How Does Code-Switching Operate on French Twitter? 


How Does Code-Switching Operate on French Twitter?

Taoues Hadour Myers

Department of Modern Languages and Literatures, University of Central Florida

 


 

 Abstract

This study investigates how English-French tweets operate on Twitter in France. Tweets are analyzed according to Poplack’s framework on code-switching (CS): tag-switching, inter-sentential switching, and intra-sentential switching. The data was collected in R.  The results reveal that intra-sentential switching is the most frequent type of CS. Intra-sentential switching reflects higher language proficiency levels. However, the level of French-English bilingualism in France remains relatively low. People in France might not be bilingual in real life, but in an online environment, their proficiency appears to be quite high. This study gives us a deeper understanding on how social media has an impact on online users’ linguistic proficiency in France.

Keywords: code-switching, French, social media, sociolinguistics.

Introduction

Today, English is the Lingua Franca. It is the most spoken language in the world and it is widespread in France. There is an increasing use of English words and expressions in the French language. However, French people are not bilingual. It has been suggested that only a minority of French people are able to speak English relatively well (Siebens, 2016). In fact, France has some of the weakest English proficiency skills in the European Union. According to the 2022 EF English Proficiency Index, France ranked 26 out of 35 countries in Europe in English proficiency behind countries such as Belgium, Italy, and Spain. The limited English proficiency has not slowed the use of English expressions and vocabulary introduced by the internet and social media.

The influence of English on other languages has been magnified significantly by the advent of the World Wide Web, and the concurrent widespread use of English as its lingua franca. More than previous forms of media, the mass media and associated technologies facilitate the rapid development and diffusion of borrowings. (Saugera, 2017, p. 7)

Different types of English lexical items have been emerging in the French language over the past several decades. Indeed, many English borrowings made their way into the French lexicon (Saugera, 2017). Lexical borrowing is a word adopted from another language and involves culturally related words that are associated with sports, food or clothes (Romaine, 1995). The present study investigates how French and English CS tweets operate on French Twitter. Code-switching (CS) and borrowing are two distinct phenomena (Poplack, 1980). If a lexical item shows only syntactic integration or only phonological integration or no integration at all, then it is an instance of CS. However, if a lexical item is integrated morphosyntactically and phonologically into the base language, then it constitutes an instance of borrowing.

The objective of this study is to document the level of bilingualism from Twitter users geolocated in France through a manual analysis of types of code-switching. Here are the questions I explore in this study: How does CS operate on Twitter in France? Does CS on Twitter resemble the patterns of CS documented in bilingual conversations? I address those questions by analyzing three types of CS using Poplack’s framework (1980), namely tag-switching (the insertion of a tag or certain phrases in an utterance that is entirely written in another language), inter-sentential switching (a change of language between individual sentences where each individual sentence or clause is written in one language or the other), and intra-sentential switching (a change of language within the same sentence or clause).

Background and Related Work

Since this research is investigating French and English CS tweets on Twitter, it is vital to provide a brief overview of essential studies on CS. Code-Switching occurs in a bilingual environment when a speaker alternates between the use of two or more languages in the same discourse (Gumperz, 1982). Milroy and Muysken defined code-switching as “the alternative use by bilinguals of two or more languages in the same conversation” (1995, p. 7). A popular misconception about CS is that it indicates a lack of proficiency in both languages. However, linguists such as Weinreich et al. (1968), Poplack (1980), and Myers-Scotton (1992), argue that CS is, on the contrary, a sign of bilingual ability:

Code-switching is a verbal skill requiring a large degree of linguistic competence in more than one language, rather than a defect arising from insufficient knowledge of one or the other… [R]ather than representing deviant behavior, [it] is actually a suggestive indicator of degree of bilingual competence. (Grosjean, 2010, p. 57)

            CS is not random and follows certain patterns. Indeed, it is subject to grammatical rules. In her study, Poplack (1980) identifies three types of CS based on the degree of integration: tag-switching, inter-sentential and intra-sentential switching. Tag switching “is an insertion of a tag in one language into an utterance which is entirely in other language” (Hamers & Blanc, 2000, p. 25). Tags can be inserted anywhere in a sentence without disturbing the syntactic order. In inter-sentential switching, the switch occurs between sentences where one sentence is in L1 and the other in L2. This type of switching involves more syntactic complexity than tag switching because the user has to follow the rules of both languages. Therefore, speakers have a higher level of proficiency in both languages. Intra-sentential switching occurs inside the same clause or sentence, generally in the middle. Intra-sentential switching requires the highest syntactical risk and is consequently used by bilinguals with a high level of fluency (Romaine, 1995).

Auer (2005) argues that CS can be seen as an index of social identity. Speakers may express their identity through different social situations. Auer also claims that switching to another language may indicate a group membership. Furthermore, Gumperz (1982) refers to this type of switching as the we-code and they-code. The we-code is associated with in-groups, privacy, and intimacy. The they-code refers to language used for out-group relations.

Otsuji and Pennycook (2010) present a different perspective on the use of different languages in conversational interactions. They introduce the term metrolingualism to explain linguistic interactions resulting from various interactions among people with different backgrounds in a contemporary setting. They define metrolingualism as a “product of modern and often urban interaction, describing the ways in which people of different and mixed backgrounds use, play with and negotiate identities through language” (Otsuji & Pennycook, 2010, p. 240). The term is used in order to capture the current social, cultural, linguistic, and geopolitical environment.

In recent years, linguists have developed a strong interest in studying CS on social media. For instance, Novianti (2013) investigated CS among Twitter users using Poplack’s framework (1980). The study involved students from a department of English Education. The results show that intra-sentential switching was the most frequent type of code switching (56.67%) followed by inter-sentential switching (38.3 %). Twitter users also used mainly English as their language combination with Indonesian-English being the most frequent language combination (73.33%).

Bali et al. (2014) gathered data from Facebook generated by English-Hindi bilingual users. They analyzed code-mixing both in English embedding in Hindi, and Hindi embedding in English. In their analysis, they make a differentiation between code-mixing and code-switching. Code-mixing refers to instance of inter-sentential switching, as opposed to code-switching, which usually occurs at the intra-sentential level. They looked at the frequency of use in order to determine cases of borrowing or code-mixing. The results of the analysis showed a significant amount of code-mixing.

Ariasih et al. (2021) analyzed CS in tweets posted by Indonesian K-Pop fans on Twitter using also Poplack’s theory on types of CS. The results of the study show that inter-sentential and intra-sentential switching were the only two types of CS observed on Twitter with intra-sentential switching being the most frequent type of CS. Tag-switching was not found in the results.

While those studies share similar results with inter-sentential and intra-sentential switching being the most frequent type of CS, the present study brings awareness to French behavior online. Indeed, there have not been many large-scale studies focusing on code-switching in French-English tweets in France.

Methodology

In this study, tweets with instances of English and French were retrieved though R (R Core Team, 2018) using the rtweet package (Kearney, 2018). The software RStudio was used to collect the data. In order to collect a large amount of English and French tweets, several steps were necessary. First, codes with the following parameters were created: tweets with language classified as English and tweets geo-tagged in France or with a French region listed in the profile. The idea was to be able to collect a large number of tweets, so that even if a large portion of tweets as non-French were discarded, a significant number of tweets would still be analyzed in the dataset. Retweets were not included in the corpus.  From March 7, 2018 to April 22, 2018, the codes were run three times a week using Rstudio and eventually produced around 600,000 tweets. Tweets were then filtered with the following parameters: tweets geo-tagged in France or French region listed in profile and with at least one tweet in French. This process produced a total of 46,845 tweets. The corpus had to be filtered down manually because the data was not perfect. The dataset is organized according to the following variables: language, geolocation, and region listed in profile. In the language dataset, several tweets appeared to be from other countries such as Canada or Belgium. Therefore, tweets from countries other than France were removed. Moreover, since the code focuses on the user and not on the language, some tweets were exclusively written in English or in French. Consequently, all the tweets written solely in French or in English were removed from the dataset. The final dataset analyzed in this study involves 2,212 tweets, all representing English-French tweets from Twitter users located and geotagged in France. The dataset might seem limited in size. However, it is worth highlighting that the sample provides a fairly representative portrayal of individuals who are using English in their French-based tweets, since the study captured all French-English tweets geolocated in France over a six-week duration[1].

 

[1] This study is based on the following dissertation: Hadour, T. (2019). #LanguageMixing on Twitter [Unpublished doctoral dissertation]. University of Missouri.

Analytical framework 

The analysis of the dataset is framed according to Poplack’s (1980) types of code-switching based on where the switch occurs. Tweets are manually divided into the three types of CS: tag-switching (the insertion of a tag or certain phrases in an utterance that is entirely written in another language [see 1]), inter-sentential switching (a change of language between individual sentences where each individual sentence is written in one language or the other [see 2]), and intra-sentential switching (a change of language within the same sentence or clause [see 3]). A comparative approach between regular tweets (original posts limited to 280 characters) and replies is used in this study in order to determine the most frequently used type of CS.

 Intra-sentential switching

  1. Psq kinkinkin men are trash #feministe #ecritureinclusiveexclusivekhraclusive https://t.co/4VPLEC3DfV

    [Cause kinkinkin men are trash #feminist #writinginclusiveexclusivekhraclusive https://t.co/4VPLEC3DfV]

Inter-sentential switching 

2. Ça me fait rappeler que je n’ai toujours pas regardé Moana. Shame on me. https://t.co/cLX1a26A00

It reminds me that I still have not watched Moana. Shame on me. https://t.co/cLX1a26A00]

Tag-switching

(3) @leonidasbrr Bruh t'es grave le dernier à être raciste même moi on pourrai plus me taxer de raciste que toi

[@leonidasbrr Bruh you’re the last one to be racist even I we could say I’m more racist than you]

Results 

The results of the study reveal that intra-sentential switching is the most frequent type of code-switching used by Twitter users geo-tagged in France, both in replies and in regular tweets with a total of 1,731 tweets. The study finds that 1,088 out of 1,468 regular tweets are switched at the intra-sentential level, which represents 74% of study samples. In replies, 643 out of 744 tweets are also switched at the intra-sentential level, which represents 86% of the data. Inter-sentential switching is the next most commonly used switch in regular tweets and occurs 317 times out of 1,468, or 22% of the time. Tag-switching is found both in regular tweets and replies. It represents the least common type of CS in regular tweets (63 tweets). However, in replies, the least common type of CS is inter-sentential tweets (38 tweets) as the table below shows:

Table 1

Types of CS distribution

 

Intra-Sentential Switching

Intra-sentential switching is found in 1,731 out of 2,212 tweets in the sample, and it represents the most frequent type of code-switching in both regular tweets and replies, accounting for 78% of the corpus (Table 1). Other studies (Poplack, 1980; Myers-Scotton, 1992; Novianti, 2013; Ariasih et al. 2021) found similar results. Intra-sentential switching is a common phenomenon in code-switching, both in online communities and in face-to-face conversations.

            With intra-sentential switching, the switch can occur anywhere in the tweet and can include one word (the word switched is inserted either in the middle of a sentence or at the end) such as tweets (4), (5); or several words/phrases in English (the switch can occur only one time in one part of the sentence or several times in different parts of the sentence) such as tweets (6), (7), (8). It bears mentioning that even though single anglicisms are counted as switched under the broad definition, the examples listed below [(4); (5)] could easily be analyzed as borrowing. The adjective cute and the noun mood are both adapted morphosyntactically, and we could assume phonologically as well. The nature of language online, especially on Twitter, makes it difficult to decide whether a word or phrase is integrated in another language, let alone its usage frequency.

Single anglicisms

      (4)  @harrystxpid @jarreash Bah oui elle est cute la photo

             [@harrystxpid @jarreash Well yes the picture is cute

      (5)  Mon plus grand mood de tout les temps: https://t.co/CXz8IedQw9

             [My biggest mood of all time: https://t.co/CXz8IedQw9]

          In the following phrasal anglicisms, the switch occurs at the end of the sentence as opposed to the single anglicisms which occurs in the middle of the sentence. The users show a good level of English proficiency as the switch does not violate the surface syntactic rule of either language.

 Phrasal anglicisms

      (6)  @Kobar94 meilleur bae ever

      [@Kobar94 Best bae ever]

(7)  @PandaTriste J'sais j'sais je continue de t'impressionné day after day, night after night

      [@PandaTriste I know I know I continue to impress you day after day, night after night]

  (8)  Toujours je demande à Laura pour mes photos Instagram bc she will never lie to me

      [I always ask Laura about my Instagram pictures because she will never lie to me]

 Inter-Sentential Switching

          Inter-sentential switching represents 16% of the tweets in our data sample, or 355 out of 2,212 tweets (Table 1). Because the results indicate three different patterns, inter-sentential switching tweets are divided into three categories. The first category of inter-sentential switching is a dialogue where a question is written in French, and the answer written in English (9) (10) (11). An interesting example is tweet (9), which uses the word nop instead of nope as a translation of French non. Here, the user either made a typographical error, or might have chosen the wrong word form due to limited English proficiency.

 Personal question L1 / Answer L2 in regular tweets

        (9)  T’es en couple—Nop https://t.co/XxByiq2LRQ

               [Are you in a relationship? – Nope]

        (10)  Ton ex?—NEXT lol https://t.co/gRe73ZfHP2

               [Your ex?—NEXT lol]

         (11)  Genre j’ai demain j’ai cours à 7h45 ? I did not sign up for this

              [Tomorrow I have class at 7:45 am? I did not sign up for this]

          The second category is emotionally motivated; the switch occurs in order to illustrate or punctuate the tweet with a feeling or a comment. This usage bears similarities to the use of emojis to add tone or clarity to the tweet (Dresner & Herring, 2010). It bears noting that the switch occurs at the end of the tweet, similarly to emojis. For instance, in tweet (12) the user shows excitement with the word “breathe”. In tweets (13) and (14), the users are switching to English to express emotion with emojis at the end of the tweet to add clarity.

 Feelings / comments

(12)  sinon..... on se rapproche encore plus du concert de lana.... breathe

      [Otherwise… we are getting closer to lana’s concert… breathe]

(13)  @__logann Ah je vois vite fait qui c'est et je crois qu'elle a un mec. Sorry :/

      [@__logann Oh I kinda see who she is and I think she has a boyfriend. Sorry:/]

  (14)  @kylogwen @ArthurSoraii @JulianeNcls @kindasnowhite @bucksyor    ÉVIDEMMENT JE PLAISANTE. I LOVE YOU MY LITTLE RED SHIT💛💛💛 https://t.co/9AqtmKiBuw

      [@kylogwen @ArthurSoraii @JulianeNcls @kindasnowhite @bucksyor Of course           I’m kidding. I love you my little red shit SHIT💛💛💛 https://t.co/9AqtmKiBuw]

          The last category occurs mostly when the switch takes place in a tweet in order to advertise a product (15) or an event (16). Most of the tweets in this category are spams generated from a third-party source and follow the same format as tweet (15). It bears noting that the language used on Twitter is different from traditional written platforms; the use of punctuation marks might not be used in conventional manners, especially in tweets used for advertising purposes. Therefore, some tweets could be categorized as tag-switching as well, such as tweet (16) with save the date.

Advertising

(15)  Hybride CALLAWAY WARBIRD Regular https://t.co/3SBzrfoCOu ⛳ Alerte Bons Plans Golf ⛳ ! (En savoir +) https://t.co/LbaaBwNOCd

[Hybride CALLAWAY WARBIRD Regular https://t.co/3SBzrfoCOu ⛳ Alert Good Deals Golf ⛳ ! (more) https://t.co/LbaaBwNOCd]

(16) Save The Date | #FoodUseTech | 20 & 21 sept. 2018 | Dijon   Expérimentez les solutions technologiques et rencontrez les acteurs du digitaldans tous les domaines de l’alimentation, du champ à l’assiette ! @LaFoodTech https://t.co/2ball3SFRy https://t.co/Fd4ySv4nUS

[Save The Date | #FoodUseTech | 20 & sept. 21st 2018 | Dijon Experience technological solutions and meet digital actors from all the fields of food, from the field to the plate @LaFoodTech https://t.co/2ball3SFRy   https://t.co/Fd4ySv4nUS]

Tag-Switching

          Only 126 tweets out of 2,212 insert a tag or an interjection in English, representing 6% of the tweets. However, tag-switching is more frequent in replies than in regular tweets, with 8% for replies and 4% for regular tweets (see Table 1). In her study on code-switching, Poplack (1980) makes a distinction between “intimate” and “emblematic” CS, which refers to the form of the switch. Intimate CS occurs frequently and involves switching at the intra-sentential level, whereas emblematic CS occurs occasionally and involves tag-switching. Emblematic CS is more common in out-group communication. Perhaps, users in this category are replying to people outside of their group. However, since there is no information about the context of the replies, definite conclusions cannot be drawn.

            Among these 126 tweets, the two most frequent tags/interjections in both replies and regular tweets at the tag-switching level are OMG (40 tokens), and WTF (13 tokens). Some examples of the top two interjections are provided below:

           (17)  @mugheadjones Omg J’espère ne pas te croiser sur la route 😂😂

                   [@mugheadjones Omg I hope I won’t see you on the road 😂😂]

(18)  @raph_lopes @cephalalgie WTF G RIEN FAIT

                   [WTF I have not done anything]

           It is perhaps not surprising to find that OMG and WTF are the most used interjections in light of the findings provided by Isnards in his Dictionnaire du nouveau français (2014). He indicates that OMG and WTF are the most common acronyms used online. Isnards (2014) argues that WTF is often used as an equivalent to OMG and could signify exasperation such as tweet (18). It can also be used to express surprise such as in tweet (17). Other similar tags are also frequent with a few instances of vocatives such as honey, baby, sista, and buddy used at the end of the tweet (see examples below). It bears noting that all the vocatives are endearing nouns. The user switches to English to express positive emotion and show affection.

            (19)  jsuis jaloux — De quoi honey? https://t.co/4cwlvZyxoL

[ I’m jealous— Of what, honey? https://t.co/4cwlvZyxoL]

  (20) 👯‍♀️ oulaaa que jtaime toi, t'es un délire change pas baby

[👯‍♀️ oh I love you, you are something don’t change baby]

(21)  @lavraiephyllis T’as compris tout de suite sista

[@lavraiephyllis You got it right away, sista]

(22)  @Ijimines t'arrive trop tard buddy

[@Ijimines You came too late, buddy]

 Discussion

          Inter-sentential and intra-sentential code-switching reflect higher language proficiency levels. This type of switching involves the greatest syntactic risk and tends to be used only by the most fluent bilinguals (Hughes et al., 2006). Poplack (1980) also argues that bilinguals often use complex intra-sentential language switches. Since it appears that intra-sentential switching is the most frequent type of code-switching in this study, one might assume that these Twitter users have a high-level proficiency in English. Moreover, when looking at the grammar of some of the CS tweets, the majority of tweets obey the equivalence constraint theory that Poplack (1980) has identified, and which supports the theory of the users’ higher language proficiency level. According to Poplack:

          Code switches will tend to occur at points in discourse where juxtaposition of L1 and L2 elements does not violate a syntactic rule of either language, i.e., at points around which the surface structures of the two languages map onto each other. According to the simple constraint, a switch is inhibited from occurring within a constituent generated by a rule from one language which is not shared by the other. (Poplack, 1980, p. 586)

          For instance, with the following tweet, the use of the place to be does not violate the surface syntactic rule of either language (23). The same can be said about the next tweet with open data (24):

(23)  Le Salon #Automobile d'#Auvergne ouvre ses portes aujourd'hui ! 🚗 Entre voitures d'exception, baptêmes de drift 🏁 et animations en tout genre, c'est assurément The place to be ce week-end! https://t.co/iD3dxCSkID

[#Car Show of #Auvergne is opening their doors today! 🚗 Between exceptional cars, drifts, 🏁 and all kind of animations, it’s surely The place to be this weekend! https://t.co/iD3dxCSkID]

(24)  Open data des territoires : les premiers résultats de l'observatoire dévoilés - https://t.co/ElOYPbDDpN #batiment #btp #construction #actualite

[Open data of territories: the first results of the observatory released - https://t.co/ElOYPbDDpN #building #btp #construction #news]

          However, a few instances of CS tweets in the data show a violation of the surface structure of the English language. They have the following construction: go + infinitive verb/location. By looking at the grammar of the following tweets (25) (26), Twitter users’ language proficiency level can be rated as not very high. The standard construction for the following example (25) should be: going to + infinitive. In the following tweet (26), a preposition is missing and the standard construction should be: go to + location.

            (25)  Go PRENDRE UN DOLIPRANNE  #RencontreLes5SOSAvecOTM

[I’m going to take an aspirine #RencontreLes5SOSAvecOTM]

  (26)  Jpense j'vais go Lille samedi, qui pour venir ?

                     [I think I’ll go to Lille on Saturday, who wants to come?

           The assumption that Twitter users in France are highly bilingual based on the complexity of their code-switching is also belied by the fact that French people are not particularly fluent in English. Saugera states in her study that French people are “not bilingual in English in any conventional way” (2017, p. 40). It should be noted that the evaluation of the English proficiency of French people is just approximate and may not accurately reflect the degree of societal bilingualism in France. But how then are we to explain their online proficiency? English overwhelmingly dominates the Internet; even as other languages are catching up. In 2020, English was the top language used on the Internet followed by Chinese and Spanish (Internet World Stats). Melitz (2016) points out that English, and primarily American English, is still the lingua franca of the Internet, and is channeled through a number of mass media platforms. Therefore, the global nature of the English language may explain the current influx of English loanwords and loan phrases. Online users in France navigate in a multilingual environment and in consequence are exposed to the English language every time they go online. Thus, they are able to easily pick up words and expressions from English examples, and then reuse them in accordance with the topic of their discussion.

          Otsuji & Pennycook (2010) introduced the notion of “metrolingualism”, explaining the use of multiple languages online as a product of various interactions from speakers with different backgrounds in the current context of globalization where English is the uncontested Lingua Franca. Might we consider CS to be a product of various linguistic interactions online, with English being the dominant language? Could we call this phenomenon online bilingualism or online multilingualism? It bears mentioning that the definition of bilingualism has been a subject of debate over the degree of proficiency. Indeed, language proficiency can vary from having some conversational fluency in one language, to being fully versed in reading, writing, and speaking two languages (Bathia & Richie, 2013). We can thus say that people in France might not be bilingual in real life, but in an online environment their writing English proficiency appears to be quite high. Twitter is considered a multilingual platform whose main language is English. Therefore, it is not surprising that Twitter users in France routinely code-switch in English.

          Additionally, the language used online is quite similar to everyday oral communication (Mesthrie, 2011). However, the online environment is different from face-to-face interactions. Twitter allows users to post tweets which can be read by everyone, but these tweets are restricted to 280 characters. Therefore, in addition to the added visibility tweets provide to users, this character restriction has changed how people express themselves on Twitter. The use of different acronyms, such as DM for direct message, as well as other languages such as English might be used not only as strategies to conform to the reduced character amount imposed on Twitter users, but also as a way to increase visibility at the same time. Twitter users in France might be more proficient in English online because of the character restriction but also because tweets are visible to the public.

          Moreover, it bears noting that the tweets analyzed in the data are from Twitter users who are geo-tagged in France. Their backgrounds, such as age, gender, and language proficiencies are unknown. However, younger generations appear to be the predominate users of social media. In 2019, the most active age group on social media was identified as those between 18 and 24 years of age. 94 percent of respondents in this age group reported using social media services, dropping to 83 percent of respondents among those aged 25-39 years (Statista). It is therefore logical to presume that Twitter users in France are all combined into this age group as well. We would thus be interested in learning whether people in France are better in English online than in face-to-face communications. A possible continuation of the study would be to compare instances of code-switching from other social media platforms such as Facebook or YouTube as it would provide more extensive knowledge regarding online users’ level of English proficiency in France. Another question of interest is whether younger generations of people in France are more proficient in English than their older counterparts.

          It bears mentioning as well that most CS tweets are regular tweets with 1,468 tweets (representing 66% of the dataset) as opposed to replies (accounting for 34% of the data) which could be interpreted as a desire on the part of French users to reach a wider audience by communicating in the most widely used language on Twitter. Nguyen et al. (2015) explored how audiences influence the use of minority languages on Twitter. They found that tweets directed to larger audiences were more often written in the majority language, whereas in personal conversations, twitter users were more likely to switch to the minority language. Is this because users are trying to alter their speech to accommodate a specific interlocutor? Giles et al. (2012) argue that people tend to accommodate their speech when speaking. They either converge (adopt similar styles of speaking) or diverge (speak differently) according to whether they want to show solidarity or whether they want to demonstrate distinctiveness and increase social distance. Unfortunately, logistical constraints in how the corpus was constructed made it impractical to examine the initial tweets to which the user responded to see if the interlocutors code-switched in English. It would have been interesting to return to the initial conversations to see if English was used in the earlier tweets. This question could be explored in a continuation of this study.

 Conclusion

          The present work has provided an initial study of English and French language mixing on Twitter by investigating users’ level of English written proficiency through a manual analysis of types of code-switching in France. Intra-sentential switching represents the most frequent type of CS, which tends to be frequent among the most fluent bilinguals. Because of Twitter’s popularity, the English language is reaching people from all over the world, and this is having a direct influence on French society. Social media has changed the way we communicate and has an impact on various languages. It is a reflection of the linguistic and cultural diversity which can expand our horizons.

 

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