CATEGORISING ENGLISH TEXT FRAGMENTS
BY LENGTH IN LITHUANIAN “TWITTER” MESSAGES:
A TRANSLANGUAGING PERSPECTIVE

**Abstract.** Translanguaging research has predominantly focused on spoken interaction in educational contexts, leaving written multilingual practices outside classrooms comparatively underexplored. This article addresses that gap by examining translanguaging in written, asynchronous computer-mediated discourse by analysing the length of English text fragments embedded in 2,100 Lithuanian “Twitter” messages. Adopting computer-mediated discourse analysis within a translanguaging framework, this study operationalises a length-based typology, reports relative frequencies of each length category, and discusses their characteristic features, thus linking structural distribution, pragmatic function, and sociocultural meaning. A total of 2,493 English fragments were identified and categorised as word-length (1,459, or 58.5%), phrase-length (603, or 24.2%), and sentence-length (431, or 17.3%). This corresponds to approximately 118.7 fragments per 100 tweets, or roughly 75.6 fragments per 1,000 words. Empirically, the research shows that English single-word insertions dominate, functioning as compact, high-salience resources for stance-taking, identity indexing, technological literacy signalling, and memetic reference, whereas longer forms are comparatively rare. Methodologically, this paper demonstrates how the structural and pragmatic dimensions of computer-mediated discourse analysis can be adapted to capture the distribution, form, functions, and meaning of multilingual features in social media texts. These findings extend beyond the Lithuanian-English context, as they highlight how brevity-driven platforms, such as “Twitter”, foster highly lexicalised translanguaging, with implications for understanding

<footnote n="1">¹ https://orcid.org/0009-0004-6655-6105, Klaipėda University, Faculty of Social Sciences and Humanities, Klaipėda, Lithuania, indersmbox@gmail.com</footnote>
language contact, digital literacy, lexicography, and language technology. Limitations include the single-corpus scope and the absence of user metadata. This study concludes with recommendations for comparative, cross-platform, and mixed-methods research to further theorise short-form translanguaging as a distinct phenomenon in global digital communication.

**Keywords:** Lithuanian-English translanguaging, computer-mediated discourse, language contact, multilingual communication, social media, the social network "Twitter"

# 1. Introduction

The incorporation of English words into Lithuanian social media exchanges has become widespread across the Lithuanian digital sphere. This multilingual language contact phenomenon, referred to as *translanguaging*, is defined here following Dumrukcic (2020, pp. 111-112), García and Kano (2014, p. 261), García and Lin (2016, p. 117), Moore and Stoelting (2021, p. 8), Otheguy et al. (2015), Vogel et al. (2018, p. 91), and Wei (2011, p. 1223). These authors characterise translanguaging as an adaptable, fluid, dynamic, transformative, and context-sensitive discursive practice, which occurs within a single communicative event. It involves language users drawing on their full linguistic repertoires, alternating between or blending resources from two or more languages or their varieties they are proficient in for communicative purposes, but still respecting the grammatical expectations of the main language they are using.

Although contemporary translanguaging research has broadened in recent years and there is a growing, albeit still somewhat limited, interest in exploring the applications of translanguaging in various multilingual contexts, much of the research remains concentrated in the educational context, particularly in language learning classrooms. This focus is unsurprising given the theory's origins and its demonstrable productivity in pedagogical contexts (Vogel & García, 2017). Consequently, investigations of translanguaging outside the classroom setting, for example, in written, public, or digital domains, still remain comparatively scarce. The Lithuanian scholarly output, likewise, reflects this tendency, with the majority of studies situated in the educational context (see, e.g., Geben & Zielińska, 2021; Mačianskienė & Bijeikienė, 2021; and Meškauskienė et al., 2021).
Much translanguaging research has focused on spoken or written interaction in classroom settings, typically examining exchanges between teachers and students who are familiar with one another and constantly interact. Therefore, there has been relatively little systematic attention to the grammatical, morphological, lexical, and syntactic properties of the target language fragments into which language users translangeage. One of such understudied properties is *fragment length*. Scholars usually discuss levels of translanguaging and tend to focus on classroom talk: Sahan and Rose (2021) discern utterance-level translanguaging; Lee and Garcia (2020) explore word-level and sentence-level translanguaging; and Aoyama (2020) and Nugrahaeni and Asib (2022) distinguish word-level, phrase-level, and clause-level translanguaging. These researchers consider the natural oral linguistic output of students and teachers solely in classroom interactions, which include language combinations of Turkish-English (Sahan & Rose, 2021), Korean-English (Lee & García, 2020), Japanese-English (Aoyama, 2020), and Indonesian-English (Nugrahaeni & Asib, 2022). These scholars primarily aim to identify the purposes and contextual factors that influence translanguaging, when and how often it occurs, and what students do during it, but they do not analyse the fragment length of the target language. Accordingly, these authors commonly advocate adopting a translanguaging perspective in both language teaching and research, arguing that it more flexibly captures the functions of multilingual practice than traditional code-switching frameworks.

Despite growing interest in translanguaging, relatively little research examines translanguaging in written language and outside the educational context. This gap prompts an investigation into how translanguaging operates in more diffuse, anonymous online environments, most notably social media platforms, such as “Twitter”. Accordingly, this study asks: What are the principal length-based categories of English text fragments in Lithuanian “Twitter” messages? How prevalent is each category? What are the characteristic formal<fnref n="2" /> and functional features of fragments in each category, given the mediating influence of the “Twitter” platform

<footnote n="2">² Here, *formal* features refer to observable structural properties of fragments (their morphology, syntactic shape, orthography, and internal length), as distinct from their *functional* uses (involving stance, identity work, or memetic reference).</footnote>
on message structure and use? To answer these questions, the aim of this
paper is to operationalise a length-based typology of English fragments,
quantify their distribution in a hand-annotated corpus, and conduct a
qualitative analysis of their features. The theoretical framework and the
methodological procedures are described in the following pages.

## 2. The definition of translanguaging

According to García & Lin (2016, p. 118), Lewis *et al*. (2012), Rymes & Smail (2020, p. 4), Williams (1994), Zhang (2018, p. 46), and Zhang & Ren (2020, p. 3), the term *translanguaging* derives from the Welsh word *trawsieithu*. Coined by pedagogical researcher Cen Williams and his colleague Dafydd Whitehalli in the early 1980s, the concept first appeared in Williams's unpublished doctoral thesis. Initially, *trawsieithu* was translated into English as '*translanguifying*', but eventually Cen Williams and Colin Baker shaped it into its contemporary form – '*translanguaging*'.

García and Lin (2016, p. 118), Rymes and Smail (2020, p. 4), Wei (2011, p. 1224), Zhang (2018, pp. 46–47), and Zhang and Ren (2020, p. 3) note that *trawsieithu* was originally coined for use within the Welsh educational context. It was framed as both a cognitive skill to be cultivated and a special pedagogical approach and practice that foregrounded the learners’ bilingual repertoires rather than teacher-centred instruction. Employed in bilingual classrooms, the practice aimed to effectively revitalise Welsh language learning, enhance competence in both Welsh and English, and strengthen academic literacy through purposeful alternation between languages across listening, reading, speaking, and writing activities. However, translanguaging was initially stigmatised under entrenched monolingual prescriptivism and policies against so-called ‘cross-contamination’ in classrooms (Baker, 2001, p. 9; Dumrukcic, 2020, p. 111; Jacobson & Faltis, 1990, p. 4; MacSwan, 2017, p. 172). Nonetheless, as Wei and Ho (2018, p. 35) put it, the concept persisted and received sustained theoretical development from scholars, such as Auer (2010), Baker (2001), Creese (2017), Crystal (1987), Dumrukcic (2020), Gafaranga (2007), Heller (2007), Jacobson and Faltis (1990), MacSwan (2000, 2017), Pinker (1994), and Wei and Ho (2018).
Over time, translanguaging moved beyond its pedagogical origins and into broader scholarly discourse. Colin Baker (2001) was instrumental in introducing the term to a wider academic audience and elaborated on its pedagogical benefits for bilingual learners' development of academic language. In the last decades of the 20th century, scholars, such as Cummins (1981, 2000), Grosjean (1982), and Hornberger (2003), were among the first to openly challenge the social and linguistic stigma attached to multilingual practices and to promote the use of various multilingual practices in the classroom, including translanguaging. The endorsement of such ideas proved successful. As Rymes and Smail (2020, p. 4) and Wei (2011, p. 1224) explain, Ofelia García's influential work (2009) further consolidated the concept of translanguaging within the international scientific community by articulating a more comprehensive and refined definition. As Rymes and Smail (2020, p. 4) and Wei (2011, p. 1224) note, García's work provided a foundation for subsequent theoretical and empirical work on translanguaging. As Makoni and Pennycook (2007), May (2013), Otheguy et al. (2015), and Vogel et al. (2018, p. 91) observe, the global diffusion of *translanguaging* contributed to a multilingual turn that challenged outdated and entrenched structuralist and hierarchical conceptions of language. This paradigm shift encouraged scholars to reconceptualise language use as fluid and practice-based, prompting broader applications of the term *translanguaging* to describe diverse, boundary-crossing linguistic behaviours – practices that resist neat classification into socially constructed, named language categories (Vogel et al., 2018, p. 91).

As evidenced by the works of Wei and Ho (2018, p. 35) and Zhang (2018, p. 47), because of these developments, the use of the term *translanguaging* in academic literature discussing multilingualism has grown substantially and globally over the past decade. The term has moved beyond its original focus in bilingual education and sociolinguistics to inform work across applied linguistics, communication, and other areas concerned with multilingual and hybrid language practices. As Rymes and Smail (2020, p. 2) and Zhang (2018, p. 47) observe, researchers now deploy the term *translanguaging* to highlight a range of phenomena, including cross-linguistic strategies, hybrid repertoires, and other forms of mediated multilingual communication, resulting in a sizeable and expanding body of scholarship.
In its contemporary conceptualisation, translanguaging encompasses not only the distinct linguistic structures and systems of different languages, but also multiple modalities of communicative activity, such as speaking, writing, signing, listening, reading, and remembering (Wei, 2011, p. 1223). Scholars understand translanguaging to cover a wide range of discursive practices, including code-switching and code-mixing, loanwords and borrowings, translation, creolisation, pidgins, transfer, crossing, lexical coinages, hybrids, and fusions (García, 2009, p. 45; Moore & Stoelting, 2021, p. 8). From this perspective, translanguaging emphasises how language users creatively and transformatively mobilise linguistic resources, thereby constituting a process of resemiotisation (Wei, 2018). Furthermore, these resources also constitute what is known as a *linguistic repertoire*.

According to García and Wei (2014, p. 25), a language user's linguistic repertoire involves the process of drawing on various social features within a fluid and intricate system of numerous semiotic signs, as they adjust their languaging to meet the demands of the immediate task, highlighting its adaptability. Wei (2011, p. 1223) defines the concept of *languaging* as the process of using language to acquire knowledge, to make sense of experience, to express one's thoughts, and to reflect on language use itself. In this process, as Swain (2006, p. 97) explains, language is a mechanism through which thought is made coherent and converted into a product form. As Becker (1988, p. 25) argues, languaging is essentially a verb-centred view that treats language not as a static noun, but as an ongoing, productive process.

According to García (2009, p. 45) and Wei (2011, p. 1223), in general, translanguaging does more than transmit information or knowledge; it also facilitates cross-linguistic discursive practices. It also enables language users to articulate thoughts and to create new meanings by mobilising appropriate linguistic resources. Through translanguaging, language users also perform and express their language identity, drawing on the distinct extents of their personal experiences, values, attitudes, beliefs, ideologies, capacities, and social relations to give voice to emergent socio-political realities. In doing so, they make sense of and communicate their multilingual actuality in a practical, efficient manner. and in synchronised and purposeful acts that create social spaces for language alternation. Importantly, Creese and Blackledge (2010, p. 109), Dumrukcic
(2020, p. 113), García and Kano (2014, p. 261), and Moore and Stoelting (2021, p. 8) caution against treating translanguaging as evidence of linguistic incompetence. Rather, they frame it as a resource through which language users develop new and sustain old components of their linguistic repertoire, treating languages not as hierarchically ranked codes, but as parts of an individual, integrated, and unified linguistic system with multiple features.

A range of related concepts appears in the translanguaging literature, including *languaging*, *translanguaging space*, *code-switching*, *code-mixing*, *code-meshing*, *crossing*, *metrolingualism*, *code-mashing*, *networked multilingualism*, and *translation* (Dumrukcic, 2020; García & Lin, 2016; Rymes & Smail, 2020; Schreiber, 2015; Wei, 2011, 2018; Wei & Ho, 2018; Williams, 2002; and Zhang & Ren, 2020). Scholars routinely map the connections and distinctions between these terms and *translanguaging*, thus highlighting its significance in understanding these multilingual practices. While translanguaging shares affinities with several of these concepts, it is often characterised as analytically broader: not confined to a particular context, linguistic resource, environment, or purpose, but encompassing diverse and dynamic linguistic performances, cognitive activities, knowledge construction, and effective communication. At the same time, researchers warn against conceptual overextension, since an excessively broad deployment of the term *translanguaging* risks terminological vagueness and may undermine its usefulness in empirical analysis.

### 3. Methodology

The social network “Twitter” was selected as the data source. Although the platform was rebranded as “X” in 2023³, the data in this study were collected between 2017 and 2020, and the service is therefore referred to as “Twitter” throughout. Regarding the sample size and data composition, the corpus comprises 2,100 Lithuanian-language “Twitter” messages, totalling 32,960 words (i.e., lexical tokens). Within the collected corpus, the messages range in date from 2007 to 2020. The minimum length of the collected “Twitter” messages is at least two words (one Lithuanian and

<footnote n="3">³ https://www.statista.com/topics/737/twitter/#topicOverview</footnote>
one English), and they may contain as many Lithuanian and English words
as authors manage to fit until they reach the length limit of 280 characters<fnref n="4" />,
imposed by “Twitter” itself at the time the messages were collected.

The *procedure of data collection* included the official "Twitter Advanced Search Tool", used without logging in to search for messages without evoking any possible user preferences (the tool was accessible without an account at the time of collection). The only search criterion relevant to research objectives was 'language', thus it was set to 'Lithuanian'. The results were inspected via the platform's 'Top' view (sorted by relevance and recency). "Twitter" messages were sampled consecutively from the auto-generated, scrollable results page. Each tweet containing coherent text and at least one Lithuanian and one English word was retrieved and recorded. This process was repeated until 2,100 qualifying messages had been extracted. All retrieved messages were saved to a plain-text ('.txt') file together with their hyperlinks for reference.

A *purposive data sampling method* was employed in this paper, since it
involves selecting data considered most relevant to the research question
and objectives, and enables in-depth analysis within a particular linguistic
context (Bryman, 2004, pp. 418-419). The size of the corpus was adequate
for achieving the aims of this paper and ensured both manageability and
robustness, balancing feasibility within research resources and constraints
with the need for a diverse and representative sample. Sampling proceeded
until a practical point of *data saturation* was reached, that is, when additional
data no longer provided meaningful insights for addressing the study's
questions, in line with established principles outlined in the literature on
data sampling methods (Guest et al., 2006; Saunders et al., 2018).

The study adopts *computer-mediated discourse analysis* as its principal
analytical approach, situated within a *translanguaging* framework. Herring
(2001, p. 612) defines computer-mediated discourse analysis as a methodological
specialisation used within the broader field of computer-mediated
communication studies, characterised by its emphasis on language and
language use in computer networked environments, and by its application
of discourse analysis methods to address that emphasis. Crucially, computer-
mediated discourse analysis permits the examination of multiple dimensions

<footnote n="4">⁴ https://developer.twitter.com/en/docs/counting-characters</footnote>
of new-media language, including its structural form, meaning, interactional
practice, social function, and technological affordances, making it especially
well-suited to analysing multilingual practices and translanguaging on
social media (Dumrukcic, 2020, p. 116).

After manually *pre-processing and annotating* each of the 2,100 Lithuanian “Twitter” messages, a total of 2,493 instances of English insertions were identified. Since each message contains at least a single English word, when separated from each other by at least a single Lithuanian word from one or both sides (depending on the position of the English element under scrutiny<fnref n="5" />), each of such elements was considered as a distinct fragment of English text<fnref n="6" />. Then, each English text fragment was further manually processed, coded, and categorised to identify and label its length.

The adopted length-based classification of English text segments is grounded in observable distinctions among words, phrases, and sentences, as commonly discussed in works on English language structure and grammar (e.g., Aarts, 2011; Biber *et al.*, 1999; Plag et al., 2015). In the present study, these distinctions were adapted for the analysis of translanguaging in computer-mediated discourse. Given that computer-mediated discourse is often informal, syntactically incomplete, disconnected, and highly variable, *fragment length* serves as a less theoretically ambiguous and clearer, practical, and functional analytical dimension than rigid syntactic criteria or pragmatic units, such as phrases or utterances.

While several scholars (Aoyama, 2020; Lee & García, 2020; Nugrahaeni & Asib, 2022; and Sahan & Rose, 2021) distinguish between different levels of translanguaging, their studies do not provide explicit definitions of these categories. This study builds on their insights by operationalising a length-based categorisation that is both practically applicable and linguistically grounded. The categories proposed here are not meant to represent strict grammatical units, but rather flexible analytical tools that reflect the functional differences in language use across varying segment lengths. This approach, which reflects both the nature of the data and the goals of the analysis, is particularly suited to the fragmented and

<footnote n="5">⁵ For instance, when an English text fragment is at the very beginning or the very end of a “Twitter” message, it can be separated by a Lithuanian word or words from a single side only.</footnote>

<footnote n="6">⁶ In the case of English-Lithuanian hybrid words (discussed in more detail in Subsection 4.1.), they were considered as English text.</footnote>
creative nature of written, asynchronous computer-mediated discourse
data, where traditional sentence boundaries and grammatical structures
often diverge from standard norms. Therefore, each English text fragment
present in a "Twitter" message was identified, categorised, and counted
as an instance of either word-length, phrase-length, or sentence-length
translanguaging according to the following operational descriptions of
each length category:

*Word-length translanguaging* refers to the insertion of a single English word into a Lithuanian message. These may include English nouns, verbs, adjectives, adverbs, prepositions, conjunctions, interjections, or pronouns. This category also includes English compounds – words, which are composed of two or more elements written as one word or hyphenated (e.g., *kpop, check-in*), as long as they appear as a single lexical unit (Biber *et al.*, 1999, p. 58). To determine whether a given item qualifies as a compound, reference was made to at least two out of the following dictionaries: “Longman Dictionary of Contemporary English”<fnref n="7" /> (LDOCE), the “Merriam-Webster Dictionary”<fnref n="8" /> (MWD), and the “Cambridge Dictionary of English”<fnref n="9" /> (CDE). If confirmed, the instance was categorised as word-length translanguaging. This category captures the shortest possible English insertions, whether simple or morphologically complex, provided they appear as standalone word forms.

*Phrase-length translanguaging* involves the insertion of a short sequence of English words that function together as a text fragment, but do not form a complete sentence. These fragments are longer than a single word and typically consist of commonly used expressions, set phrases, short descriptive or referential chunks, or partial constructions. Examples include adjective phrases (e.g., *super not amused*), noun phrases (e.g., *pizza delivery guy*), or short idiomatic expressions (e.g., *on point*). These fragments lack the full structure of a sentence, such as a subject-verb combination, but are longer and more complex than single-word insertions. Their length and form vary, but they generally function as self-contained multi-word units embedded into Lithuanian message text.

<footnote n="7">7 https://www.ldoceonline.com/</footnote>

<footnote n="8">8 https://www.merriam-webster.com/</footnote>

<footnote n="9">9 https://dictionary.cambridge.org/</footnote>
Sentence-length translanguaging refers to the insertion of complete or nearly complete English sentences into Lithuanian tweets. These segments typically include a subject and a predicate, and often express a full thought or idea. In some cases, sentence-length fragments may also consist of multiple coordinated clauses or short sequences of sentences. Although not always grammatically perfect due to the informal nature of "Twitter" discourse, these English fragments are generally long enough to stand alone as sentences and often carry clear communicative intent. Punctuation or orthographic completeness is not required, and unpunctuated or informal strings that nevertheless instantiate a subject-predicate structure, or perform the discourse function of a sentence, are treated as sentence-length items. Examples may include direct statements (e.g., *I'll become king if I'll ever survive this*), questions (e.g., *god, is that you*), or commands (e.g., *fight me*), whether used alone or inserted between other parts of Lithuanian message text. This category captures the longest and most structurally elaborate English insertions observed in the data.

To illustrate how English fragments appear in context and how fragment-level categorisation was applied, Table 1 presents representative KWIC-style concordance lines drawn from the annotated corpus, with all concordances generated using "AntConc". Each row shows the immediate left and right Lithuanian context surrounding an English fragment ('Hit'), with English translations provided below each example for clarity. Example numbers are presented in the left-most column. All excerpts were anonymised to remove identifying information. These lines were extracted from the hand-annotated dataset to exemplify word-length, phrase-length, and sentence-length translanguaging and to demonstrate category boundaries.

<table><caption>Table 1. KWIC concordance lines illustrating English fragments</caption><thead><tr><th>Example No.</th><th>Left context (Lithuanian)</th><th>Hit (English fragment)</th><th>Right context (Lithuanian)</th></tr></thead><tbody><tr><td>(1)</td><td></td><td>Tfw</td><td>net nežinojai kad turi<br>svajonę o in ima ir išsipildo</td></tr><tr><td></td><td colspan="2">'Tfw you didn't even know you had a dream but then it comes true'</td><td></td></tr><tr><td>(2)</td><td>Labai stiprus</td><td>ego boost</td><td>, kai instagrame kas nors<br>panaudoja'</td></tr></tbody></table>
<table><tr><td></td><td>, kai instagrame kas nors panaudoja'</td><td>close friends</td><td>' funkciją ir supranti, kad tave kažkas vertina.</td></tr><tr><td colspan="4">'It's a huge <b>ego boost</b> when someone uses the 'close friends' feature on Instagram and you realise that somebody appreciates you.'</td></tr><tr><td>(3)</td><td>[Interlocutor username] AS IS RIMTO VERKIU</td><td><b>THIS IS THE BEST THING EVER</b></td><td></td></tr><tr><td colspan="4">'[Interlocutor username] I AM SERIOUSLY CRYING <b>THIS IS THE BEST THING EVER</b>'</td></tr><tr><td rowspan="5">(4)</td><td></td><td><b>story fucking time</b></td><td>katik kazkokia sviesa praskrido pro dangu belekaip greitai, bet negali but zvaigzde, nes</td></tr><tr><td>katik kazkokia sviesa praskrido pro dangu belekaip greitai, bet negali but zvaigzde, nes</td><td><b>literally</b></td><td>rukas visur ir</td></tr><tr><td>rukas visur ir</td><td><b>im shaking unnies</b></td><td>visiskai</td></tr><tr><td>visiskai</td><td><b>omg CHILLS</b></td><td>KAS TEN KAIP UZMIGT SITOJ NEZINIOJ</td></tr><tr><td colspan="3">'story fucking time' just now some kind of light flew across the sky really fast, but it cant be a star because there's <b>literally</b> fog everywhere and <b>im shaking unnies</b> totally <b>omg CHILLS</b> WHAT IS THAT HOW CAN I SLEEP IN THIS UNCERTAINTY'</td></tr></table>

Table 1 provides KWIC-style concordance lines that illustrate representative English insertions in the analysed Lithuanian "Twitter" corpus. These qualitative, context-rich examples ground the length-based categories used in the quantitative analysis, showing how single-word, multi-word, and full-clause/sentence English text fragments operate within the Lithuanian "Twitter" messages and how they were identified in situ. Example (1) illustrates word-length translanguaging (Tfw), example (2) exemplifies phrase-length translanguaging (ego boost and close friends), and example (3) represents sentence-length translanguaging (THIS IS THE BEST THING EVER). Example (4) demonstrates multiple fragment types contained in a single tweet: one word-length insertion (literally), two phrase-length segments (story fucking time and omg CHILLS), and one sentence-length fragment (im shaking unnies). In each case, the length category of each
English text fragment was determined by its internal composition and
by the presence of Lithuanian words separating each fragment.

After labelling every occurrence of each English text fragment, the
**frequency counts** for each length category were computed in "Microsoft
Excel" to derive category percentages and to determine which of them is
the most prevalent and which is the least common. Meanwhile, "AntConc"
was used to verify that the frequency counts were correct. All percentages
are based on the total number of English fragments in the corpus and
were calculated at the fragment level (N = 2,493). Each English fragment
was manually assigned to exactly one category (either word-, phrase-, or
sentence-length), so the category percentages are mutually exclusive and
sum to 100%. As was shown in example (4), individual "Twitter" messages
may contain one or more English text fragments from different length
categories occurring in varying configurations. In this analysis, each
fragment was treated as an independent analytical token and counted
equally regardless of its length. Quantitative counts and percentages for
each length category are reported in Table 2 (in Section 4).

Subsections 4.1, 4.2, and 4.3 present a selection of examples that illustrate salient features of each length category in computer-mediated discourse. The discussion focuses only on linguistic content and category-relevant characteristics rather than on a specific group of "Twitter" users of certain demographic criteria. In each presented example, the English fragment of analytic interest is highlighted in **bold**. An English translation of the full message is provided beneath each example to ensure accurate interpretation of English text fragments, especially when their syntactic role or pragmatic meaning might be ambiguous. These translations help clarify whether the English segment functions as a word, a phrase, or a sentence in its original context. English translations preserve relevant orthographic irregularities where these contribute to analysis, while the remainder of the message is reproduced without special formatting to retain its original linguistic features. To protect user privacy and minimise potential harm, usernames, non-public individual names, and other such identifiable elements were anonymised. Common product, company, and media names were excluded from the English fragment counts to prevent data distortion.
*Definitions for various English words* under inquiry were checked against major online dictionaries: LDOCE, MWD, and CDE. The online dictionary "Dictionary.com"<fnref n="10" /> was used to look up the meanings of words originating in netspeak, such as rekt, lit, and cringe. The crowdsourced online dictionary "Urbandictionary.com"<fnref n="11" /> was consulted for more obscure and recent urban and online slang words and phrases, the definitions of which are simply not available in conventional dictionaries. Names and commonly attributed functions of emoji placed in the immediate vicinity of the various English text fragments were verified using "Emojipedia"<fnref n="12" />. "Urbandictionary.com", "Dictionary.com", and "Emojipedia" were used with caution and only to interpret items absent from established dictionaries. Where relevant, lexical claims were corroborated with at least two authoritative sources.

**4. Findings**

The analysis shows that all English fragments in the dataset can be
classified into three distinct categories based on their length:

1) Word-length;

2) Phrase-length;

3) Sentence-length.

Table 2 reports the number and percentage of English text fragments in
each category.

<footnote n="10">¹⁰ https://www.dictionary.com/</footnote>

<footnote n="11">¹¹ https://www.urbandictionary.com/</footnote>

<footnote n="12">¹² https://emojipedia.org/</footnote>
<table><caption>Table 2.
The frequency of English text fragments by length</caption><thead><tr><td>Length of English text fragment</td><td>Number of instances</td><td>%</td></tr></thead><tbody><tr><td>Word-length</td><td>1,459</td><td>58.5</td></tr><tr><td>Phrase-length</td><td>603</td><td>24.2</td></tr><tr><td>Sentence-length</td><td>431</td><td>17.3</td></tr><tr><td>Total:</td><td>2,493</td><td>100</td></tr></tbody></table>

As Table 2 shows, the annotated corpus of 2,100 Lithuanian “Twitter” messages contains a total of 2,493 English text fragments. This corresponds to approximately 118.7 English fragments per 100 tweets, or roughly 75.6 per 1,000 words. Among these, word-length translanguaging is the most prevalent (1,459 cases, or 58.5%), representing over half of all instances. Phrase-length insertions appear in 603 instances (24.2%), while sentence-length fragments are the least frequent, with 431 occurrences (17.3%), despite being the longest and most elaborate.

These findings suggest that Lithuanian “Twitter” users most commonly insert English elements into single-word fragments, with longer, more elaborate English fragments appearing less frequently. This pattern reflects a general tendency toward brevity and conciseness in English use within Lithuanian tweets. A more detailed qualitative analysis of each category is presented in the following Subsections 4.1, 4.2, and 4.3, including a discussion of their characteristic features.

## 4.1. Word-Length translanguaging

The first and most concise category of English insertions identified in the data is *word-length translanguaging*. This category encompasses individual lexical items, including single English words and compounds. This Subsection presents illustrative examples from the corpus, discusses the defining features of this category, and explores its relevance within the broader context of computer-mediated discourse. To illustrate word-length translanguaging, consider example (5):
(5) Noriu bliuduku nusikirpt Ne, Greta, baik Negalvok apie tai Atrodysi kaip grybas Nu bet **cool** Tu nesi tokia **edgy** BET BLIUUUUUUUDAS NE **Ugh** ...vis tiek noriu Tai gal bent **mullet** NE BLET

‘I want to get a bowl haircut No, Greta, stop it Don’t think about it You’ll look like a mushroom Yeah but **cool** You’re not that **edgy** BUT BOOOOOOWL NO **Ugh** ...I still want to Maybe at least get a **mullet** haircutUCK NO’

Example (5) contains the English text fragments *cool*, *edgy*, *Ugh*, and *mullet*, which constitute instances of word-length translanguaging. These items illustrate a common tendency for language users to employ English sparingly – as short, minimally complex lexical segments used to name or describe singular or simple concepts in English, while the remainder of the message is expressed in Lithuanian. In deploying such insertions, the posters actively access, negotiate, and draw on their linguistic repertoires to include the resources needed to satisfy specific pragmatic or expressive needs, even when those resources take the form of single words.

When engaging in translanguaging, authors of the analysed “Twitter” messages not only tend to deploy various ready-made English lexical resources common in computer-mediated discourse, but also know how to adapt these items to the specific contexts and content of their postings, in such a way as to satisfy their personal communicational needs. In some instances, users even improvise novel variants of English words to use them in contexts they are not typically related to, as illustrated in example (6):

(6) aš noriu ne **bootycall**, o **beautycall**, kad paskambintu pasakyti kokia aš graži ir dingtų

‘I don’t want a **bootycall**, but a **beautycall**, so that they would call me to tell me how beautiful I am and get lost’

In example (6), the English nouns *bootycall* and *beautycall* both constitute instances of word-length translanguaging. *Bootycall* is defined in MWD
as a communication <...> by which a person arranges a sexual encounter with someone. In this message, bootycall<fnref n="13" /> functions as an adoption of an English vulgar slang term, whereas beautycall demonstrates how an existing lexical item may be creatively repurposed and softened to convey a poster's desire for attention and appreciation of their attractiveness.

In the analysed tweets, English text fragments include not only plain, unmodified English lexical items, but also hybrid words. According to Andrason (2021), Belpoliti and Bermejo (2019, p. 45), Crystal (2005), Gogonas and Maligkoudi (2019), Gutiérrez et al. (1999, p. 288), Mackinney (2022), Nash and Piña (2020), Poza (2017, pp. 107-108), Qi and Li (2022, pp. 1-2), Qi and Zhang (2020, p. 12), and Rubdy and Alsagoff (2013), hybrids are a specific form of language, blending items from two or more separate languages. Such intentional and creative mingling of linguistic components yields dynamic, diverse, and adaptable hybrid forms that language users strategically deploy to produce new sociocultural meanings, actions, and specific purposes, and to communicate identities and voices. Hybrids also contribute to shaping nuance, expressing ideas, creating distinctive pragmatic, semantic, and onomatopoeic effects, managing interpersonal relationships, and enabling inventive language play across different languages. Moreover, hybrids represent innovation and a departure from entrenched language ideologies, contributing to communicative change, as recurrent use may lead some hybrids to become integrated into the vocabulary of a particular language community. Example (7) illustrates a hybrid formation in which an English base is combined with affixes and letters drawn from the Lithuanian language:

(7) man keista kad apskritai atskirą **devaisą** turi navigacijai. Tai jau praktiškai **obsolete** reikalas su **smartphone'ais** kišenėse

‘i find it strange that you have a separate **device** for navigation at all. That’s already practically an **obsolete** thing with **smartphones** in pockets’

In example (7), the English text fragments' 'devaisą', 'obsolete', and 'smarphone'ais are instances of word-length translanguaging, with 'devaisą' and

<footnote n="13">¹³ https://www.merriam-webster.com/dictionary/booty%20call</footnote>
'smarphone'ais constituting English-Lithuanian hybrids. *Devaisą* derives from the English noun *device* (rendered orthographically as *devais*), to which the Lithuanian accusative suffix -ą is appended, producing a morphologically-adapted English-Lithuanian hybrid noun that is phonologically rendered to approximate Lithuanian pronunciation and inflectional patterning. Similarly, the hybrid noun *smartphone*ais is formed by appending the Lithuanian instrumental/plural suffix -ais to the English noun *smartphone*. Of note here is the use of an apostrophe ' as a visual marker clearly denoting the boundary between the English base and the Lithuanian morphological material. Such orthographic and morphological strategies introduce extra cultural nuance, informality, authenticity, character, and emphasis, thereby imitating the conversational style. This illustrates how English and Lithuanian coexist dynamically among users on "Twitter", showcasing the diverse ways they tend to deploy their language skills to blend the resources of both languages.

## 4.2. Phrase-Length translanguaging

The second category focuses on phrase-length translanguaging, which involves short, multi-word English insertions, collocations, or expressions that function together as self-contained units but do not constitute complete sentences, as they typically lack a subject-verb structure. The examples presented in this Subsection illustrate the typical forms and characteristics of these fragments, as well as the communicative trends they reflect within the "Twitter" environment. Example (8) provides a representative instance of phrase-length translanguaging:

(8) Aaaaaaaaa galvojau tipo iš tos serijos.: aš norėčiau būti į ją panaši nes ji turi \*kažkokios **strong independed woman**\* savybes

'Ummmmmm I thought it's, like, a kind of a 'I would like to be like her because she has \*some strong independed woman\* features' deal'

In example (8), the English phrase *strong independed woman* constitutes a case of phrase-length translanguaging. The message author uses this
multi-word English insertion to describe a culturally recognisable
female persona associated with empowerment and autonomy. Since it
exceeds a single lexical item, this English phrase permits more nuanced
identity construction and evaluative commentary than a single-word
insertion would. Furthermore, the non-standard spelling *independed* (for
*independent*<fnref n="14" />) is likely unintentional, yet it reflects characteristics commonly
associated with computer-mediated discourse, such as non-standard
orthography, which is prevalent in digital writing and is considered a
consequence of how users engage with multilingual and informal online
environments. This minor error does not impede comprehension.
Rather, it enhances the authenticity and informality of the message,
emphasising the spontaneous and personal quality of the expression.

Phrase-length translanguaging also includes fixed combinations of
words that are commonly encountered together in groups, appear in
particular contexts, and function as packaged units of meaning. Example
(9) illustrates this phenomenon:

(9) **double tap to wake up** ne tik LG yra įdiegę. O apie **finger print scanner** panašiai galvojau, kol pats nepradėjau naudoti. Gėris.

'double tap to wake up' is not only available on LG. I thought
the same about the **finger print scanner** until I started using it
myself. It's great.'

In example (9), the message author discusses the useful features of
smartphones and names two English technical concepts – *double tap to
wake up* and *finger print scanner*, both of which constitute instances of
phrase-length translanguaging. These two multi-word expressions are
specific to the domain of information technology and rely on English
lexical conventions for technical concepts, suggesting greater user familiarity
with English terminology than with Lithuanian equivalents. This pattern
is consistent with a learning strategy known as chunking, introduced by
cognitive psychologist George A. Miller (1956).

<footnote n="14">¹⁴ https://www.ldoceonline.com/dictionary/independent</footnote>
Chunking is a widely applicable cognitive strategy in language acquisition, facilitating faster comprehension, greater fluency, and reduced error rates. By packaging and storing frequently co-occurring words together into single, salient, and retrievable chunks, language learners acquire and deploy multi-word sequences more effectively than by assembling items one word at a time. By treating technical terms as ready-made, multi-word packages, language users reduce cognitive load and facilitate rapid uptake and reuse of specialised vocabulary in contexts associated with computer-mediated discourse. Consequently, phrase-length translanguaging both reflects and supports the acquisition and online dissemination of domain-specific knowledge, and the presence of compact, ready-to-use technical phrases in example (9) suggests that the author likely acquired and accesses these terms as chunks.

Several English phrases in the corpus reference Internet memes. As shown in example (10), text-based memes often comprise unique, fixed, and formulaic multi-word constructions, such as *big tiddie goth gf*, which here functions as an instance of phrase-length translanguaging:

(10) Kur mano **big tiddie goth gf** kur skaniai valgyt daro

'Where's my **big tiddie goth gf** who makes delicious meals'

In example (10), the message author makes a reference to a variation of the Internet meme, commonly referred to as “Goth GF”<fnref n="15" />, by specifically using the English phrase *big tiddie goth gf*. According to “Know Your Meme”, this meme typically jokes about desiring a gothic-subculture girlfriend and is frequently circulated with accompanying images. However, in example (6), the meme appears in text form only. Such textual deployment demonstrates that effective translanguaging on social media requires not only bilingual competence, but also familiarity with platform-specific conventions and meme culture. The poster’s use of the phrase in a jocular register indicates knowledge of the meme’s form and function despite its absence from standard and established dictionaries, as, for instance, LDOCE, MWD, and CDE do not provide any definitions of *goth gf*.

<footnote n="15">15 https://knowyourmeme.com/memes/goth-gf</footnote>
The concept of *meme* was first introduced in Richard Dawkins' 1976 book "The Selfish Gene". In his work, the distinguished zoologist, ethologist, and evolutionary biologist proposes memes as cultural replicators – units of cultural transmission or imitation (Blackmore, 2000, p. 6). According to Dawkins (1976), memes encompass a wide range of transmissible cultural items, such as ideas, catchphrases, tunes, and fashions, that propagate through imitation. Contemporary scholarship emphasises that memes are particularly prone to user-generated variation (such as parodies, remixes, mashups) and that they typically operate intertextually, referencing and recombining prior items in creative ways (Shifman, 2013, p. 2). From a linguistic perspective, memes are not merely units of imitation, but also carry grammatical and lexical meanings. Furthermore, they are instantiated in language and shaped by morphology, lexis, and syntax, which makes them pertinent objects of linguistic analysis (Driem, 2002, p. 56).

Given these properties, the study of memetic practices intersects naturally with translanguaging, since memes provide ready-made, high-salience multi-word packages that language users can import, adapt, and hybridise across languages. Example (10) thus illustrates how translanguaging on "Twitter" entails mixing of not only lexical resources, but also sociocultural practices and replicable semiotic forms, reinforcing the argument that online multilingualism is as much about shared cultural repertoires as it is about formal linguistic structures.

### 4.3. Sentence-Length translanguaging

The final category comprises sentence-length translanguaging, referring to grammatically complete or nearly complete English clauses or sentences embedded within Lithuanian "Twitter" messages. These segments typically contain a subject and a predicate, may also involve multiple coordinated clauses or short sequences of sentences, and are generally sufficiently long to stand alone. Additionally, they usually express a full thought or idea and carry a clear communicative intent when in context. Drawing on illustrative examples, this Subsection examines how such instances function within "Twitter" discourse, outlines their distinctive features,
and highlights the broader communication patterns they reveal in social media contexts. Example (11) provides an instance of sentence-length translanguaging:

(11) Tas laikas, kai įsisportuoji, ir po treniruočių nieko nebeskauda ir jauties **like you're doing it wrong**.

‘That moment when you’ve worked out and nothing hurts anymore after a workout and you feel **like you’re doing it wrong**.’

In example (11), the English text fragment *like you're doing it wrong* constitutes an instance of sentence-length translanguaging. Such insertions often convey more complex, detailed information than single words or short phrases, allowing language users to articulate fuller stances or interpretations. In this message, the author describes post-exercise experience in Lithuanian but elects to render the evaluative stance (the impression that they're doing it wrong) in English at the end of the tweet, suggesting that English here serves as a resource for expressing a particular evaluative or stance-taking nuance.

In some instances of sentence-length translanguaging, the English segment is markedly longer than the surrounding Lithuanian part of the message. Example (12) illustrates this pattern:

(12) uoj bus kavytės 😂 and gonna Do Many Things i've been thinking about them all morning uwu

‘coffee coming up soo 😂 and gonna Do Many Things i’ve been thinking about them all morning uwu’

In example (12), the English fragment *and gonna Do Many Things i've been thinking about them all morning* constitutes an instance of sentence-length translanguaging. The poster begins this message in Lithuanian, but switches to English after three words, maintaining English for the remainder of the message. The extended use of English here appears to be stylistic or affective. English enables the author to adopt a more
performative or expressive register, framing their productivity plans in a playful and exaggerated tone. The choice to render *Do Many Things* in initial uppercase letters and *i've* in an initial lowercase letter, the arguably superfluous word *them* between *about* and *all*, the use of the *hot beverage*<fnref n="16" /> emoji (新冠疫苗) as a kind of visual boundary to separate the Lithuanian part of the message from the English one, and the inclusion of *uwu*<fnref n="17" />, an emoticon used to depict a cute face that is associated with positive emotional expressivity, all reinforce a casual, Internet-savvy voice, which may be felt as more natural or impactful in English. Taken together, these features suggest that the author is not merely conveying information, but is also deploying English to craft a particular persona and stance toward their day's activities.

Sentence-length translanguaging also includes the deployment of English idiomatic expressions. In example (13), the poster directs their interlocutor toward an implied, arguably risqué meaning that they appear reluctant to state explicitly:

(13) Eik į barą su random žmonėm. Dažniausiai būna afigienai nuobodu bet kartais su laiminga pabaiga **if you know what I mean**

‘Go to a bar with random people. Most often it’s awfully boring but sometimes with a happy ending **if you know what I mean**’

In example (13), the message author signals a double entendre by deploying the English idiomatic expression *if you know what I mean*, which indexes an additional, more suggestive meaning. On the surface, the poster seems to recommend that their interlocutor go to a bar with random people to end the day on a positive note. However, in the pragmatic sense, the idiom implies that such an outing could lead to meeting a partner for a one-night encounter. As noted in MWD<fnref n="18" />, *if you know what I mean* is commonly used to check whether the interlocutor has grasped an intended indirect meaning. The use of such ready-made idiomatic

<footnote n="16">¹⁶ https://emojipedia.org/hot-beverage</footnote>

<footnote n="17">¹⁷ https://www.dictionary.com/e/slang/uwu/</footnote>

<footnote n="18">¹⁸ https://www.merriam-webster.com/dictionary/if%20you%20know%20what%20i%20mean</footnote>
expressions thus enables Lithuanian users of English to convey nuanced, potentially delicate meanings efficiently while attenuating bluntness.

**5. Conclusions and discussion**

This study examined 2,100 Lithuanian “Twitter” messages and identified 2,493 English text fragments, which were grouped into three distinct categories based on their length: word-length (1,459, or 58.5%), **phrase-length** (603, or 24.2%), and sentence-length (431, or 17.3%). This corresponds to approximately 118.7 fragments of English per 100 tweets, or approximately 75.6 English fragments per 1,000 words, highlighting the relative density of English use in this Lithuanian “Twitter” discourse sample.

The predominance of word-length insertions indicates that users most often incorporate English in compact, lexicalised forms rather than extended English discourse. This pattern is plausibly driven by several interrelated factors. First, the affordances of short-form social media, which blend the features of spoken and written language, and favour fluidity, brevity, unhindered communication, and rapid exchanges (Androutsopoulos, 2011; Herring & Androutsopoulos, 2015; and Honeycutt & Herring, 2009). Second, the communicative economy of using ready-made lexical items (such as technical terminology and memes) reduces production and processing effort. Third, the indexical functions of single words, which express stance, affect, or identity. In short, English words appear to function more as stylistic and pragmatic resources in the form of quickly deployable signals of social meaning rather than as vehicles for extended content in this medium.

The analysis revealed that message authors employed English fragments in various ways: adapting individual words to personal preferences, creating English-Lithuanian hybrids, acquiring and reproducing phrases through chunking, incorporating meme-based expressions, embedding English segments that exceeded the length of the surrounding Lithuanian text, and utilising full English utterances as idiomatic expressions. These findings **fit naturally within the translanguaging perspectives** that view multilinguals as effectively and purposefully negotiating and drawing from their whole linguistic repertoire to make meaning rather than
switching between bounded codes (García, 2009, p. 45; García & Wei, 2014, p. 25; Vogel *et al.*, 2018, p. 91; and Wei, 2011, p. 1223). The length-based distribution observed here suggests that, in computer-mediated discourse, translanguaging often takes the form of selective lexical importation, which usually involves individual words or short phrases and serves pragmatic ends, such as indexing youth culture, technological literacy, humour, or affiliation, rather than producing long stretches of discourse in the other language.

At the same time, translanguaging entails more than the blending of languages. It integrates linguistic practices with elements of behaviour, culture, and digital communicative norms. Choosing to communicate in English, even when Lithuanian equivalents are available, reflects not only pragmatic efficiency, but also agency, stylistic creativity, and the desire to infuse nuance, playfulness, or expressive intensity into messages. In this way, the findings align with and extend existing theoretical accounts of translanguaging as an efficient, creative, strategic, and identity-inflected practice of voice and self-expression (Dumrukcic, 2020, pp. 111-112; García & Kano, 2014, p. 261; García & Lin, 2016, p. 117; Moore & Stoelting, 2021, p. 8; and Otheguy *et al.*, 2015).

While prior studies (e.g., Aoyama, 2020, p. 7; Lee & García, 2020, p. 7; Nugrahaeni & Asib, 2022, p. 145; and Sahan & Rose, 2021, p. 10) identify utterance-level translanguaging, word- and sentence-level translanguaging, or word-, phrase-, and clause-level translanguaging, most do not operationalise these levels for written, computer-mediated discourse data. Where comparable, Lee and García's (2020) finding that word-level instances outnumber sentence-level ones is corroborated here. Nevertheless, this study extends that observation by providing explicit operational criteria for length categories, normalising frequencies for cross-study comparability, and situating the analysis in asynchronous social media rather than classroom talk. These methodological contributions make the results directly reusable for future corpus and computer-mediated discourse studies involving translanguaging.

Speaking of practical and sociolinguistic implications, the predominance of short English insertions suggests that analyses that track only clause-, sentence-, or utterance-level translanguaging will substantially underestimate the presence and functions of short-form translanguaging on digital
platforms (as seen in Sahan and Rose, 2021). For educators, lexicographers, and language technology developers, recognising short-form translanguaging as functional (not merely ‘errors’ or noise) is important for assessment, dictionary coverage, and natural language processing tools that handle mixed-language content. This aligns with translanguaging scholarship that supports treatment of short-form items as legitimate entries rather than mere noise (Amari, 2010, p. 9; Creese & Blackledge, 2010, p. 112; Herring, 2001, pp. 616-617; Solorio & Liu, 2008, p. 1055).

This study has limitations, however. It is based on a single corpus of Lithuanian “Twitter” messages containing English text fragments and does not include any insights into language user demographics or interactional metadata. Corpus annotations were manual and based on contextual judgment, which may limit replicability across different coders or platforms. The dataset is cross-sectional rather than longitudinal, so inferences about changes in translanguaging practices over time are not supported.

To build on these findings, recommendations for future research directions involve these concrete avenues: cross-platform comparisons (e.g., across “Instagram”, “Facebook”, and “TikTok”, among others) to test whether length distributions differ by medium. Incorporation of sociodemographic metadata and user network analysis to examine who uses which fragment types and in what contexts. Development of automated detection methods to scale annotation and enable large-scale frequency or sequence analyses. Mixed-methods studies combining corpus analysis with interviews or interactional analysis to link fragment use to conscious identity work and pragmatic intent. Experimental or comprehension studies to test how short versus long English insertions affect message interpretation among different audiences. In addition to going beyond the educational context, involving more than spoken language, and considering more diverse language combinations, more emphasis should be put on the exploration of the relationship between translanguaging and identity formation (Schreiber, 2015), creativity (Wei, 2016), translanguaging space (Hua et al., 2017), and translanguaging among peers (Kolu, 2018).

To conclude, by operationalising a length-based typology adapted to computer-mediated discourse, this study contributes both a practical tool for corpus analysis and new empirical evidence on how English
functions as a creative, concise, and socially meaningful resource in
Lithuanian “Twitter” discourse.

REFERENCES

Aarts, B. (2011). *Oxford modern English grammar*. Oxford University Press.

Amari, J. (2010). *Slang lexicography and the problem of defining slang*. https://ora.ox.ac.uk/objects/uuid:fd2d4042-0f65-4037-bfd3-230c6193bc1d/files/m00726697418d64b3e050d8f6c0e38f8e.

Andrason, A. (2021). Сашко-lect: The translanguaged grammar of a hyper multilingual global nomad. Part 2 – contact mechanisms. *Studia Linguistica Universitatis Iagellonicae Cracoviensis*, 138(1), 7–24. doi:10.4467/20834624SL.21.002.13279.

Androutsopoulos, J.K. (2011). From variation to heteroglossia in the study of computer-mediated discourse. In C. Thurlow & K. Mroczek (Eds.), *Digital discourse: language in the new media* (pp. 277–298). Oxford University Press. http://doi.org/10.1093/acprof:oso/9780199795437.003.0013.

Aoyama, R. (2020). Exploring Japanese high school students' L1 use in translanguaging in the communicative EFL classroom. *The Electronic Journal for English as a Second Language, 23*(4), 1–18. https://tesl-ej.org/wordpress/issues/volume23/ej92/ej92a12.

Auer, P. (2010). Code-switching/mixing. In R. Wodak, B. Johnstone, & P.E. Kerswill (Eds.), *The SAGE handbook of sociolinguistics* (pp. 460–478). SAGE Publishing.

Baker, C. (2001). *Foundations of bilingual education and bilingualism*. Multilingual Matters.

Becker, A.L. (1988). Language in particular: A lecture. In D. Tannen (Ed.), *Linguistics in context: Connecting observation and understanding: Lectures from the 1985 LSA/TESOL and NEH Institutes (Advances in discourse processes, Vol. 29)* (pp. 17–35). Ablex.

Belpoliti, F., & Bermejo, E. (2019). *Spanish heritage learners' emerging literacy: Empirical research and classroom practice*. Routledge.
Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (1999). *Longman grammar of spoken and written English*. Longman.

Blackmore, S. (2000). *The meme machine*. Oxford University Press.

Bryman, A. (2004). *Social research methods*. Oxford University Press.

Creese, A. (2017). Translanguaging as an everyday practice. In B. Paulsrud, J. Rosen, B. Straszer, & A. Wedin. (Eds.), *New perspectives on translanguaging and education* (pp. 1–9). Multilingual Matters. https://doi.org/10.21832/9781783097821-002.

Creese, A., & Blackledge, A. (2010). Translanguaging in the bilingual classroom: A pedagogy for learning and teaching? *The Modern Language Journal*, 94(1), 103–115. https://doi.org/10.1111/j.1540-4781.2009.00986.x.

Crystal, D. (1987). *The Cambridge encyclopedia of language*. Cambridge University Press.

Crystal, D. (2005). *The stories of English*. Penguin Books.

Cummins, J. (1981). The role of primary language development in promoting educational success for language minority students. In California State Department of Education (Ed.), *Schooling and language minority students: A theoretical framework* (pp. 3–49). California State University. http://dx.doi.org/10.13140/2.1.1334.9449.

Cummins, J. (2000). *Language, power and pedagogy: Bilingual children in the crossfire*. Multilingual Matters.

Dawkins, R. (1976). *The selfish gene*. Oxford University Press.

Driem, G. van. (2002). *Languages of the Himalayas: An ethnolinguistic handbook of the greater Himalayan region, containing an introduction to the symbiotic theory of language*. Brill.

Dumrukcic, N. (2020). Translanguaging in social media. Output for FLT didactics. *heiEDUCATION Journal*, 5, 109-137. https://doi.org/10.17885/heiup.heied.2020.5.24159.

Gafaranga, J. (2007). Code-switching as a conversational strategy. In P. Auer, & L. Wei (Eds.), *Handbook of multilingualism and multilingual communication* (pp. 279–313). de Gruyter. https://doi.org/10.1515/9783110198553.3.279.García, O. (2009). *Bilingual education in the 21st century: A global perspective*. WileyBlackwell.

García, O. (2009). *Bilingual education in the 21st century: A global perspective*. WileyBlackwell.
García, O., & Kano, N. (2014). Translanguaging as process and pedagogy: Developing the English writing of Japanese students in the US. In J. Conteh, & G. Meier (Eds.), *The multilingual turn in language education: Opportunities and challenges* (pp. 292-299). Multilingual Matters. https://doi.org/10.21832/9781783092246-018.

García, O., & Wei, L. (2014). *Translanguaging: Language, bilingualism and education*. Palgrave Macmillan Pivot.

García, O., & Lin, A.M.Y. (2016). Translanguaging in bilingual education. In O. García, A.M.Y. Lin, & S. May (Eds.), *Bilingual and multilingual education (Encyclopedia of language and education)* (pp. 117–130). Springer. http://doi.org/10.1007/978-3-319-02258-1_9.

Geben, K., & Zielińska, M. (2021). Translanguaging in Polish minority schools in Ukraine and Lithuania. *Zeitschrift für Slawistik*, 66(2), 229-248. http://doi.org/10.1515/slaw-2021-0011.

Gogonas, N., & Maligkoudi, C. (2019). Translanguaging instances in the Greek linguistic landscape in times of crisis. *Journal of Applied Linguistics*, 32, 66–82. https://doi.org/10.26262/jal.v0i32.7528.

Grosjean, F. (1982). *Life with two languages: An introduction to bilingualism*. Harvard University Press.

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. *Field Methods*, 18(1), 59-82. https://doi.org/10.1177/1525822X05279903.

Gutiérrez, K.D., Baquedano-López, P., & Tejeda, C. (1999). Rethinking diversity: Hybridity and hybrid language practices in the third space. *Mind, Culture, and Activity*, 6(4), 286–303. http://doi.org/10.1080/10749039090524733.

Heller, M. (2007). Bilingualism as ideology and practice. In M. Heller (Ed.), *Bilingualism: A social approach* (pp. 1-22). Palgrave Macmillan. https://doi.org/10.1057/9780230596047_1.

Herring, S.C. (2001). Computer-mediated discourse. In D. Schiffrin, D. Tannen, & H.E. Hamilton (Eds.), *The handbook of discourse analysis* (pp. 612–634). Blackwell Publishers. https://doi.org/10.1002/9780470753460.ch32.

Herring, S.C., & Androutsopoulos, J.K. (2015). Computer-mediated discourse 2.0. In D. Tannen, H.E. Hamilton, & D. Schiffrin (Eds.), *The handbook of discourse analysis* (pp. 127–151). Wiley-Blackwell. https://doi.org/10.1002/9781118584194.ch6.
Honeycutt, C., & Herring, S.C. (2009). Beyond microblogging: conversation and collaboration via Twitter. *Proceedings of the forty-second Hawai'i international conference on system sciences*, 1–10. IEEE Press. http://doi.org/10.1109/HICSS.2009.89.

Hornberger, N. (2003). *Continua of biliteracy: An ecological framework for educational policy, research, and practice in multilingual settings*. Multilingual Matters.

Hua, Z., Wei, L., & Lyons, A. (2017). Polish shop(ping) as translanguaging space. *Social Semiotics*, 27(4), 411-433. https://doi.org/10.1080/10350330.2017.1334390.

Jacobson, R., & Faltis, C. (1990). *Language distribution issues in bilingual schooling*. Multilingual Matters.

Kolu, J.K. (2018). Translanguaging practices in bilingual adolescents' conversations in Haparanda, Stockholm and Helsinki. *Nordiques*, 35, 135–153. https://doi.org/10.4000/nordiques.1016.

Lee, C., & García, O. (2020). Unpacking the oral translanguaging practices of Korean-American first graders. *Bilingual Research Journal*, 43, 32–49. https://doi.org/10.1080/15235882.2019.1703844.

Lewis, G., Jones, B., & Baker, C. (2012). Translanguaging: Developing its conceptualisation and contextualisation. *Educational Research and Evaluation*, 18(7), 655–670. https://doi.org/10.1080/13803611.2012.718490.

Mačianskienė, N., & Bijeikienė, V. (2021). Perspectives of linguistically sensitive teaching as a component of inclusive classrooms in Lithuanian general education. *Pedagogika / Pedagogy*, 142(2), 89-104. https://doi.org/10.15823/p.2021.142.5.

Mackinney, E. (2022). "It's how we get along" – translanguaging in middle-school mathematics class. *The Electronic Journal for English as a Second Language (TESL-EJ)*, 26(3), 1-16. https://doi.org/10.55593/ej.26103a8.

MacSwan, J. (2000). The threshold hypothesis, semilingualism, and other contributions to a deficit view of linguistic minorities. *Hispanic Journal of Behavioral Sciences, 20*(1), 3–45. https://doi.org/10.1177/0739986300221001.

MacSwan, J. (2017). A multilingual perspective on translanguaging. *American Educational Research Journal*, 54(1), 167–201. https://doi.org/10.3102/0002831216683935.

Makoni, S., & Pennycook, A. (2007). *Disinventing and reconstituting languages*. Multilingual Matters.
May, S. (2013). *The multilingual turn: Implications for SLA, TESOL, and bilingual education*. Routledge.

Meškauskienė, A., Pundziuvienė, D., Matulionienė, J., & Ringailienė, T. (2021). The role of linguistic and cultural mediation in learning the host country's language. In A. Daukšaitė-Kolpakovienė & Ž. Tamašauskaitė (Eds.), *Sustainable multilingualism 2021: 6th international conference, June 4-5, 2021: Book of abstracts* (p. 117). Conference paper. Vytautas Magnus University.

Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. *Psychological Review*, 63(2), 81–97. https://doi.org/10.1037/h0043158.

Moore, P., & Stoelting, S.L. (2021). My favorite subject is lengua because the teacher es un crack: Translanguaging in CLIL student writing. *CLIL Journal of Innovation and Research in Plurilingual and Pluricultural Education*, 4(1), 7–18. http://doi.org/10.5565/rev/clil.49.

Nash, K.T., & Piña, I.P. (2020). Translanguaging pedagogies in a bilingual preschool classroom. In K.T. Nash, C.P. Glover, & B. Polson (Eds.), *Toward culturally sustaining teaching: Early childhood educators honor children with practices for equity and change* (pp. 35–61). Routledge. http://doi.org/10.4324/9781351108317-2.

Nugrahaeni, R.P., & Asib, A. (2022). The role of L1 in English language acquisition using translanguaging pedagogy of Indonesian EFL students. *English Education Journal, 10*(3), 138–150. https://doi.org/10.20961/ee.v10i3.60357.

Otheguy, R., García, O., & Reid, W. (2015). Clarifying translanguaging and deconstructing named languages: A perspective from linguistics. *Applied Linguistics Review*, 6(3), 281–307. https://doi.org/10.1515/applirev-2015-0014.

Pinker, S. (1994). *The language instinct: How the mind creates language*. W. Morrow.

Plag, I., Arndt-Lappe, S., Braun, M., & Schramm, M. (2015). *Introduction to English linguistics*. Walter de Gruyter GmbH.

Poza, L. (2017). Translanguaging: Definitions, implications, and further needs in burgeoning inquiry. *Berkeley Review of Education, 6*, 101–128. http://doi.org/10.5070/B86110060.
Qi, F., & Zhang, K. (2020). Translanguaging hybrids on Chinese gateway websites. *Asian Englishes*, 23(2), 1-14. https://doi.org/10.1080/13488678.2020.1743913.

Qi, F., & Li, J. (2022). Chinese university students' translanguaging hybrids on WeChat: Creativity nurtured language play in the context of a Chinese digital social media. *English Today*, 39(4), 1-10. http://doi.org/10.1017/S0266078422000256.

Rubdy, R., & Alsagoff, L. (2013). The cultural dynamics of globalization: Problematizing hybridity. In R. Rubdy & L. Alsagoff (Eds.), *The global-local interface, language choice and hybridity* (pp. 1-14). Multilingual Matters. http://doi.org/10.21832/9781783090860-002.

Rymes, B., & Smail, G. (2020). Citizen sociolinguists scaling back. *Applied Linguistics Review*, 12(3), 419-444. https://doi.org/10.1515/applirev-2019-0133.

Sahan, K., & Rose, H. (2021). Translanguaging or code-switching? Re-examining the functions of language in EMI classrooms. In B. Di Sabato & B. Hughes (Eds.), *Multilingual perspectives from Europe and beyond on language policy and practice* (pp. 348-356). Routledge. http://doi.org/10.4324/9780429351075-6.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. *Quality & Quantity*, 52, 1893-1907. https://doi.org/10.1007/s11135-017-0574-8.

Schreiber, B.R. (2015). "I am what I am": Multilingual identity and digital translanguaging. *Language Learning & Technology*, 19(3), 69-87. http://doi.org/10125/44434.

Shifman, L. (2013). *Memes in digital culture*. The MIT Press.

Solorio, T., & Liu, Y. (2008). Part-of-speech tagging for English-Spanish code-switched text. *Proceedings of the 2008 conference on empirical methods in natural language processing*, 1051-1060. https://aclanthology.org/D08-1110.pdf.

Swain, M. (2006). Languaging, agency and collaboration in advanced second language learning. In H. Byrnes (Ed.), *Advanced language learning: The contributions of Halliday and Vygotsky* (pp. 95-108). Continuum, Bloomsbury Publishing.
Vogel, S., & García, O. (2017). Translanguaging. In G.W. Noblit (Ed.), *Oxford research encyclopedia of education*. Oxford University Press. https://doi.org/10.1093/acrefore/9780190264093.013.181.

Vogel, S., Ascenzi-Moreno, L., & García, O. (2018). An expanded view of translanguaging: Leveraging the dynamic interactions between a young multilingual writer and machine translation software. In J. Choi & S. Ollerhead (Eds.), *Plurilingualism in teaching and learning: Complexities across contexts* (pp. 89–106). Routledge. https://academi cworks.cuny.edu/gc_pubs/435/.

Wei, L. (2011). Moment analysis and translanguaging space: Discursive construction of identities by multilingual Chinese youth in Britain. *Journal of Pragmatics*, 43(5), 1222–1235. https://doi.org/10.1016/j.pr agma.2010.07.035.

Wei, L. (2016). New Chinglish and the post-multilingualism challenge: Translanguaging ELF in China. *Journal of English as a Lingua Franca*, 5(1), 1–25. https://doi.org/10.1515/jelf-2016-0001.

Wei, L. (2018). Translanguaging as a practical theory of language. *Applied Linguistics*, 39(1), 9–30. https://doi.org/10.1093/applin/amx039.

Wei, L., & Ho, W.Y.J. (2018). Language learning sans frontiers: A translanguaging view. *Annual Review of Applied Linguistics*, 38, 33–59. https://doi.org/10.1017/S0267190518000053.

Williams, C. (1994). *Arfarniad o ddulliau dysgu ac addysgu yng nghyd-destun addysg uwchradd ddwyieithog / An evaluation of teaching and learning methods in the context of bilingual secondary education*. Unpublished PhD thesis. University of Wales Bangor.

Williams, C. (2002). *Extending bilingualism in the education system. Education and lifelong learning committee report (ELL 06-02(p.4))*. National Assembly for Wales: Education and Lifelong Learning Committee. https://web.archive.org/web/20161220052821/http://www.assembly.wales/Committee%20Documents/ELL%2006-02(p.4)%20Dr%20Cen%20Williams%20paper-20032002-28970/3c91c7af00023d82000059500000000-English.pdf.

Zhang, L. (2018). *The relationship between online translanguaging practices and Chinese teenagers’ self-identities*. PhD thesis. University of Leeds. Identification number/EthosID: uk.bl.ethos.762534. https://ethese s.whiterose.ac.uk/22538/1/Thesis_LZ.pdf.
Zhang, Y., & Ren, W. (2020). ‘This is so skrrrrr’ – creative translanguaging by Chinese micro-blogging users. *International Journal of Multilingualism*, 19(3), 289–304. https://doi.org/10.1080/14790718.2020.1753746.

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