sider in g this de finition, a w o
sider in g this de finition, a w orking definition of the term that is worked with with in this t hesis can be formed . The t erm “ vulgarism ” throughout this thesis refers to a ny use of coar se language or inapp ropr iate u s age of lang u a ge o n the examined s ite s. The original Latin term vulgus means “ co mmon people ” ( Online Latin Dictionary, 20 20 ) , thus the langu age that is to be examined will be as such. Name - calling, obscenity, profa nity, cursing, e tc. are all cons ide r e d vulgari sms for the sake of this thesis. This helps a chieve a greater samp le s ize and overall improve the qua li ty of the gathered dat a . 11 2. 2.3 W HY IS TABOO LANGUAGE USED? Q uoting Jay (2009,155) : “ Swearing is like using the horn on you r car, w hich can b e used to signif y a n u mber of emotions (e.g., anger, frustration, joy , surpris e ) ” . Taboo words can be used to achieve multi ple outcomes f rom multiple p e ople. The outcome of their use can be positive , negative , or even ultimatel y incons eq ue nti a l when i t com es to t he impact on others ( Jay , 2009 ) . Jay continues by s tati ng that we primarily use swearing fo r em otional connotation. This occurs in the form of epi t hets or in sults d irected to wards others. ( ibi d . ) Another rea son for u sing taboo wor d s would be t o a ch ieve a specific reaction from others. It injects a dire ct e motional component into the discussion that helps to express the fe eling of anger, frustration, or su rprise. Insulting form s of taboo words include name - cal ling, c u r sing , or w i shing harm to so meo ne . Taboo words are one of the defining features of discr imin ation, hate speech, sexual harassme nt, verbal abuse, etc. Thes e us es of taboo words usually lead to negat

14 ive outcomes ( ibid. ) . Not all
ive outcomes ( ibid. ) . Not all uses of taboo w ords are negative , h o wever, Jay c onti nu e s to point out that tabo o words have p os itive uses in so me o f their f orms. These forms include h um or and jokes, soc ial com mentary, sex talk, self - deprecation , storytelling , or ironic sarcasm . He also notes that a pos itive ou tc ome can b e achieved whe n phys i cal v iolence is replaced by speech ( ibid. ) . When it com es to inconsequential uses of t aboo words, Jay mentions that many uses of these words are mere casual conversation habits. These uses lack a clear soc i al mot iv e ot her than fitting in wi th ot her s’ info rmal use of taboo words. This kind of use, whic h ma y not be intended to be inhe rentl y offensi ve, can still be regard ed as impolite or rude by the speakers who are not directly involved in the conversation ( ibid. ) . The uses of t aboo w o rds are multi ple . They can be used in a cathartic wa y , the wa y of emo tional releas e that h el ps us handle an unplea sant situation better o r even to help us endure pain more eff e ctivel y (S tephens, 2009) . They can be used to ins ult or e xclude someone. But the y can al so be used in your friend group as terms of endearment ( J orda n , 2018 ) . As Jay states in his article , the lexicon of taboo words can somewhat be compa red to a toolbox. It can be used for a wide range of emotion al expre ssions and one can achi e ve a g r eat n umber of s ocial a nd pe rsona l go al s by usin g them ( Ja y , 2 009 ) . According to him, swearing is a unique human be havio r that h as developed for a 12 purpose . T aboo words have the a bili ty to convey emotions t hrough commun ication that non - taboo words si m ply do not ha ve ( ibid. ) . 2 . 3 S P

15 ECIFICS OF INTERNET C OM MUN I CAT ION
ECIFICS OF INTERNET C OM MUN I CAT ION Throughout its existence , people have been us ing the internet in a myr iad of way s. One of the most important of its uses now a days i s clea r and almost insta ntaneous communication. Some o f the way s the internet can be used for communication a re inst ant messaging, email, VoIP (aka. Voice over Internet Protocol ), Videoconfer encing , or Internet Relay Chat. ( Call Hippo, 2020 ). T h e internet fundamentally provides two types of communication, asynchronous co mmunication , and s ynchr onous communicati on (Nebojteseinternetu, 2020) . Asyn ch r onous communication does not require the imm e diate reaction of the addressee . This means that once the message is sent , it takes from minutes up to days until the recipient replie s. Examples o f a s ynchr on ous communication are e - mails or online forums ( Holloway, 2020) . Sync hronous communication on the other happens w hen messages can only be exchanged in real time. It is used when we require the immediate reaction of the addressee . It requ ires direct in ter action of its members ( Nebojteseinternetu, 2020 ). Examples of synchronous communication are chatting programs s uch as WhatsApp or Messenger , or programs that allow their users to ta lk directly, such as Skype or Discord. A s the internet evolved , so did the lang uage that its users employ. This gave birth to “ internet slang ” . Internet slang has many forms, which were created for various reasons. S ome of them were simply created to save keystrokes and make on - line communication devices such as chatting fas t er. These include letter hom ophones ( ty ping “ CU ” instead of typing out the entire phrase see you ) and acronyms made out of initial letters of words ( “ LOL ” meaning laugh ing out loud )

16 (Wikipedia, 202 1). A pa rticularly
(Wikipedia, 202 1). A pa rticularly interesting type of internet slang is “ flami ng ” . Flaming is the act of posting or sending offensi ve messages over the internet ( Techterms, 202 1). Originally meaning t he act of sending an angry or insulting e - mail ( Cambridge Dictionary, 2021), flamin g is currently comm on throughout the internet. Curr ently, flaming does not only on e - mail, but can occur on p ractically any platform that allows its users to communicate. Discussion for um s , live chats , e - mail , video or audio call, all of these means or internet communication are where flam i ng can occ ur . 13 3 PRACTIC AL PART 3.1 TWITCH.TV ( source: http s://www . pcmag. com/reviews/twitch ) Twitch.tv is a vid eo streaming platform lau nched in 2011 , that is operat ed b y Twitch Interactive and owne d by A ma zon. The website ma i nly focuses on video game live streaming. These broad ca sts include major esports events and com p eti t ions, music br oa dcasts, crea tive content ( painting, 3D mode ling , d rawing, etc. ) or the most rec ent addition to th e roster, the “ I RL ” (a ka. “ In Real Life ” ) o r otherwise known as the “ Just Chatting ” secti on, in w hich the broadcas ter simply doe s whate v er they want ( as long as the Terms of Service al low so) and i nteracts with the chat while doing so ( Wikipedi a, 2020 ) . The broad ca st s a re most often r u n by indiv id ual s ca l led “ streamers ” , that have ch osen broadcasting on T witch a s the ir hobby , and many of them eve n as their m ain sou rce of income. The con t ent on the w ebsite is availab l e in two forms : watching the bro ad cast live or watch in g a recording of the broadcast , whi c h t he s i te saves automati cally . These recordings are c alle

17 d “ Video on Dema nd ” or “ VO
d “ Video on Dema nd ” or “ VOD ” for sho rt. Ove r the 9 ye ars of its existe nce, t he site has a c hieved wides pread po pularit y. Over this time, t he site has b uil t a m as sive , lo yal audience . The increa s e i n vi e wer ship was so significant that it led the American giant Amazon to buy Twitch in August o f 2014 for al most $ 1 billion ( Vide oGamesS tats, 2020 ) . The site Twi tchT rac k er .com prov ides insight i nto how exactly the si te grew in terms of viewership, as well as the numb er of broadcaste r s that h ave picke d T witch as the ir streaming platform of choice. T he graph below shows the s ta tistics: 14 ( Source : http s://twitchtracker .com/statistics , screenshot t aken on January 2 nd 2021) Th e popul arity o f t he s ite mig ht a lso be attrib uted to the fact that when it comes to video game live streaming, there is little competit io n , as Twit ch decisiv ely domin at es the scene ( Mal lick, 2020 ) . Most promi nen t stream ers, use r - f riendly inter face , and the fact that Twitch can be enj oyed absolutel y free a r e just some of the pros t hat sol idify T w itch as the go - to plat form for a great number of viewers as well as broadcas ters. Other we bsites such as Y ou Tube or now no n - exist e nt Mi xer ha ve tr ied t o compete with Tw itch in terms of video g ame live streami ng, bu t their attempts have thu s fa r prov en unsu c cessful. Furthermore , T witch as of late has been attracti ng other content c reator s apart from video game broad caster s, as th e Mus ic, Ar t , and “ Ju st C h a ttin g ” sections seem to be steadily gro w ing by the d ay. A ll the above - mentioned facts le d to choosing Twitch.tv as one of the primary sources of data for this work . Th e l

18 ive stre a m is always ac com panied
ive stre a m is always ac com panied by live chat, where people post their me ssages a nd talk about topic s ranging from commen ting on what i s happening on the st ream an d commu n i catin g with the st reamer to simply chatting with other use rs . Some streams can have up to t en s of thousands o f viewers at a time , plus t he fa ct tha t the en vironment of the stream may vary significantly f r om s tr eamer to streamer are just some of the factors that make Twitc h .tv a suitable source of material for this work. 3. 1.1 METHOD OF RE SEA RCH Twitch is a li ve - streaming platform . A st reamer broa dcasts the content of their choice to the pe ople who choose t o join the stream. The people who do so may or may 15 not choose to a lso engage in chat . The following dis plays how a typical Twitch stream might l ook like : (sour ce: https://www.twitch.tv/arvius ) O n the left , there is the wi n do w of the stream i tself. Whate ve r the st reamer decides to br oadcast shows there . An d on the right is the chat window. T he chat is how the viewers communicate with the s t r eamer and vice versa. In this stu dy, each stream was s tudied in tw o ways. Firs t ly , the main focus was o n the chat . Th e data w ere recorded throughout one entire broadcast and put into a text file . Thi s was done via software called Chatty, which automatic al ly save s the c hat log from the entir e broa d cast. T he t ext file was th en importe d i nto a software ca lled # LancsB ox . #La ncsBox is a corpus analysis tool that helps anal y z e the t ext in great detail. Once the full chat log is imported into #La ncsBox, th e text is analy z ed b y u sing the “ K WIC ” f u n ction. This function allows the user to se arch for s pe

19 cific words and/or word grou ps. Usin
cific words and/or word grou ps. Using an integrated function of the software, typing “ SWEARWORDS ” into the se arch bar high li ght s certain swearwords that are present i n the text . Th is is a us eful tool, b ut it d oes not highlight all the soug h t - after expressions , thus a m anual search fo r certain expressions nee ded to be made using the “ KWIC ” funct ion. 16 Afterw ard , the “ Word ” function of # LancsBox was used, which allows the user to sea rch for alternative variants of or a word, to find variations of t he sought - after expressions. List of swea rwords integrate d into #LancsBox : arse, arsehole, bastard, b e lle nd, bint, b itch, bloodc laat, bloody , bollocks, bugger, bullshit, clung e, c ock, cra p , cun t, da mn , dick, dic k head, fanny, feck, fuck , gash, git, god , god dam , J esus, minge, minger, motherfucker, munte r, piss, prick, punani, pussy, shit, sod , tit, t wat . Variations of the se expressio ns ar e included. List of manually searched expr essi ons: ass , cum, slut , whore . Abbreviated forms of searched expression s : BS (st and s for bullshit ), FFS (stands for for f uck ’ s sak e ) , LMAO (stands for laughing my a ss off ), LMFAO ( stands for laughing my fucking ass off ), WTF ( stands for what the fuck ) . Va rian ts of these ex press ions are included. After analy z ing the text data , t h e soug ht - after expressions were put into tables and graphs to fur the r show how of ten they are use d. The text was then revised in detail using the “ Text ” option of #LancsBox, which a llow s the user to go th rough the entire te xt one instance of a n expression at a time. Th is was done to filte r out the po ss ible spam that might have alter ed the data. This means that a message bei

20 n g spammed an d repeated m u ltiple ti
n g spammed an d repeated m u ltiple times that cont ains a vulgar exp res sion was on ly c o unted a s one ins tan ce of a vulgarism being used. Secondly, the fo cus of the ana lysis is on the stre amers themselves. It is not un usual to see that the language of the streamer often refle cts on the chat. This means that a t least a pa rt o f the sample s iz e m i ght be chat users simply mi mick ing something that the str eam er said , and th us the sampl es of the l anguage might differ in a noticeable way from channel to channel. 3.1.2 INDIVIDUAL SPE C IFICS OF TWITCH.TV There are many things to be c on sidered wi th each in dividua l channe l. E ach of them is unique in one way or ano ther and no two T witc h channels a re id entical . The aspects to consider includ e the game that is being broadcast ed, th e broad cast e r s themselves, the ta r get audience of the strea m, the n at iona lit ie s of the audience, t he time at which the broadcast takes p lac e , the number of viewer s the broadc ast has, etc . All of these are to be considered to pr operly identify wh y the streamer and the users in the chat behave the way they do in ter ms of using vu lgari sms. 17 There are multiple types o f twitch vi ewers. Some of t hem watch certain channels for the str eam er and their personali ty, while other s are watching solely for th e conten t that is being b r oadcasted. This means that some of the use rs mi gh t frequent o nly o ne ca tegory of content on Twitch, while others only watch s pe cific broadcasters rega r d less of wh at content the y decide to broadcast. Every Twitch streamer is unique . The p ersonality of the streamer is a factor that has an undeniable i mpa ct on the stre am. It is up to the streamer s to decide w hat game they broadcast , how

21 they beha ve towards the game , the
they beha ve towards the game , the chat of their stream o r their possible teammates/en emies and how th ey choose t o express the ir emotions . This nature of th e br oadcaster t en d s to be ve ry cl e arly reflected by the users in chat. A lo ng - time viewer of a cert ain streamer ten ds to pick up their speech habits, often used words or expressions that th ey might have not used o therwise. In certain ways, the streamer can d irectly choose th e target au dience of their stream. One of these ways is se tti ng the minimum age of the viewer s tha t can join the st ream. According to the Twitch Te rms of Service, all Twitc h users are required to be at least 13 ye ars of age to use the webs ite. Some stre am s a re f it fo r all 13+ viewers , but some are not. I f the str eam er s themselves choose to target a mat ure aud ience with their broadcast, they can do so by switching on a settin g, that disp lays t h i s m e s s a g e w h e n e n t e r i n g t h e s t r e a m : (source: ht tps://www.twit ch. tv/forsen ) 18 This way, the viewer is requ ired to con f irm t hat they are mature before entering the stream . By using th is feature, the streamer can filter the viewers that jo in t he ir stream . When the st reamer s know t hat the au dience of their stream is mat ure , the strea mers may be more prone to usin g mature lan guage . T he nation alities of the viewers and the nationalities of the st ream ers are fairly closely l i nked to t he time at which the broadcast s take place. When the st reamer is from th e USA for example , the viewers th ey attract are most likely to be from the same o r a similar time zone . This is due to the fa ct the streaming time of the streamer can be in t h e middle of the n ig ht for the European vi e wer. The principl

22 e works the other way around . Thi
e works the other way around . Thi s way, A merican E nglis h - speaking streamers are more likely to attract o ther Am eri can English - speaking view ers, while European streamers often attract English - speaking viewer s of Europea n or igin , who se first language may not be English. Th is can affect the streamer s as well as v iewers in multiple ways . Being a nat i ve English - speak ing vi ewer may prompt t hem to be more cautious when using vulgarisms or making them more vulgar by si mply kn owing the language better and thus knowing more vulg ar express ions . While not bei ng nat i ve Engl ish - speaker might cause them to be extremely c autio us while using vulgaris ms, or it might prompt them to use them more as filler words. Lastly , the s i ze of the audie nce might greatly influence the use of vulgarisms in the cha t. Simp ly put, th e more viewers there are in the stream, the more poss ible it is t hat at l east some of the m will use this type of language. T his means that a streamer with 10 vi ewers c ould be th e most vulgar on the plat form without a single vul gar messag e in his stream cha t, w hil e a str eamer with 10 , 000 vi ewers can ut ter a single vulgar ism and hundreds of viewers might respo nd in kind. Taking all of these factor s into consideration, an a ly z ing only o ne stream er would not provide a p roper overview of the usa ge of vul ga risms o n Twitch .tv. T his is why it is necessary to anal y z e multiple strea mer s , w it h which these facto rs vary. 3.1.3 OVERALL SPECIFICS OF TWITCH.TV Although every channel on Twit c h is unique , some rules apply to all of the channels on the pl atform . These rules are referred t o as the Tw itch Terms of Service (TOS for short ). Specifica lly, the p art o f the T

23 witch TOS that this work is concerne d
witch TOS that this work is concerne d 19 most about is the “ Ha teful Conduct and Har assmen t ” part of the “ Commun ity Gui delines ” tab. In this tab , Tw itch specifie s h ow they handle preventing and prote cting the users of their s ite from hat eful behav io r and harassment . Ha teful conduct is conside red to be any cont ent or activity that promotes , en c our ages , or facilitate s discrimination, denigration, objectification, h aras sment , or viol ence based on the following characteristics : ra ce, ethni cit y, nationa l o rigin, religion, se x, gen der , ge nder identit y , sexual ori entation, age, disability or seri ous m e dical condition (Twit ch.tv, 2020) . Harassment is any content that a ttem pts to intimidate , bully, ab use, degrade , or create a host ile en vironment fo r others. Thi s include s r epeated name - calling, stalking, t elling someone to hurt themselves , et c. 1 Th ese rule s apply to every cha nnel on Twitch with no exceptions. B oth indivi dual a n d overall spec ifics of Tw itch push streamers towards enforcin g the rules that the s ite itself requires and that the streamers themselves prefer. T hat is why streamers can set up a popup messag e , that warns the us er s before typing anything in the chat so th at they do not accidenta lly bre ak a ny ru les. The popups look something li ke this: (source: ht tps://www.twitch.tv/gmhikaru ) (s ource: https://www.twitch.tv/cohhcarnage ) 1 The p olicies mentioned in the “O VERALL SPECIFIC S OF TWITCH ” are only in e ffect until 22 nd o f Ja nuary 20 21. From this date on, a n ew pol icy will be enforced on the sit e . 20 Whenever any rules are in place, there is a risk of the rul es being broken . This is w hy a vast majority of strea me rs on the platfor

24 m opt to have users that watc h over
m opt to have users that watc h over the ch a t for them . These pe ople are called Chat moderato rs and they watch out for any forbidden behavio r an d punish it acco rding to the chan nel ru le s. T h ese punish ments range from a f ew seco nd s long ti meouts to permanent bans from typing any messages in the c h a t . Twitch chat mod erators are present since it would be extremely difficult f or the streamer to create content and wat ch over an d punis h any in appr opriate cha t behav io r at the sa me time . A great am ount of streamers al so chooses to use chat bots in their str eam chat . These bots automatically punish users for using certain expressions (such as rac ial slurs) . The reason for usi ng these bots is the si mple f act that as the viewershi p of the chan nel grows, it becomes increasingly difficult for human moderators t o wat ch over the chat by themselves . A couple of human individual s moderating a chat of mo re than 10 000 viewers could prove to be very diff icult . 3.1. 4 STREAME RS In this part , the chosen streamers are systematicall y anal y z ed one by one. The order in which the y a re analy z ed goes f rom the one with the least average viewers to the one with the most. 3.1.4 .1 WOLFSGORA WR https://www.twitch.tv/w olfs gorawr Twitch username WolfsGoRaw r belongs to a ma le streamer from T he Ne therlands named Ace. Ace streams daily and is a varie ty streamer, meaning he does not stream one video g ame in particu lar , but ma n y of them , although it could be said that he usuall y focuses on vi deo games that conta in some so rt of turn - bas e d e lement. His stream usually starts arou nd 1 PM CET (GMT+1 ) and goes on for around 5 hours. 3.1.4.1.1 ENVIRONMENT A ce l abels himsel f a sarcastic

25 person an d his on - stream beh avi
person an d his on - stream beh avio r confirms this lab el. Ace is no stranger to u sing sarcasm , ir o n y , and tend s to joke often. H is stream is aime d at a mature audience and his speech mat ches this aim . Ace ’ s view er a vera ge as of 21 December 2 020 is 51 av erage viewers, whic h makes his stream the sma llest o ut of all the e x amined streams. Wit h his small but dedicated a udience , Ace has been a full - time streamer f or years . 3.1. 4.1.2 ANALYSIS The analy z ed stream took place on the 27 th of December 2020 . A ce was strea ming a game called Darkest Dungeon. Throug hout the stream , his viewer average was 12 5, which is more th a n twice December ’ s average. The ch at log of this stream contains slightly more than 1200 messages and almost 8200 word tokens. Expression # of uses Ass 1 Bloody 1 Crap 1 Cunt 2 Damn 9 Dic k 4 FFS 2 Fuc k 8 God 4 Jesus 1 Shit 6 W TF 2 Total 41 22 A lthough Ace himself is not at all op po sed to using strong language an d uses it quite frequentl y, the chat of his stream does not share this sentim e nt as much. Out of the 8200 words use d in t he chat throu ghout the s tream, on ly 41 of them contained vulgar expressions . This is the eq uiva l e nt of 0,5% of the chat being made ou t of strong language. This might be attributed simply to the fact that the viewers of the stream are not so numerous, and t hus their mes sages stand ou t more . Even thou g h strong l anguage is not a t all forbidden i n Ace ’ s st ream chat, users might still cho os e not to use these expressions , since other users are more likely to read their message and po int it ou t. In the case of th is p artic ular stream , even though the

26 streamer is not agains t using stro
streamer is not agains t using strong lang uage, cha t seems to not mi mic his behavio r much . A vast majority of vulgari s ms being said by the streamer resulted in no v ulgar reaction by the chat what so ever . This , among othe r things, mig ht be due to Ace not communicating with his v iewers directly and simply co m m e ntatin g on the event s that are takin g place in the game he is playing. This way, the viewers may not feel as involved and might not feel t he urg e to r espond in kind. Taking this i nto consideratio n, a lot of messages involving strong langu a ge in the chat w ere, f o r e x ample, used to express the i rrit at ion of the viewers over the gam e t hat was played , and simply mentioning their frustratin g experie nces with it. 3.1.4.2 FR OSTPRIME http s://www.twitch.tv/fr ostprime_ The second analyzed streamer is F rost Prim e. F r ost is an Americ an streamer, currently re sid ing with his girlfri en d in Rich mo nd, V irginia . Frost streams daily , start ing at eithe r 4 PM CET (GMT+1) or 7 PM CET (GMT +1) and strea ming for around 6 or 7 hours. Forst ’ s content revolv es ar ound a set of f e w things. First ly, Strea ming video games such as Le ague of Legends, Slay the Spire, a nd an o ccasi o nal variety of other games . Secon dly, On a regul ar basis, us ually once a week, Frost also streams in the “ Just Chatting ” section , at which po i nt his content mainly focuses on d irec t ly communic ating with his chat. 23 3.1.4.2.1 ENVIRON MENT Frost Prime h as a cheery and easy - going personality. Frost started as a content cre ato r on YouTube bu t later shifted his primary focus to creating stre a m content on Twitch. His na tural charm and t he joy he ta kes from streaming make his

27 streams a n exceptional source of e
streams a n exceptional source of ent er tainment. As of December 2020, Frost ’ s viewer average was 310 view er s. His out going nature helped him build a big and supportive fanbase, which allowed him to start streami ng full time. Frost e njoys communicating with his chat throughout hi s st reams and do es so ver y often. Frequently throughout his bro adcasts , Frost asks h is viewers ques tions, responds to their messages , or sometimes even lets the viewers decid e on what he shou l d do nex t or what decisi on he should ma ke in the game he is current ly playing. 3.1.4.2.1 A NALYSIS The analy z ed stream took place on th e 29 th of D e cember of 202 0. The stream starte d with a roughly 30 - minute long intro where Frost was ta lking to h is aud ience and waiting f or them to join the stream, followed b y 6 hours of a game called Slay the Spire. Throughout the stre am, Frost ’ s viewer average w as 429 viewers, w hich is more than a hundred above his average viewer count for the month. The h igher invol vement of the chat in this stream shows in the collec ted data since the c hat log of this stream contained more than 6500 messages and more than 34000 wo rd t okens. Ex pression # of uses Ass 9 Bitch 8 Bullshit 1 Cock 1 Crap 2 Cum 5 Damn 21 Dick 5 FFS 1 Fuck 91 God 19 Jesus 5 24 LMAO 26 Motherfucker 1 Pussy 3 Shit 34 Tit 4 WTF 9 Total 24 5 Compared to WolfsGoRawr , th ere is a clear incre a se in the numb er of vulgar e x press ions used . Out of the 34000 word tokens, 2 45 were uses of strong l ang ua ge, which i s the equivale n t of roughly 0, 71 % of all the words in the cha t being vulgar. F rost has an open personality and so he often uses swear w ord s to expr

28 ess hi s emotions when he i s , for e
ess hi s emotions when he i s , for example, not doing well in the game . The environment he creates makes i t so that the v iew er s feel the same way , and do not mind typi n g out a vulgar expression to show their o pinion about the particular m atter. Fros t often does n ot h esitate to descr ibe things he is excited about in a vulgar way. For example, h e might ask his viewers: “ How fucking great is this chat ? ” which ma kes the vi e w er s not fear any rep er cussions and re spond in kind with the same vulgar expr essio n. Know ing thi s, his long - term vie w ers know how to behave in his chat and which expressions to use and which to st ay aw ay from. 25 3.1.4. 3 TOB I AS FATE https://www.twitch.tv/tobiasf at e T he t hird ana lyzed s trea mer is called Tobias Fate, alternatively Tobi t o, or simply To bias. Tobias is a C an adian streamer, currently residing in Toronto . Tobito ’ s streaming schedu le is en tirely spor adic and not at all . Tobias c a n be see n s treaming at random h ours on random da ys, sometimes not streaming for day s and other time s streaming for multiple days in a row, sometimes even multiple streams per day. The lengt h of the strea m s is also compl etely random, as Tobias someti mes can stream for m or e than 8 hours without st oppi ng, and on d ifferent occasions “ rage quits ” the stream after 2 hours o f streaming. His co ntent is mainly focused on streaming a vide o game called League of Le gends, a nd that is where most of his community comes from. He is kn ow n for his particu lar skill in the game an d exc e ptional mastery of certain charac ter s in it. 3.1.4 .3.1 ENVIRONM ENT Tobias Fate has a unique stream ing style. During his streams, Tobias tends to sh if t between active ly commentating th e

29 game that he is playing at the mome
game that he is playing at the moment, whi le sometimes h e enti rely mutes h is microphone and play s music , so he can foc us on winni ng the m a tch. Tobias mixes his in - game skill with en t e rtaining commentary and if he is not co m mentating, with fast mu s ic to m atch his energetic pla yst yle. A s of Decemb e r 2020, Tobi to ’ s viewer average was 1202 viewers. In the past years, Tobias u sed to have many mo re viewers watching his stream , b ut t he dr op in viewership has not changed h is st reaming st yle in the sli ghtest. His lov e for pirates, powe r metal , and h is stra igh tforward personalit y are what make his viewers come back to watch his s tre am. 3.1.4.3.2 AN A LYSIS The analyzed stream took place on the 28 th of December 202 0. The stream star ted at around 1 A M CET (GMT+1) a nd ended at aro un d 10 AM CET (GMT+1). Tobias pla yed League o f Legen d s for the entire duration of the strea m , and would 26 re gularly switch betw een co m mentated and muted segments. His viewer average was 1 215, w hich perfectly in line with Decemb e r ’ s average. The chat log of thi s stream consist s of more th an 8400 messages and over 62 000 word tokens. Comparing th e da t a t o the previ ous log of Frost Pri me shows one thing. While the number of mess ages i s not necessarily too different, the number of wor d tokens is almost doubled. This goes in line wit h the fa ct t hat from watching the channel, it can easily be said that Tob ito ’ s chat is v ery spammy . Long m essages wit h repeated text and numerous emot icons , pai red with even the cha t bots being programmed to sp a m along with the viewers inflates the wo rd token count by a considerable am ount. Expression # of uses * Ass 9 Basta rd 1 Bitch 6 BS 1 Bullshi

30 t 3 C rap 2 Cum 3 Damn 17
t 3 C rap 2 Cum 3 Damn 17 Dick 8 FFS 1 Fuck 58 God 16 Jesus 6 LMAO 24 LMFAO 3 Motherfucker 1 Piss 1 Shit 60 (47) Twat 1 Total 22 1 ( 208) * T he numbers WITHOUT brackets are the ab s olute values , the nu mbers WITH brac kets are the va lues with spam filt ered out. 27 *Graph made using absolute values. Looking over the graph and the table and comp aring the m t o the word toke n coun t of this st ream confirms what was stated in the previous paragraph. The s pammy nature of thi s particu lar ch at re sult s in onl y roughly 0,3 5 % of the chat being vulgari s ms if spam is counted, and only 0,33 % if it is not. A lthough Tobias him s elf is not at all tame when it com es to his la nguage. This a l so points towards the fact that althoug h use rs in the chat spam , th e spam is not vu lgar. A lot of League of Le gen ds streamers share one particular characteristic , which is being very harsh to other players, be it teammate s or enemies. Tobias is no e x ception to thi s , although his vocabulary is very partic ula r. He can v ery often be heard c alling o ther players retarded, disgusting, slimy, filthy, creeps, sick fucks, creatures , etc. The viewers in his chat however rarely engag e in such behavio r and rathe r just laugh at th e way Tobias interacts with other Le ag ue of Legen ds pla yers. Tobias seem s to have almost no filter and seems to always sp eak his mind, which he does not care to mask with sof ter words . Over all, Tobias is even more vulgar than his chat . He is not shy to call out a bad play of his teammate or hi s enemy and add a swearword t o emp hasi z e his statement. 3.1.4. 4 F OLLOWGRUBBY http s://www.t witch.tv/followgrubby M anuel S chenkhuiz en is the full name o f the owner

31 of this Twitc h channel . H e resides
of this Twitc h channel . H e resides in The Netherlands and is often nicknamed “ Grubby ” . Gru bby , who is now a days 28 a Twitch stre amer is also a great personali ty in the e - sport s scene. Sin ce 2003, wh en Grubby entered the prof es sional scene, he has won m u ltiple international e - sp or ts t ournam ent s in the video game Warcraft 3, which he now streams. Bes id es being a streamer, and an ex - p rofessional gamer , Grubby is a lso a You Tuber. Gru bby has 6 days a we ek streaming sched u le. Usually starting at around 8 PM CET ( GMT+1) , Grubby usually s treams unti l around 2:30 AM CET (GMT+1). Gubby ’ s content revolves arou nd streaming Warcraft 3 on a n almo st professional level. His ski ll in the game is v ery well known and g reatly respected even am o ng conte mporary Warc r aft 3 profes sionals. 3.1.4.4.2 E NVIRONMENT Grubby likes to run his stream in a very clean manner . Hi s content is aim ed at viewers of all ages and it can be deduced by simply watch ing hi s stream . Grubby ’ s v ie wer a vera ge as o f December 2020 is 2 404 viewers. Gr ubby is e xt r emely good at wh at h e does and he often shares his knowledge with his community. Althoug h during the matches themsel ves Gr ubby only rar ely communicat es with his v ie wers, i n bet ween the matches he is a lways ready to an swer any question s about the ga me that his viewers mi ght have. Grubby is very encour ag ing when it comes to hi s viewers wanting to improve in the game and does no t mind going in - depth and over - ex plain any issues that want to discuss . 3.1.4.4.1 ANALYSIS Th e analyzed stream took place f rom around 8 : 30 PM CET (G MT+1) on the 26 th of December 2020 to around 4 AM CET (GMT+1) on the following day. Grubby was playing

32 Warcraft 3 for the entirety of th e st
Warcraft 3 for the entirety of th e stre am . Altern atively, G rubby was watc hin g replays of professional Warcraft 3 players to analyze them. The view er average of this stream was 2939 vi ewers, which is more than 500 above Dec ember ’ s average. The chat log of t his particula r stream consist s of m ore than 6300 messages and 41000 word tokens. Comparing this to TobiasFate ’ s channel, the amount of both messages and word tokens is lower . Considering the environment of Grubby ’ s channel this is not a surprise . Grubby ' s stream chat is usually slow . S in ce Grubby does no t interact with his audience much while in the middle of a mat ch, t he window open for in t eraction is considerably 29 small. Grubby ’ s chat also contains no spam and is kept unde r strict rules to match Grubby ’ s p re ferences. Expression # of uses Ass 1 Bloody 1 Crap 1 Damn 40 God 14 Jesus 3 LMAO 10 L MFAO 1 Prick 3 Shit 7 WTF 14 Total 95 When considering the characteristic of Grubby ’ s stream, the result s of the a nalysis are not surprising . Ran gin g f rom the low total nu mber of instance s of st ro n g language uses, to the word s used, it shows a lot about how the channel is r u n . Only ro ughly 0,2 3 % of Grubby ’ s chat were vulgarisms . Expressions that stand ou t on graphs an d tables of other strea m chats are in this case very low in numb e rs , or even c omple tely abse nt. What dominates the table , in this case, is t h e f airly mild expression 30 damn . A l though abbr ev iations con taining vulgar expressions are present, they have become s o standard on the site that only v ery few users consider them v u lgar anymo re. Never theless, in the att em pt to keep his st ream as non - vulga

33 r as possible, Grubby often tends to
r as possible, Grubby often tends to u se euph emisms, such as “ gee z ” inst ead of “ Jesus ” , “ shoot ” instead of “ shit ” , and “ darn ” instead of “ damn ” . 3.1.4. 5 ELAJJAZ https://www.twitch.t v/ela jjaz Elias L ön n is the full name of a Twitch streamer calle d Elajjaz . Elajjaz is a European streamer from Sweden, who lives in a Swedis h cit y called Gävle . Elajjaz streams 6 days a week , usually starting at 4 PM CET (GM T+1) and streaming for at leas t 8 hours, usually endin g his stream shortly after midnight . Elajjaz is a variety str e amer who often te n ds to st r eam particular games for longer periods of t ime . Elajjaz is what is know n as a “ speed - run ner “ . What th a t means is that Elajjaz often chooses a game and pla ys it o ver and ov er again , com pe t i ng with other speed - runners for the shortest time, in which t h ey can f i nis h it. This means that he often switches f rom playing a new game every fe w days to grinding the best comple tion time in a single game for months without swi tching. 3.1.4.5. 1 ENV IRONMENT Elajjaz is an open - m inded per son wh o prefers to run his st ream in a free manner. H e is determined an d knows his community very th o roughly. His lo ng - time view ers tune in dai ly to enjoy his cheery persona lity and entertaini ng content. As of December 2020, his viewer average was 5861 viewers. Being a speed - runne r has its benefits since Ela j jaz was ab le to de v elop good muscle memory, allowing hi m to int eract with his chat at alm ost all times. Elajjaz likes t o keep his chat inv olved, often even d isplaying it as a part of his stream. His streams often involve segme nt s, in which he talks to t h e chat to interact with his community more. Elajjaz often calls his

34 viewer s by their Twitch userna mes
viewer s by their Twitch userna mes and can keep a stea dy conversa tion wit h his chat even in the middle of playing a game. 31 3.1.4.5. 2 ANALYSIS Th e analyzed stream took place from 4 PM CET (G M T+1) on the 22 nd of December 2020 to 12:30 AM CET (G MT+1) on the followin g day. Elajja z s tarted with the stream with a roughly 30 - minut e long int ro and proceeded to play Hitman 2 for the rem a ind er of the stream . The viewer average o f this s tream was 6 912 view ers which is more th a n a thousand above his avera ge for the month . Th is increase is not su rprising since man y vie wers that watch var iety streamers tend to come and go depending on the game the streamer decides to play. Comparing th e viewer t o the previous streamer shows more than t w ice the viewer ship, bu t the main differ ence can be seen when it comes to the chat. Th e re were more than 5 8000 messages in the chat throughout the st ream which contained mo re than 232000 word tokens. Expression # of uses* Arse 1 Ass 80 (72) Bitch 34 Bloody 3 BS 5 Bullsh it 13 Cock 48 (41) Crap 1 Cum 20 (15) Cunt 2 Damn 78 Dick 13 FFS 13 Fuck 6 78 (43 0) God 140 (82) Jesus 41 LMAO 227 LMFAO 36 Motherfucker 4 Piss 3 Pussy 7 Shit 205 (186) Tit 3 Twat 1 32 WTF 329 (306) Total 19 85 (1617) * T he numbers WITHOUT brackets are the ab s olute values , the nu mbers WITH brackets are the values with sp am fil tered out. *Graph made using absolute values. Out of the 23 2000 word tokens used , 19 85 of them were vulgar . This means that more than r oughly 0,8 5 % of the chat we re vulgarisms . Ela jjaz is a s tream er that is not afr aid to show h is emotions. To ad d to the impact of his speech, Elajjaz

35 often uses vulgaris ms to emphasi ze
often uses vulgaris ms to emphasi ze his feelings, w h ether positive or ne gative. W h ether s omething goes his way or abso l utely does not, Elajjaz lets his viewer s know. “ T hat ’ s fucking ama z ing ! ” or “ Get fucking destroyed ! “ are just some examples of the ut te rances he uses. His viewers, who enj oy Elajjaz ’ s straigh t fo rw ard personali t y often join in and use the same expr essions t hat he d oes , so metimes in a spammy manner. 3.1. 4. 6 LOLTYLER1 https://www.twitc h.tv/l oltyler1 T yle r Steinkamp is the civil name that is connected to the online ali as Tyler1, often simp ly called Ty le r or T1. Proclaiming himself “ the most toxic streamer ” , Tyl e r 1 is not far to uphold his statement. Hist orically streaming mainly Leagu e of Legends, 33 Tyle r got a total of 22 of his League of Legends accounts b anned for excessive toxicity . Tyl e r ’ s offens i ve beh avio r , ho wever, is not something that is only kept to t he in - ga me c hat but somethin g that can be very often seen live during his Twi tch stre am. Being an American streamer, his streams usually start aroun d 10 P M CET (GMT+1) and usually go into the mornin g hours, often ending his stream between 7 AM CET (GMT+1) and 9 AM CET (GMT+1), usually dependin g on his mood. Tyler1 s t ream s Monday th rough Friday , taki ng the weekend s off. 3.1.4.6.1 ENVIR ONMENT Tyler 1 has a very clear and well - known image. His enjoyment in fitness and body - building, paired with his aggressive min d set and will to ac hieve the best resul ts make u p for a very n ot i ceabl e p e rsonality. Bui lding his trademark on being “ alpha ” , Tyler1 qu ick ly rose to fame in 2016 , when he st arted gar nering his still - growing audience. As of December 2020, Ty

36 ler ’ s viewer average was 24588
ler ’ s viewer average was 24588 viewers. Being lou d, a g g ressive, rude , but also very sk illed in v ideo gam es are j ust some of his most know n characteristics. Tyler ’ s personality, mix ed with the fact that he st reams League of Legends, make s his stream a unique experi e nce. Tyler1 ’ s f a me is considerable , and there are only very few Twitch users who do not know wh o Tyler1 is. 3.1.4. 6. 2 ANALYSIS The analyzed stream took pl ace from around 10:30 PM CET (GMT+1) on the 2 2 nd of December 2020 and ended at around 10:15 AM CET (GMT+1) on th e following day, whi c h makes the s tream al mo st 12 hours long. Thr oughout the enti re stream, Ty ler played Leag ue of Le gends , onl y taking 5 - minut e breaks on a fe w occasi o ns . The viewer average of this stream was 2557 8, which is in tact with December ’ s ave r age . Tyler 1 is a notori ous persona in the League of Legends communit y and thus this am ount of vi ewers is normal for his s treams . Tyler commentat es on almost everything that is happen ing in - game , while activ ely reading and communicating with his stream chat . T aking int o consideration Tyler1 ’ s active interaction with his str eam ch at, his high viewer cou nt , and the le n gth of this particular stream, all of the se factors result in a total of over 2 09000 messages being post ed in chat, which contained over 946000 wor d tokens in total . 34 Expression # of uses* Arse 6 (1) Ass 611 (4 59) Bastard 29 Bitch 500 (353) Bloody 2 BS 34 Bullshit 82 (68) Cock 271 (210) Crap 11 Cum 55 Cunt 3 Damn 315 (299) Dick 160 (93) FFS 66 (49) Fuck 5323 (4134) God 616 (521) Jes us 94 (73) Jizz 3 LMAO 1260 (108 5) LMFAO 346 (294) Motherfucke r 34 Piss 154 (9

37 3) Prick 6 Pussy 365 (285) Sh
3) Prick 6 Pussy 365 (285) Shi t 1373 (1154) Tit 17 Twat 1 WTF 1735 (1560) Total 13472 (10926) * T he numbers WITHOUT bracket s are the ab s olute values , the nu mbers WIT H brackets are the v alues with spa m filtered out . 35 *Graph ma de u sing absolute val ues. As expected judging fr om t he number of messages and word toke ns, the number of vulgari s ms drastically rose. Out of the 9 4600 0 word tokens, 13472 were vulgari s ms . T h is is the equiva lent o f roughly 1,4 2 % of all t he chat being strong l an guage , which is a n extreme ly high percentage compared to the rest of the streamers . This is no doubt due to Tyler ’ s behavior on stream. Tyler do es not stra y away from using vulgar expressi o ns, aiming them towards his teamm ates, enemies, deve l op ers o f the game , and even h is stream audience. Whe n Tyler behaves in such way towards his view ers , the viewers usu all y respond in kind, often spamming their message s , since Tyle r ’ s chat moves very qui ck l y, and they w ant t he stream e r t o notice th e ir message. 3.1.5 OVERALL RE SU LTS F OR TWITCH.TV Tw itch.tv is a live - streaming platform , and as such, it captures i ts users ’ immedia t e reactions to wh atever happens. The reaction of the streamer can be obser ved on the stream , while the react ion of the view ers is capture b y the chat of t he stream. The analyses were made throughout one ent ire broa dca st of each of the chosen Twitch chann els. Stream c ha ts of si x T witch channels were analyzed. The names of these channels are WolfGo Rawr, Frostpr im e_, TobiasFate, FollowGrubby, E lajjaz , and loltyl er1. 36 Star ti ng with the first c hannel , the stream chat of WolfsGoRawr contained 8200 word tokens, out of which 41 were v ulga

38 r, which equates to 0,5% of the stream
r, which equates to 0,5% of the stream chat being vulgar e xpressions. The s econd c hannel, Fros tPri me _, had a tota l of 34000 word to kens poste d in his stream chat througho ut the stream, out of which 245 were vulgar isms . This equates to 0,71% of the stream chat being made out of vu lgarisms. The third cha nnel, Tobias F ate, had a total of 6 2000 word tokens poste d into th e stream c hat over t he stream ing runtime. Out of thi s total , 221 words we re vulgar, out of which 13 were spammed . Th is equates to 0,35% of the chat being vulgar isms if the sp am is counted , an d only 0,33% of the chat be ing mad e out of vulgar expressions i f spam is le ft out. The fourth channel, Follow Grub y, had 41000 word token s poste d in the chat du ring the stream, out of which 95 were vulg ar . This equates to on ly 0,23% of the chat being ma de o ut of vulgarisms, which makes the chat of FollowGrubby ’ s channel the least vulgar o ut of the analyzed c hannels . Th e f ifth of the six c h an nels, Elajjaz, had a total of 232000 word to kens poste d into the s tream chat during the broad cast. Out of th is total, 1 985 words were vulgarisms when spa m is not omitted , and 1617 vulg ari s ms w hen i t is . This equates to 0,85% of the stream chat being vulgarisms if spam is counted , and only 0,7 % if it is not. Lastly, the biggest of the six channels, loltyler1, had a to tal of 946000 word tokens poste d into th e chat of the channel during th e stream . Out of t his total, 13472 wo rds were vulga risms when spam is counted , and 10926 vulgar expressions when spam is not counted. This equates to 1,42% of the chat consisting of vulgar ex pressions when spam is involved, a nd 1,15% of the chat be ing vulg arisms when i t is not. This mak es th e chat of this c hannel the mo

39 st vulgar o ut of the analyzed chann
st vulgar o ut of the analyzed channel by a considerable margin. Putting all the r esult s together, the average chat of an analy zed Twitch channel c onsist s roughl y 0,68% pure ly out of vulgarism s if spam is count e d , and roughly 0, 6 % purely out of vulgarism i f spam i s omitted. 37 3.2 REDDIT.CO M ( Source: ht tps://www.slashgear. com/re dd it - is - experie ncing - an other - outage - heres - w hat - we - know - 18592169/ ) Boastin g t he ti tle “ front page of the internet ” , the popularity of R eddit is considerable ( R eddit, 202 0 a ) . W ith over 4 30 million average active users per mon th , the title is not far from reality . A t the time of writing, Reddit is the s eventh most p opu lar site in the US , ( Alexa , 2020 a ) a nd as hig h as the eighteenth most pop ular si t e in th e worl d. ( Alexa , 2020 b ) In simple terms, Reddit is a massive forum made up of s maller forums ( Boyd, 2 01 8 ) . Put in a m or e compl ex way, Reddit is a commun ity - det e rmin ed aggrega tor of conten t. I t is a site that allows re gistered member s to submit, rate , and disc uss the co nt e nt of their choosing. Reddi t is ma de out of many smaller comm unities, that are also know n as “ sub reddi t s ” . Eac h of these communities has i ts own p age , s ubject matter, user s, and moderators ( Reddithelp, 2020 a ) . Th e user s of these co mmunities can post any th ing, r anging from stories to l inks and media . After that , it is up to other use rs to vote and comment on these posts . Through voting, the use rs determ in e w hi c h posts make it to the top of their individual community page s , or even the front page of the entire web site . This way the site is in constant motion and i s al ways full of new

40 content for i ts users to dis
content for i ts users to discove r. This constant moti on als o pro mp t s users to com e back on a regular basis, to check out what is new and trending. For some people, going to R eddit is the equivalent of reading a d ail y newspaper ( CGP Grey , 2 013 ) . As menti oned before, Redd it is a large co mmunity made out of many smaller communities. Th ese sub - commu nities are known as subreddits. Each of these subreddits is dedicated to a dif ferent topic and is a ll moderated by Reddit user s (a l so know n as 38 “ R e dd itors ” ) . Every sub reddit has it s own topic . ( R e ddith elp, 2020b ) The site is so va st and popular that there i s a subreddit for almost everyt hing . These range from general news, to hobbies , legal advice, science, video games, mu sic , liter ature, and many mo re ( Re ddit, 2020b ) . A t the tim e o f wri ting, there are over 2 .5 million subreddits, wit h about 1500 to 2000 new subreddits being created e very single day ( FrontPageMetrics, 2020 ) . Redditor s can interact with content on the si te in several ways. A ll th e con tent on R ed d it is poste d by i ts u sers . Once a use r makes a post, other users have several way s to in teract w it h it. User s can comment on their own , or other user s ’ post s to cont i n ue the conversation about the topic or add their opinion o r someth ing new to it. Ano the r k ey feature of Reddit is th at users c an cast positive or negative votes (called “ upvo tes ” and “ dow nvot es ” ) , for each post and comment on the site ( Wid m an, 2020 ) . The more the post is upvoted , the more visible it becomes on the site , and thus mo re p eop le are like ly to interact with the post in some way . By creating a n exceptional post or by bei

41 ng a good part of the community , the
ng a good part of the community , the users earn what is called “ kar ma ” , which reflects their st atus within the communit y and their contribut ion s to Reddit ( Widman , 2020 ) . This way , t h e use rs are encourage d to con trib ute to the ir communities in the best wa y possible , to po st good content , and provid e relevant feedback to t he content of other users. ( A nderson , 2015 ) The s heer pop ularity and amount of pe ople that vi sit Reddit on the daily ba sis , ma kes the s ite a suitable choice for this work . New users come to Reddit every day and a re eager to interact with ot her l ike - mi nded users . Each community deals with its users differentl y and the bound aries a re se t diff erently by the mod erators on ever y subreddit. The langua ge used and how it is dealt w ith can differ significan tly f rom one subreddit to another and thus t he s ite w ill certainly provide an immense amount of data for anal ysis. 3.2.1 METHOD OF RESEAR CH Red dit and Twitch are very different websit es , and t hus the metho d of research cannot stay the same for both of t hem. Reddit is a forum, which me ans there is no such thi ng as live chat . It is d ivided into numerous subreddits , where pe ople post content dependi ng on the topic of the subreddit . The fron t page of a subreddit loo ks something like this: 39 (source: https://www.reddit.com/r/news/ ) At the top left, there is the name of the s ubreddit, and beneath it the posts that the users of th is subreddit have p osted. On the right , there is a s um ma r ized “ About “ sec tion , th at also co nt a in s the information about the total amount of members of the s ub reddit, and the numb er of them that are currently online. Focusing on the list of pos

42 ts in the mi ddle of the picture , tha
ts in the mi ddle of the picture , that is where the data for this study is collec ted f rom. Use rs create post s , an d other use rs co me in to discuss them . Depending on the size and popularity of the subreddit, some posts can have up to h undreds or even thousands of comments. Popular posts o n several di fferent subreddi ts un derwent the anal ysis . Three p osts per subre ddi t have bee n a nalyzed . To get the best picture of the current language situat ion on the chosen subred di ts, “ Popular “ posts have under go ne the analysis. “ Popular “ posts are the p osts, th at are displa yed when the fron t page of a subred dit is sorted by the “ TOP “ option and that fall under the “ Today “ category. To get the most recen t data, the anal yzed posts had to be from 12 hours to 24 hours old at the time of data collection. Th e data was co llec ted by manually copying the post with all of its comments in to a text file . The text file was afterward imported into th e corpus analysis software #LancsBox, in which it was analyzed. Once the entire post with all its comments is imported into #La ncsB ox, the text is analy z ed by using the “ K WIC ” f u n ct ion. This f unc tion allows the user to search for s pecific words and/or word groups. Using an integrated function of the soft ware, typing “ SWEARWORDS ” into the se arch bar high li ght s certain swear words that w ere used in the text . This is a us efu l tool, b ut i t d oes not hig hlight all the words that we are loo king 40 for , thus a m anual search for certain expressions nee ded to be made u sing the “ KWIC ” funct ion. From this point onward, the method of resear ch followed the same p attern used for Twitch . tv , which can be found

43 in chapter 3.1.1 . 3.2.2 SPECIFIC
in chapter 3.1.1 . 3.2.2 SPECIFIC S OF REDDIT.COM Even though Reddit is a community - driven web site, the us ers of the website have t o follow a set of rules . Rules that apply for e very sub r eddit are Reddit ’ s Conten t Policy rules. These ru les appl y eve rywhere within the boundar ies of Reddit without any exceptions. Include d in the Content Policy is the prohib it ion of h arassment, b ullying , or threatening violence against other use rs. Content of any form, be it username s, posts , comments , or e ven privat e m essages that are harassing or abus ive in any way are not allowed and may result in a ban ( Re ddit, 2020c ) . Furthermore, people or c ommun itie s that incite violence or promote hate based on vulner ab ility or identity are s t rictly forbidden. This means that an y attac ks aimed at race , co l or, religion, national origin, ethnicity, gender, gen der i dentity, sexual orientation, disability , or ethnicity are not allowed. Examples of these hateful activiti es would be subreddit commun i ti es dedicated to mocki ng people wit h disabilities, a derogat or y post aimed at a racial minority, or a comment justifying rape ( Reddithelp , 2020c ). These are the most relevant platform - wide rules for this work. Fu rt her more, all us ers of R eddit must be 13 years of age or older (R eddit, 2020d) . If a subreddit features content t hat is targete d at a strictly adult audie nce, you must f i rst confirm your age before gaining a c cess to the subreddit. Additionally, each subreddit has its own set of rules, that the u se rs are to a bide by . Th ese usually i nclude prohibiting R edditors from posting con tent that does not belong on the subreddit, st ealing content, spamming, using deceptive t itles on post s, sel f - promoti ng, reposting, etc. All s

44 ubreddi t s a lso include rul es tha t d
ubreddi t s a lso include rul es tha t directly prohibit R edd itors from po sting any content that would contain racism, sexism, h omophobia , or any other forms of toxic content. If any of these rule s were to be broken, the moderator team of the subreddit takes care of t h e p roblem . Depending on the sever ity , the mod era tors might de cide to simply delete the content off of the subred dit, or e ven ban the user from ever accessing the subreddit again. 41 Comparing R eddit to Twitch reveals some similarities, as well as some majo r diffe ren ces. The m ajor similarity of the two si tes lies in t he set of overa rching rules of the site, as well as the spec ific set of ru les for each subsection. The major differences lay in the exclusi v it y of the content, and its form. Reddit is made for long - te rm discussion , st r uctured thoughts , and co mments. Insig ht and helpfulness a re some of the qualities that Reddit v alues the most. Redditors choose to enter di scussions o n the post s that they cho ose mainly because they are interested in the topic , wa nt to share their knowled ge of the subje c t with o thers , or sim ply because they find the post s enter ta ining an d want to share the entertainment with other R edditors . The number of comments and the size of t he discussion is direc t ly linked to t he number of members t hat each subred dit has. The more member s there are o n the subreddit , the more member s are likely to notice the post an d interact with it. Once a post gets enough traction, it might even appear on the front page of the ent ire Reddit , which usually brings i n e ven more user interaction . Content o n R eddit is n ot time - exclusive. Once a post is mad e, as long as it does not v iolate an y rules, it can stay p

45 osted on the site forever. Wh at is excl
osted on the site forever. Wh at is exclusive , ho wever, is the traction that the post gets. The disc ussion in the commen t s ec tion below the post is u sually most a ctive once it reaches peak traction, and falls o f f in time once it is no longer relevant. The comment section of the post is archived, however , the users participating in the discussi on might no t be present anymore . 3 .2 . 3 SUBREDDITS The foll owing section contains the analys e s of the chosen subreddits. Three popular posts from each subreddit were analyzed and put into joint tables and graphs to better sho w the overall usage of vulgarisms on ind i vidual subreddits. A ll of the data was collected throughout th e 1 st of January 2021. 3.2.3.1 R/GAMING https://www.reddit.com/r/gaming R/gaming is a subreddit that is ded icated to discussing everything related to games, be it video games, card games, or board games, wi th t he ex clusion of sports. 42 Reddi tors v isit th e subreddit daily to learn abo ut gaming news, new releases, discuss their experiences w ith certain games, show off their achievement s , etc. Dis cussions a bout game flaws, rating games , and much m ore are daily occur ren ce s . With its 29 million me mbers , r/gam i ng experiences no small amou nt of daily tra ffic. Discussing something that a Re d ditor is invested in can often lead to an em otional reaction. Debating over a game that you have pla yed, sha ring your experiences w ith oth er players of the sam e g ame , and enga ging in discourse with them over something you disagree on mig ht escalate the conversation exceptionally quickly. R/gaming has n o extra rules regarding profanity and vulgarisms besides the gener al Content P olicy , and th us vulgarisms on the sub reddit are pr esent. 3.2.3.1.1 ANALYSIS

46 All three of the an al yzed po sts were
All three of the an al yzed po sts were post ed on the subreddit for more than 20 hours and am assed a total of 3 136 comments. The first post was discussing a home - made arca de machine that one of th e Redditors made. A t the time of the a nalysis , it has been posted for 21 hours and amass ed 880 comments which con tained over 20000 wor d tokens . Th e second post was a „ no stalgia thread “ , discussing t wo old gaming console s, Play Sta t ion 1 and PlayStation 2. At the time of the a nalysis , it h as been posted for 22 hours and had 1151 comments in the comment sect ion which contained over 29000 word tokens , making this post t he largest out of the analyzed tree. Lastly, the third post was discussing a newly relea sed game called C y berpu n k 2077, which came out o n the 10 th of December 2020. At the time of analysis, the post has been posted for 20 hours and amassed 1105 comments w hich contained almost 25000 word tokens. Exp ression # of uses Arse 1 Ass 33 Bastard 2 Bitch 2 BS 4 Bulls hi t 7 Cock 2 Crap 9 Damn 37 Dick 4 43 Fuck 136 God 20 Jesus 7 LMAO 18 LMFAO 1 Motherfucker 1 Piss 3 Shit 101 WTF 4 Total 39 2 The total number of word tokens taken f ro m r/g aming was more than 74 000, and out of the menti oned number 39 2 wer e vul gar . This mea ns that roughly 0 ,5 3 % o f all words used were vulgarisms. The post that included the least amount of vulgari sms was the first post , and most of these were used posit ively , in phrases such as This is fucking amazing! The most v ulgar post was the last post , i n which Redditor s were expressing their negative emoti o ns towards the game and its creators . Some of the comm ents also praisin g the game, but these were not nearly a s numer

47 ous. 44 3.2.3.2 R/N
ous. 44 3.2.3.2 R/NEWS https://www.reddit.com/r/ news R/news is a subred dit dedica t ed to real news articles. These primarily include news relating to the U nited States and the rest of the world. News u sually s pa r k s disc ussion, primarily with the Redditors , that th is new s affect s . Taking into consider ati on the fac t that r/new s has over 2 2,5 milli o n members, the number of comments on big news articles tend s to gr ow into considerable amounts . Due to the size and focus of the subreddit, r/news implements some extra rules directe d tow ards strong lan guage . In additi on to the Co mmunit y Polic y o f Reddit, the moderators or r/new s warn other Redditors about posting overly crude comments that add nothing to the conversat ion, as well as po sting comments that are unnece s sarily rude or purpo sef ully prov ocative. Comment s that inclu de suc h cont ent are su b ject t o removal. Even with th ese rules in p lace, strong language is not forbidden on the subreddit, a nd it can be seen throughout it . 3.2.3.2.1 ANALY SIS All three of the analyzed posts contain ed new s regarding the U S an d am a ssed a total of 384 5 comments . The first post was by far the largest in terms of the number of comments, as there was a total of 2493 comments at t he time of the analysis , and it has be en posted for 19 hours. The pos t in quest ion contained a news arti c le ab out a new law in V irginia, w hich is capping insulin prices at $ 50 per mont h. The second post was a news article about thousands of cannabis cases being expunged d ue to legalization in Illinois. At the time of the analysis, th e post has been post ed 16 hours ago and generated 772 comme nts. The third post was a news article concerning a Wisconsin hospital worker being arrested f or spoiled vaccine doses. At

48 t he time of the analysis , the post h
t he time of the analysis , the post has been posted for 19 hours and garner ed a total of 580 comment s Expr ession # of uses Ass 34 B astard 1 Bitch 6 Bloody 2 BS 8 45 Bullshit 29 Crap 9 Cunt 5 Damn 30 Dick 4 FFS 1 Fuck 215 God 50 Jesus 13 LMAO 1 2 LMFAO 1 Motherfucker 2 Piss 12 P rick 2 Pussy 1 Shit 96 Whore 1 WTF 8 Total 542 I n total , the th ree posts contained over 131000 word tokens , and 542 of the word tokens w ere instances of vulgar language . This means that on ly roughly 0 ,4 1 % of the words used in these posts were vulgarisms. Due to the sheer size of the first post, it comes a s no surprise that mor e th a n hal f of all the cases were present in that post alone. Redditors not only from the US but from all over the world joined the c onversation and 46 discussed the pr ices o f insulin at the place of their resi dence, and discuss ed the s teep price of hea lth ca re in the US . 3.2.3.3 R/ MEMES https://www.reddit.com/r/ memes R/memes is a subreddit dedicated to sharing memes. “ Meme s “ in this case refe r to images, that are meant to be for the sole purpose of entertain ment and may or may prov i de a pie ce of social com mentary wi th in them. If th ey do, Redditors i n the comment section often start a conversation about the piece of social commentary that the meme features. With 14 ,3 million members, r/memes is visited by many Redditors every day , who primarily seek entertai n ment. Ap art from the Community Policy, r/mem es asks its users not to “ troll “ other use rs within the subreddit, prompts its users to keep the comment sections civil an d resp ectful to other Redditor s. The ru les of the s ubreddit also forbid Reddit ors fro

49 m p ost ing me m es ab out death, mass
m p ost ing me m es ab out death, mass shootings, tragedies, rape , war, etc. Overall all o f the rules mentioned in the Community Policy also extend to the memes that are allow ed to be posted on the subredd it. Apart from the mentioned rules , no rule forbids the us ers of r/ m emes from us ing vulgar lang uage . 3.2.3.3.1 ANALYSIS The three post s chosen f ro m r/meme s contained 2031 comments in total. The first one was made purely for entertainm en t, as it poked fun a t Europ e officially entering the ye ar 202 1 before the US . At the t ime of the analy sis , it has been posted for 20 hours and contained 434 comments. The second post has garnered the most attention out of the analyzed three, as it was making fu n of certain jobs and how much money people ear n doing these jobs. At t he time of analysis , the post has been posted for 13 hours and Redditors posted 1102 comment s to it. The t hird post was po inti n g out th at the year 2020 has been very difficult for a lot of people, and felt c onsiderably longer than 3 6 6 days. A t the time of the analy s is, the post has been posted for 20 hours and c ontained 495 comments. Expression # of uses 47 Ass 16 Bastard 3 Bitch 1 BS 1 Bullshit 6 Cock 2 Crap 6 Damn 15 Dick 2 F uck 81 God 14 Jesu s 7 Jizz 1 LMAO 23 LMFAO 2 Piss 1 Shit 90 Whor e 1 WTF 3 Total 275 The three analyzed posts contained over 50 000 word token s in total, out of which 275 were vulgarisms. This means that 0,5 5 % of all the w ords were vulgar expressions . Considering t h e siz e of the posts, it is not su rprising that m ost of the v u lgari sms w ere part of the second post. The second post contained the big gest amount of social commentary and sparked a massive discu

50 ssion about jobs. It evoked a way
ssion about jobs. It evoked a way 48 more emotional re sponse in R eddito rs, and thus th e Re dditors were more prone to use str ong lang uage . 3 .2.3.4 R/ TODAYILEARNED https://www.reddit.com/r/ todayile arned R/todayilear ned or r/TIL is a n informational subreddit. The subreddit is dedicate d to sharing small pieces of s pecifi c info rmation that other users of the subre dd it m ay find in terestin g. With 24,5 milli on members, r/ TIL get s a con siderable amount of daily traffic. Finding out new information often leads to Red di tors sharing their thought s in the comment sections of the p ost in whi ch they found i t. Th e Community Polic y of Reddi t rules is the o nly set of rule s that is dedicated to restricting Redditors from using vulgar language on this subreddit. 3.2.3.4.1 ANALYSIS Each of the three analyzed post s c ont ained inf orma t io n from diffe ren t field s , and they amasse d 1474 comments in to tal. The fi rst post was about Stephen Hawking, and it mentioned the fact that due to this condition, he should have passed away in the early in the mid dle 19 60 s . At the time of the analysis , t h e post has been posted f or 17 hours and containe d 733 comm ents. The second post featured a giant tortoise speci es that was believe d to be extinct , only for it to be d iscovered again. At the time of the analysis , the post has been posted for 17 hour s and 3 49 comments. The third po st was discussing the ra p artist c alle d MF Doom, who has been confirmed dead o n December 31 st , 2020. At the time of the an alysis , the post has been posted for 20 hours and the comment section contained 392 comments . Expression # of uses Arse 1 Ass 6 B itch 4 Bloody 1 49 BS 1 Bullshit 1 Cu nt 1 Damn 27 Di

51 ck 4 FFS 1 Fuck 77 God 16
ck 4 FFS 1 Fuck 77 God 16 Jesus 7 LMAO 5 LMFAO 1 Motherfucker 1 Piss 1 Pussy 1 Shit 30 Whore 4 WTF 5 Total 195 The three analyzed posts combined contained ov er 37000 word tokens . O ut of th is total , 195 wo rd tokens count as vulgar. Thi s means t hat roughly 0,5 2 % of all the words are strong language. The second post containe d the least amou n t of vulgarisms by a considerable amount, while the first and the third post s had a l most th e sam e number of vulgaris ms, e ven thoug h the fir st post ha d almost twice as m any comments. 50 This might be because the death of MF Doom is a recent event , and th us the emoti o n al reaction to it might be way strong er, tha n to Stephen Haw k in g. Second ly, MF Doom ’ s mu sic contained vulgar lyr ics, so th e Redditors d iscussing him might be more used to using the same type of lan guage , and might not see a problem with other users doing the same. 3.2.3.5 R/FUNNY https://www.reddit.com/r/funny R /f u nny i s a subredd it purel y made for entertainme nt. R edditor s can post vi deos, pictures, gi fs, or any type of media, as long as they make an att emp t at humor. With its 34.9 million members, r/funny is the second biggest subreddit of the en tire website . Th is means th at the tr affic t hat the subreddit has is large, al though many of the users do not interact with the content in any other way than simply viewing it. Even though r/funny has a long list of rules regarding what can or cannot be posted on the subr eddit, no rule enhan ces the Comunity Polic of R ed dit when it comes to using vulgar lan guage within the subreddit. 3.2.3.5.1 ANALYSIS All three of the analyzed posts were commenting on the end ing of the year 2020. In total, the three analyzed posts

52 a c cumulated 2234 comments. Th e f
a c cumulated 2234 comments. Th e f irst of the three p osts shared the poster ’ s l ast major e xperi e nce of 2020, which was fallin g through the c eilin g of their own house. At the time of the analysis , the post has been posted fo r 19 hours and co ntained 771 comments . The second pos t was the b igges t of the three . I n this post, the Redditor cl aims, that Google Maps on their p hone automatically shows where they traveled d uring the year, at the end o f each year. The poster follows the claim with a picture, which shows th at they have only been in t heir ap art me n t through out the year 2020, due to the global pan demic. At the time of the ana lysis , the post has been posted for 20 hours and amassed 953 comments. The third post contains an image of a map. In this post, the Reddit or shared their f r iend' s la st rid e of 2020 on a m ap software . The map software c apture d the track of the ride and the final picture that the track made seemed like the track is “ flipping the middle fin ger “ directed towards the entire year of 2020. At 51 the t ime of the analysis, the p o st has been pos ted for 16 hours an d garnered 510 comments. Expression # of uses Ass 16 Bastard 2 Bitch 1 Bloody 1 BS 2 Bullshit 5 Crap 4 Cunt 2 Damn 16 Dick 4 Fuck 33 God 9 Jesus 3 LMAO 8 LMFAO 1 Motherfucker 1 Piss 6 Prick 1 Pussy 1 Shit 57 WTF 2 Total 175 52 A ll three posts combined co ntained alm ost 47000 wor d tokens, out of which 175 were in stan ces of vulgarisms . This means that only ro ughly 0, 37 % of all the word tokens were vulgarisms. This statisti c is quite s ur prising , espe cially bec ause t here a re almost no restrictive rules re garded tow a rds vulga

53 risms on r/funny , and also considering
risms on r/funny , and also considering the aim of the posts . The first two posts contained almost the same amount of vulgar i sms , while the last one c ontai ned the least. Jud g ing by t he fact that all of th e posts revo lve d aroun d one gen eral topic, th e differences bet ween them are dict ated so lely by the number of comments. 3.2.4 OVERALL RESU LTS FOR REDDIT.COM Reddit.com is a massive forum , made out o f small er forums , called subredd it s. Rather than capturi ng the imm ed i at e reactions of its use rs, Reddit captures calm a nd structured posts an d comments. The analyses were conducted on three popular posts from each of the five subreddit, in an att e mpt to cap ture a sample of the current lang uage t rends. Posts of fi v e subred dits in total were analyzed. T hese subreddits were r/gaming, r/ news , r /memes, r/t odayilearned , and r/funny. Starting with r/gaming, the analyzed posts of this subreddit contained a total of 7400 0 word tokens, out of which 392 were instances o f vulgarisms. This equates to roughly 0,5 3% of all the word tok en s in the post and its comments being vu lgarisms. 53 The s econd subreddit, r/ news has the biggest word token sample size which is 131000 word tok ens . Out of these word toke ns, 542 were instances of vulgar e xpressions. This equates to ro ughly 0,42% of all the word tokens being vulgarisms. T he t hi rd su breddi t of the five, r/memes h olds a 50000 word token sample size, out of which 275 word tokens wer e vulgar . This means that r oughly 0,55% of the wo rd token sam ple size from r/memes are vulgarisms. Th e fourth subreddit, r/t odayilearned had the smallest wor d toke n sample si ze of the five, 37000 word tokens . Out of th is total, 195 were vulgar expressions . This means that roughly 0 ,52% of all

54 the word t okens that w ere take n
the word t okens that w ere take n f ro m this subreddit we re vulgari sms. Lastly, r/funny had a sample size of 47000 word token s , 175 of wh ich were v ulgarism s. This equates to only r oug h ly 0, 37% of all the word tokens be ing vulgar, which m akes the last of the five subr eddits the l eas t vul gar. Taking all of the above - ment i oned percentages and making an average for the entire websit e re sult s i n a value of roughly 0,48 %. This means that ro ug hly 0,48% of all word tokens tak e n f ro m Reddit.com were vulg arisms 3.3 COMPARISON OF TWITCH .T V AND REDDIT.COM Word Tokens Vulgarisms * WolfsGoRawr 8191 41 FrostPrime_ 34388 245 TobiasFate 62254 221 FollowGrubby 41061 95 Elajjaz 232277 1985 (1617) loltyler1 946606 13472 (10915) r /gaming 74005 392 r/news 131567 542 r/memes 502 26 275 r/TI L 37456 195 r/funny 46935 175 Total 1664966 17638 (14713) * T he numbers WITHOUT brackets are the ab s olute values , the nu mbers WITH brackets are the values with spam fil tered out. 54 Chanel/Subre ddit % of Vulgarisms WolfsGoRawr 0,5 FrostPrime_ 0,71 Tobi asFate 0,33 FollowGrubby 0,23 Elajjaz 0,7 loltyler1 1,15 r/gaming 0,53 r/news 0,42 r/memes 0,55 r/TIL 0,52 r/funny 0,37 Rough average 0,54 In total, 1323200 word tokens were taken f r om the stream chat s of Twit ch.tv, and 339000 wor d tokens were taken f ro m post s and comments of Reddit.com. A total of 16059 vulgar isms were uttered in twitch ch at when spam is co un ted, and 13132 if it is not . Considering that there was no spam present on R e ddit.com, the re i s o nly on e number for the instance s of all v ulgarisms, which is 1579 of total vulgar expression s. The to tal values differ significantly,

55 whic h is why the total numbers are c
whic h is why the total numbers are converted into p ercentages, which are easi er to compare. The most vulgar stream cha t on Tw itch.tv b elonged t o a channel named loltyler 1 . Th e perc entage of vulgarisms in this stream chat was 1,15%, while to most vulgar subredd it was r/memes with a value of 0,55% of all word tokens being vulgarisms . The least vulga r strea m chat of a Twitch .tv stream belong s to the channel Follow Gru bby, which only had 0,2 3% of all word tokens being vulgari sms, while the least vulgar part of Reddi t .com was a s ubred dit calle d r/funny, which only con t ained 0,37% of all word tokens in the form of vulgarisms. Wh en spam is not considered , the overall perc entage of word tokens taken f rom Twitch .tv contained 0, 6 % of vulgarisms, while using the same equa t ion for R eddit.c om results in a value of 0,48%. Taking the final data , it can be co mpa red to th e research made by Jay ( Jay, 2 009), which ment ions that spo ke n language contains from 0,5% to 0,7% of taboo words . The value for both of the websites show s that on average 0, 54 % of the w ord tokens from each corpus are vulgarisms. 55 4. CONCLUSI ON The internet is a massive medium, that is used by countless amounts o f people. The behavior of people on the internet may differ significantly from their behavior outside of it. The differences in behavior might also include their use of specific language expressions, due to the anonymity that the internet often provides. The main aim of this thesis was to discover, whether vulgar language is used more on the internet, than in everyday life. No two sites are the same and subsections of these sites can differ from one another considerably. That is the reason for two sites w ere chosen to be covered in this thesis, and why multiple sub

56 sections of the two sites were analyzed
sections of the two sites were analyzed. Although the aim of the sites and the content that they provide are significantly different, the main differences between the sites were not too signif icant considering the entire sites, but rather in their subsections. The results from each of the subsections came out different, especially in the case of the first website. Considering the overall comparison, however, showed the frequency of using vulgar isms on both of the sites was not significantly different. The results of the research were quite surprising. Even though there might be less of a repercussion while using vulgarisms on the internet, the results show that people on the internet use vulga risms just as often as they would use them normally in spoken language, with one of the websites using them slightly more often than the other. Since the internet is vast, it is hard to form a complete picture of the entire medium, even more so since it i s ever - changing and fast expanding. Analyzing the behavior of people on the internet is no easy task, especially since the collected data may become outdated considerably fast. Even though the research might prove to be difficult, knowing how people behave and speak on the internet might prove to be very valuable information. 56 5. SO URC ES AKMAJIAN, Adr ian, Ric hard A. DEMERS, Ann K. F ARMER and Ro bert M. HARNISH, 2010. Lin guistics: A n i ntro duction to langu age and communicatio n . 6th ed. London, England : M IT Press. ISBN 9780262013758 . BATTISTEL LA, Edwin L., 2007. Bad l anguage: Are some words better t han others ? New York, NY: Oxf ord Univers ity Press. ISBN 978 01 95 337457 . JAY, Timothy, 2009 . Th e uti lity and ub iquity of taboo word s . Perspectives on psy chologi ca l science: a jo urnal of the Association for Psycho logical Scienc e . 4 (2), 153 â€

57 “ 161. ISSN 1745 - 6916 . STEPHENS
“ 161. ISSN 1745 - 6916 . STEPHENS, Ric ha rd; ATKINS, John; K INGSTON, Andrew. Swearin g as a respo ns e to pain. Neu r ore por t , 2009, 20.1 2 : 1056 - 1060 . Internet sources: University of F lo rida International Center . 20 Cultural Ta boos [ Online ] . [ c it. 20 2 0 - 1 2 - 1 1 ] . http://m ail.rsg c.on.ca /~mb ader/FOV2 - 0001D7 B4 /20%20Cultu ral%20Taboos.pdf � Alexa. Top Sites i n Unite d States [ Online ] . last upd ated 4 th of December 2020, [ cit. 2020 a - 12 - 04 ] . https://www.alexa.com/topsites /countries/US � A lexa. Top 500 Si tes on the Web [ Online ] . last updated 4 th of Dece mb er 2020, [ cit. 2020 b - 12 - 04 ] . https://www.alexa.co m/topsites � ANDERSON , K.E. (2015) . Library Hi Tech News . Ask me any thing : what is Redd it ? [ Online ] . [ cit. 2020 - 12 - 05 ] . Vol. 32 No. 5, pp. 8 - 11. https://rucore.libraries.rutgers.edu/rutgers - lib/46445/PDF/1 � BOYD , John . What is Reddit?, [ Online ] . pub l ished Februar y 28 th 2018. [ cit. 2 020 - 12 - 04 ] . https://www.brandwatch.co m/blog/what - is - red dit - guide/ � 57 Call Hippo. Int ernet Communications: What Is It & Ways To Communicate Over The Inter n et [ Online ] . publ ished Decem ber 2 0 th 2019, last updated O ctober 19 th , 2020 [ cit. 2 02 0 - 12 - 1 1 ] . https://callhippo.com/telephony/interne t - communicatio ns - w hat - is - it - w ays - t o - communic ate - over - the - internet # � Camb ridge Dic ti onary . Taboo [ Online ] . [ c it. 2 02 0 a - 1 2 - 1 1 ] . https: // dictionary. c ambrid ge.org/dictionary/english/ ta boo � Cambrid ge D ictionary . Prof a nity [ O nline ] . [ c it. 202 0 b - 1 2 - 12 ] . ht tps://dictionary.cambridge.org/dictionary/english/profanity � Cambridge Dictionary. Flamin

58 g [ Online ] . [ cit . 2021 - 01 - 03
g [ Online ] . [ cit . 2021 - 01 - 03 ] . https://dictionary.cambridge.org/dictionary/english/flaming � CGP Gr ey. W hat is Reddit? [ Online ] . post ed 9 th of September 2013 . [ cit. 2020 - 12 - 04 ] . https://www.youtube.com/watch?v=tlI022aU WQ Q � Dictionary.com . Epithet [ Online ] . [ cit. 2020 a - 12 - 12 ] . https:// www.di ctionary.com/b rowse/epithet � Dictiona ry.com . Vulgar ism [ Online ] . [ cit. 2020 b - 12 - 12 ] . https: //www.dictionary.com/ br owse/vulgarism � FrontPageMetrics. Complete L ist of Subreddits [ Online ] . last updated 5 th of December 2020 . [ cit . 2 020 - 12 - 05 ] . https://f ro ntpagemetrics.com/list - all - subreddits � Holloway, Synchronous vs. Asynch ronous Communication [ Online ] . [ cit. 2021 - 01 - 03 ] . https://www.holloway.com/ g/ remote - work/sections/synchronous - vs - asynchronous - communication � J ORDAN , John - Erik. The Science of Curse Wo rds: Why The &@$! Do We Swear? [ Onlin e ] . 2018 [ c it. 202 0 - 1 2 - 1 3 ] . https: //www.ba bb el.com/en/magazine/ why - do - we - s wear � 58 MA LLICK, Abis hek . 5 best video game streaming platforms as of 2020 , [ Online ] . published August 17 th 2020. [ cit. 2021 - 0 1 - 0 2 ] . https://www .sportskeeda.com/esports/5 - be st - video - ga me - streaming - platforms - 2020 � Merriam - We bster Dictionary . Epithe t [ Online ] . [ c it. 202 0 a - 1 2 - 12 ] . https://www.merria m - webster.com/dictio na ry/epithet � Merriam - Webster Dictionar y . Profanit y [ Online ] . [ c it. 202 0 b - 1 2 - 12 ] . http s://ww w.merriam - webs ter. com/dictionary/p rofa nity#synonyms � Merriam - Webster Dictionary . Ob scene [ Onl ine ] . [ c it. 202 0 c - 1 2 - 12 ] . htt ps://www. merriam -

59 webs ter.com/dictionar y/obsc ene �
webs ter.com/dictionar y/obsc ene � Merriam - Webst er Dictionary . Vul gar [ Online ] . [ c it. 202 0 d - 1 2 - 12 ] . https ://www.merr iam - webster.com/dicti on ar y/vulgar � Merri am - Webster Dictionary . V ulgar ism [ On line ] . [ c it. 202 0 e - 1 2 - 1 2 ] . https://w ww.merriam - webster.c om /dictionar y/vulgarism � Nebojteseinternetu.cz. On - line ko munikace [ Online ] . [ cit. 2021 - 0 1 - 0 3 ] . https://w ww.n ebojteseinternet u.cz/page/3416/on - line - komunikace/ � Online Ety mo logy Dictionar y . Taboo [ Online ] . [ c it. 202 0 - 12 - 1 1 ] . http s://www.etymonline.com/wor d/taboo#et ymon lin e_v_4300 � O n line Latin Dictionar y. Vu lgus [ On li ne ] . [ cit. 2020 - 12 - 12 ] . https://www. online - latin - dictionary.com/l atin - englis h - dictionary.php?lemm a= VULGUS1 00 � Oxford Lea r ner ’ s Dictionari es . Taboo [ Onlin e ] . [ c it. 202 0 - 12 - 11 ] . h ttps ://www.oxfo rdlear nersdictiona ries.com/definition/american_english/taboo � Reddit . [ Online ] . [ cit. 202 0 a - 12 - 04 ] . https://www.red di t.com/ 59 Reddit.com. Today ’ s Top Growing Communit ies [ Online ] . last updated 4 th of De cember 2020. [ cit. 2020 b - 12 - 04 ] . http s:// www.reddit.com/s ubreddits/leaderboard/ � Red dit. Reddit Conten t Po licy [ Online ] . [ cit. 2020 c - 12 - 31 ] . https://www.redditinc.com/policies/content - policy � Reddit. How old d o you need to be t o use Reddit? [ O nline ] . [ cit. 2020 d - 12 - 31 ] . https://reddit.zendesk.com/hc/en - us/articles/360043066492 - How - old - do - you - need - to - be - to - use - Reddit - � Reddithelp . What is Reddit? [ Online ] . last updated 5 months ago . [ cit. 2020 a - 12 - 04 ] . https://www.reddithel

60 p.com/hc/en - us/articles/20451147 9 - W
p.com/hc/en - us/articles/20451147 9 - What - is - Reddit - � Reddithelp . What are communities or “ subreddits “ ? [ Online ] . last updated 5 mont hs ago . [ cit. 2020 b - 12 - 04 ] . https://www.reddithelp.com/hc/en - us/artic les/20453356 9 - What - are - communities - or - subreddits - � Reddithe lp . Promoting Hate Based on Identity or Vulnerability [ Online ] . last upd ated 6 months ago [ cit. 2020 c - 12 - 31 ] . https://www. reddithelp.c om/hc/en - us/articles/360045 715951 � SOCIOLOGY GROUP . 201 8, Examples of Ta boos in Societies Around the World [ Online ] . [ c it. 202 0 - 12 - 11 ] . https://w ww.sociologygroup .co m/ tab oo - mean i ng - examples - types/ � Techterms.com . Flaming [ Online ] . [ cit. 2021 - 0 1 - 03 ] Twitch.tv , Harassment [ O nlin e] . [ cit . 2020 - 12 - 29 ]. https://www.twitc h. tv/p/legal/communit y - guidelines / harassment/ � VideoGamesStats. Twit ch Stats , Facts and News [ Online ] . last updated on Decembe r 30 th 2020, [ cit. 2 021 - 0 1 - 0 2 ] . https://videogamesstat s .com/ tw itch - stats - facts/ � WH IT E, Mary G . . 2014, Examples of Taboos in S ociet ies Around the World [ Online ] . [ c it. 202 0 - 1 2 - 1 1 ] . https://ex ample s.y ourdicti onary.com/examples - of - taboo.htm l � 60 W IDMAN, Jake. What is Reddit? [ On line ] . posted 26 th of Dec ember 2020. [ cit . 202 1 - 0 1 - 0 1 ] . https://www.digitaltrends.com/ web/what - is - reddit/ � Wikipedia. Twitch (Service) [ Online ] . last edited 1 st of December 2020 , [ cit. 202 0 a - 12 - 02 ] . https://en.wikipedia. org/wiki/Tw itch_(service) � Wikipedia . Internet slang [ O nline ] . l ast 26 th of December 2020, [ cit. 2021 - 0 1 - 0 3 ] . https://en.wikipedia.org /wiki/Internet_slang