The Strategies Used in Translating the F*-word into Arabic A Comparative Case Study between Human & Google Translation

1.Introduction

Language is a part of the cultural and religious fabric of any society, so linguistic expressions often carry socio-cultural meanings that surpass their literal content. Mark Manson's book "The Subtle Art of Not Giving a F*ck" is characterized by its frequent use of the F*word and its derivatives to convey certain attitudes. The translations examined include a human translation by Al-Hareth Al-Nabhan and a machine translation by Google Translate. Al-Hareth Al-Nabhan is known for his skill in exploring the cultural contexts required for such a sensitive translation. The cultural

and religious norms in Arabic-speaking countries significantly influence the acceptance and interpretation of taboo words. This research paper focused on the strategies implemented by translators to translate the English F*word and its derivatives into Arabic. The goal is to examine the degree of similarity between human and machine translation in terms of cultural appropriateness. This study aims not only to assess the performance of machine translation in handling taboo language but also to highlight the inseparable role of human translators in contexts requiring cultural mediation. The study applies two theoretical frame works, Chesterman’s (1997, 2000) model and Baker (1992) taxonomy to evaluate each translation method in handling linguistic and cultural elements, which are particularly challenging with taboo language. It explores fifty- seven instances of the F*word in both translations. The study is significant because it highlights the limitations of machine translation (MT) in dealing with F* word, contrasting it with the nuanced approach of human translation (HT).

1.1 Taboos

A taboo is a word or an action that is not accepted for cultural aspects (Cambridge University Press,2020). In (Britannica,2020) a taboo means prohibiting an action because it is too holy for the people in a certain community. Taboos are specified by the rules of the community, which are agreed-upon customs and rules, people’s attitudes, and behavior are affected by these taboos (Fershtman et al., 2011). Taboos differ from one country to the other and from one society to the other. Hence, Gobert (2014) for instance has confirmed that a taboo word differs according to their classifications according to a specific country, culture, and even religion. Moreover, Gobert (2014) investigated the use of taboo issues in Arab schools. In Arab schools, taboo topics, mainly those related to sexuality, politics, or religion, are often either excluded from the topics or addressed through abstract language. In the Arab world and the Middle East, many topics can be considered taboo as they are related to the culture and the norms of society. For instance, Gao (2013) has classified dating as taboo because of the cultural norms. In Islamic societies, relationships between men and women are guided by strict moral guidelines to preserve religious virtue. Romantic relationships, especially those involving physical intimacy, are often viewed as morally unacceptable. Saad (2017) revealed that taboo topics are not limited to sex, disease or abilities. The translation of taboo words from English into other languages has been studied by many researchers. Pratama (2016) and Isbuga-Erel (2007) analyzed how translators in Indonesian and Turkish cultures use strategies such as euphemism and omission to translate culturally sensitive expressions. In the Arabic context, Abbas (2015) and Al-Yasin and Rabab’ah (2019) have explained the role of religion and social norms in audiovisual texts. Almijrab (2020) goes deep in this discussion by examining subtitling practices in Arabic media, while Debbas and Haider (2020) investigated the subtitling of Western TV shows, noting frequent use of cultural substitution. Although these studies have focused on general translation practices, few have investigated how literary translations, especially those translated by human versus machine, differ in addressing the taboo expressions. This study addresses that gap by applying a comparative analysis of a literary text translated both by a human translator and by Google Translate, with a particular focus on the F*word and its derivatives.

1.2 The F* Word                                                                                                                                                                                                                            

O’Connor (2000) argued that the F*word appeared in the 1400s as this word might have occurred before without documentation because it was considered a vulgar word that became unprintable after the invention of printing. It had not been used in dictionaries for approximately 170 years, before reappearing in the 1965 Penguin Dictionary. However, Gao (2013) stated that the F*word appeared in Eric Partridge’s 1963 Dictionary of Slang and Unconventional English, a case that had been objected from schools and libraries. It was not written fully. Partridge used an asterisk for the vowel (u). The meaning of the word occasionally evolves from the context, but not always. For instance, O’Connor (2000) argued the F*word sometimes has a happy meaning and sometimes is a vague one, a case that can confuse the reader or listener as to whether to interpret the intended meaning as good, bad, happy, or sad. O’Connor (2000) also mentioned that the use of the F*word is increasing as an expression of anger, surprise, or frustration, a meaningless modifier; and an adjective for emphasis. Although the F*word is still mentioned as a taboo word and is prohibited by the majority of the middle-class people, it is one of the 3,000 most frequently spoken used words, while the word f*cking is among the 1,000 most frequently spoken words (Hughes, 2006). The use of the F* word is not limited to movies and television; it also invades books, and the F*word explicitly has started to appear as book titles. The corpus of this paper, The Subtle Art of Not-Giving a F*ck, is not the only book with the word on the title and cover page. Several other books contain the F*word on the cover page, including Everything is F*cked by Manson in 2019; Unf*ckology by Amy Alkon in 2018; F *You Very Much by Danny Wallace in 2018; Un fu*k Yourself by Gary John Bishop in 2017; What I Mean When I Say Miss You, Love You and F*ck You by Robert Drake in 2019. 

1.3 The Translation of F*word  

Although few studies have focused on the translation of the F*word, Pujol (2006) studied the translation of the F*word into Catalan, including its compounds and derivations. Pujol’s (2006) analysis of using this word in a movie indicated that was used to convey emotions such as extreme anger, emphasis, disgust, contempt, surprise, and happiness; however, he argued that these categories are not ultimate and occasionally overlap. Santaemilia (2009) investigated the translation of the F*word as a sex-related term into Spanish and Catalan. Santaemilia (2009) discussed various options for dealing with sensitive and taboo language other than public censorship, including self-censorship; individual ethics; and one’s attitudes towards religion, impoliteness and sex. There are some studies that have focused on translating taboos into Arabic, such as Almijrab (2020), Al-Yasin and Rabab’ah (2019), and Debbas and Haider (2020), but no study has focused mainly on the F*word translation into Arabic and on the strategies used to translate it. In this paper, my focus is mainly on how a selected F*word is translated and the strategies used to present this word in the target text.

2. Literature Review

2.1 Developments in Machine Translation (MT)

Machine Translation (MT) is a rapidly evolving field in computational linguistics and Artificial Intelligence (AI) that focused on translating text from one language to another without human interference. While (MT) systems such as Google Translate have significantly advanced due to the implementation of Neural Machine Translation (NMT) and transformer-based architectures (Bahdanau et al., 2014; Vaswani et al., 2017), their translation in culturally nuanced content as taboo remains a challenge. (MT) is dominate in technical fields but fails in translating idiomatic expressions, context-specific phrases, and culturally embedded language (Castilho et al., 2018). Toury (1995) emphasized that taboo language presents a significant problem for (MT) due to its reliance on connotative meaning, emotional tone, and socio-cultural acceptability. Many people might think that replacing human translators with (MT) is not the ultimate goal. On the other hand, as Koehn (2020) explained that the main aim for (MT) is to serve as a tool to enhance the human translation. Recent advancements in MT, mainly transformer-based architectures like BERT and GPT (Vaswani et al., 2017; Brown et al., 2020), are acknowledged for their improvements in fluency but still fall short in culturally complex translations as stated in Koehn’s research paper (2020) and a recent benchmarking study by Castilho et al. (2022) which highlight the gaps in contextual awareness, mainly in Arabic. Recent developments in Google Translate rely heavily on Neural Machine Translation (NMT), which uses transformer-based architectures to model the whole sequences of text through attention mechanisms (Vaswani et al., 2017). The transformer permits the model to weigh the relevance of each word in a sentence in relation to all other words. This can lead to improvements in fluency and grammatical cohesion. Although there are architectural advancements, (NMT) systems still limited in their capacity to interpret pragmatic points such as tone, intention, and socio-cultural appropriateness. This is mainly because the models are trained on large corpora where taboo usage is underrepresented. As a result, systems like Google Translate may produce output that is lexically fluent but semantically inappropriate when dealing with culturally sensitive texts. This limitation is evident in the translation of taboo terms like the F*word, where meaning relies on context and cultural understanding, areas that current (NMT) systems lack. These findings align with the present study’s results and affirm the need for culturally embedded models.

2.2 Human Translation vs. Machine Translation of Taboo Language

The main difference between (MT) and (HT) lies in the ability of the translator to apply culturally appropriate language. (HT) relies on pragmatic strategies to deal with taboo language, such as euphemism, omission, reformulation, and cultural substitution (Baker, 1992; Chesterman, 1997). These strategies give humans the chance to preserve the functional equivalence of the message while aligning with the socio-cultural standards of the target reader.Many studies, such Ávila-Cabrera (2016) and Hendal (2020), have demonstrated that human translators sometimes avoid direct translations of taboo phrases. They may choose culturally acceptable alternatives that deliver the intended meaning. On the other hand, (MT) systems depend on direct or transliterated texts, which might lead to inappropriate or offensive translations.

2.3 Language, Culture, and the Translation of Taboo Language

Language and culture cannot be separated from each other. According to Newmark (1988), culture is divided into: universal culture, which can be translated by machines, and specific culture, which needs human interference. Translating from Arabic to English and vice versa is problematic as both languages are different especially in the fields of religion and public discourse. This can present complex translation challenges. For instance, Arabic is a language that is characterized by moral standards, which strongly restrict the public use of sexual language in public domains (Al-Yasin & Rabab’ah, 2019). So, the translation of English taboo terms like the F*word sometimes needs specific cultural adaptation. A literal translation would not only be culturally unacceptable but also semantically misleading. Instead, Arab translators may choose to substitute such terms with euphemistic expressions (e.g., “عليك اللعنة” [curse upon you]) or omit them altogether if they do not add substantive meaning (Gottlieb, 1992).

2.4 Machine Limitations in Handling Cultural and Pragmatic Context

While (MT) have developed through deep learning, they still face challenges with metaphorical meaning. (MT) cannot distinguish between literal and metaphorical meaning. As noted by Hatim and Mason (1997), translation is not a linguistic act but it is communicatively added to context and culture.Google Translate, for instance, is famous on transliterating offensive terms rather than translating them. The translation produced is often a phrase that is linguistically accurate but culturally or semantically incorrect (Almijrab, 2020) unless there is access to annotated corpora that are rich in cultural and pragmatic context, mainly for under-resourced languages such as Arabic, machine translation systems have a limited ability to translate taboo content effectively.

3. Research Questions

The main objective of this study is to compare the accuracy and cultural appropriateness of (HT) vs. (MT) (specifically Google Translate) in translating F* word by identifying the strategies used by human translators to adapt offensive language in Arabic. This aim helps in formulating the research questions as follows:

1-To what extent does Google Translate accurately convey the semantic and pragmatic meanings of the F*word and its derivatives in Arabic?

 2-What are the strategies that human translators use for F* word translation?

 3-To what extent are the translations provided by humans and machine similar in terms of appropriateness and semantic accuracy?

4. Methodology

This study adopts mixed approach by combining qualitative analysis of the translation strategies and quantitative approach to categorize their frequency. The corpus consists of 57 F*word and its derivatives taken from The Subtle Art of Not Giving a Fck*: A Counterintuitive Approach to Living a Good Life. The F* word was translated into Arabic by Google Translate (MT) and compared to the translation published by AL-Hareth Al-Nabhan (HT). To analyze the translation strategies used, this study adopted two theoretical models: Baker’s (1992) taxonomy of translation strategies for dealing with non-equivalence at the word level, and Chesterman’s (1997, 2000) model, mainly his pragmatic strategies which can help in analyzing how translators preserve communicative function beyond lexical meaning.  Baker’s (1992) taxonomy is used to classify translation strategies at the lexical level, including paraphrasing, omission, and cultural substitution. Pragmatic strategies like cultural filtering, which means omitting or replacing culturally sensitive content with something more appropriate in the target culture; reformulation, which means rephrasing the original word while preserving the overall meaning, and illocutionary change that refers to changing a command to a suggestion or criticism to a milder observation to go with cultural expectations. Each translated F*word and its derivatives was coded based on both frameworks. These adopted models are particularly relevant when translating taboo words as they help to explain why human translators adjust or omit taboo words to maintain cultural appropriateness, something machine translation struggles to do.  For example, translating "Who f*cked whom?" as "تبادل الاتهامات" (exchange of accusations) reflects a reformulation strategy that avoids offensive language while translating the core meaning. In short, Baker’s model focuses on lexical and semantic choices as paraphrase, neutralization, generalization whereas Chesterman’s model focuses on pragmatic shifts as illocutionary change, cultural filtering, reformulation.

Although the researcher initially coded the data, a second linguist also reviewed the classifications to ensure consistency. Instead of using statistical tools like Cohen’s Kappa, agreement between the two coders was reached through discussion. Whenever there were differences in how a translation was categorized, especially in cases where a phrase could fit more than one strategy, the coders talked through the context and meaning to decide on the most accurate classification. This process helped ensure that the results were reliable and carefully considered.Quantitative data were generated by counting the frequency of each strategy across the 57 derivatives. Qualitative examples were then selected to illustrate key patterns, discrepancies, and implications for semantic accuracy and cultural appropriateness. Strategies such as functional substitution, reformulation, and the use of unrelated but contextually appropriate words are better explained in Chesterman’s pragmatic equivalence model. Baker’s strategies provide a concrete framework for assessing lexical-level adaptation. The model facilitates the analysis of how meaning and tone, especially in taboo context, are preserved or lost in translation. For the purposes of this study, the term 'paraphrasing using related words' refers to semantically lexical substitutions that keeps the denotative meaning of the source term. As for 'functional or cultural substitution' they denote a shift in lexical field to keep the communicative or cultural appropriateness. Each F*word was classified according to both frameworks to determine whether the translation achieved semantic, pragmatic, or cultural equivalence. This dual-framework allows a more comprehensive analysis.

4.1 Data Analysis

The analysis proceeded in two phases:

• Phase 1: Strategy Identification

Each F*word instance was categorized according to both Baker's and Chesterman's frameworks:

Baker strategies: paraphrasing with related words, omission, cultural substitution, literal translation

Chesterman strategies: functional substitution, reformulation, and pragmatic paraphrasing (i.e., using unrelated but contextually appropriate expressions)

Each F*word was analyzed in terms of the translation strategy employed by both (HT) and (MT).

• Phase2: ComparativeEvaluation

The outputs from (HT) and (MT) were then compared using Chesterman’s comparative model to evaluate:

• Equivalence: Whether ST ≈ MT/HT in terms of meaning, tone, and pragmatic function.

• Cultural Sensitivity: Whether the translation respects sociolinguistic norms in Arabic.
• Accuracy: Whether the translation conveys the intended communicative function.

This dual-framework approach acknowledges that the taboo language is not purely a lexical issue but is mainly embedded in socio-pragmatic and cultural contexts. It is important to note that the boundaries between paraphrasing using related words and functional substitution are not always clear. Certain examples may fall into both categories depending on whether one emphasizes semantic accuracy or pragmatic effect. In this study, classification was guided by whether the translated term shared a lexical meaning or only served a functional role.

4.2 Rationale for Tool Selection                                                                                                                                                                                                                                                                                      G   Google Translate was chosen as the (MT) system due to its widespread usage, accessibility, and integration with artificial intelligence and neural machine translation (NMT) techniques. Al-Hareth Al-Nabhan’s Arabic translation was selected due to its recognition and publication as a formal human translation of the source text.

4.3 Limitations                                                                                                                                                                                                                                                                           i This study focuses on a one lexical item (the F* and its derivatives) within a specific genre. Although this allows an in-depth analysis, it can limit generalizability to other taboo words or genres.

4.4TheSample                                                                                                                                                                                                                                                                                                                                                                 The sample is explored by the usage of three versions of “The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good Life”. It is a non-fiction book which is characterized by the frequent use of taboo language, particularly the F*word and its variations. Three parallel texts were used:

1-The original English version (ST)

2-Human Translation (HT)

3-Machine Translation (MT)

The first is the source text (ST) in English. The second text is the translated text (HT) which represents the human translation into Arabic (by Al-Hareth Al-Nabhan). The third text is the machine translation text (MT), by Google Translate. The book frequently uses the F*word to emphasize points and deliver certain attitudes. The main focus is mainly on the F*word and its derivatives. Although the text includes many taboo words, I removed all other words that did not contain this specific word, F*ck. The researcher used fifty-seven F*word that have been tabulated in Table 1 to show the difference between human and machine translation for each one of them.

5. Results &Findings

This section presents the findings of the comparative analysis between human translation (HT) and machine translation (MT) of 57 instances of the F*word and its derivatives from The Subtle Art of Not Giving a Fck*. The analysis focuses on translation strategies, cultural appropriateness, and semantic accuracy. Each result is categorized and tabulated according to the primary strategy used.

5.1 Strategies used by Human Translators

The researcher applied two frame works: Baker’s taxonomy (1992) and Chesterman’s (1997, 2000) model. This paper compares the results obtained from Google Translate to the human translation presented by Al-Hareth Al-Nabhan to examine the differences between machine translation and human translation. The most frequently used techniques used by human translators were omission, literal translation, and reformulation. As for Google Translate, it does not use such techniques in its translation. (MT) has its own annotated sentences that can help in the translation process and that is why various problems and challenges appeared in the (MT) translated words.

5.1.A Paraphrasing Using Related Words (Baker’s taxonomy)

One of the key strategies identified in the human translation of the F-word is paraphrasing using a related word. Baker (1992) mentioned that this strategy is used when a word in the source text (ST) has no direct equivalent in the target language (TL), and the translator uses a semantically related expression that conveys a similar meaning. In this study, the F*word, especially in the idiom "to give a f*ck", is rarely translated literally. On the other hand, the human translator (HT) substitutes it with Arabic expressions such as اهتمام (care), أهمية (importance), or يهتم (to care), which effectively convey the intended pragmatic function of concern without violating cultural norms.

This strategy is especially relevant when dealing with phrases like “give a f*ck,” “have given a f*ck,” or “no f*cks given,” which, in English, express attitudes of concern, indifference. The human translation reveals a consistent pattern of functional equivalence, whereby the informal tone is neutralized, and the intended meaning is retained in a culturally acceptable way.

This approach is distinct from literal translation, which often fails in this context, and from paraphrasing using unrelated words, where the translator may use a more interpretative strategy to deliver a broader propositional meaning rather than a close lexical match.

Machine translation (MT), as exemplified by Google Translate, does not adequately address this paraphrasing strategy. On the contrary, it frequently relies on literal translations such as اللعنة (curse), نكاح (intercourse), or الملاعين (the damned), which either distort the meaning or produce culturally inappropriate translation. The human translator’s use of related expressions preserves the pragmatic function and emotional tone of the original, while aligning with the sociocultural norms of Arabic discourse.

Examples in Table 1 (phrases 1–6, 7–16, and 17–24) illustrate this strategy. In these cases, the employment of this strategy highlights the translator’s ability to adapt offensive language in a culturally sensitive manner, a task at which (MT) continues to fail in.

• لم يكن بوكافسكي مباليا (“Bukowski did not care”)
• المبالغة في الاهتمام (“excessive concern”)
• عدم الاهتمام على الإطلاق (“not caring at all”)

These choices respect the socio-cultural and religious sensitivities of the Arabic-speaking audience while keeping the pragmatic impact of the original text.

This application of paraphrasing using a related word highlights the human translator’s cultural and contextual awareness, which (MT) currently lacks.

The human translator’s use of related expressions preserves the pragmatic function and emotional tone of the original text, while aligning with the sociocultural norms of Arabic discourse.

This approach is particularly evident in examples categorized under:

• (possessive pronoun + F*word group),
• (F-word + -ing adverb),
• (F-word + verb "give").                                                                                                                                                                                                                                            

5.1.B Omission(Baker’sTaxonomy)                                                                                                                                             

 Baker (1992) emphasized that omission does not affect the meaning if it is not crucial to the text. Gottlieb (1992) has also stated that omission is agreed upon if the removed words are not essential. In (Table1) phrases number (26, 36) are deleted and the deletion did not harm the original text or the intended meaning but frequently does not convey the writer’s attitude. Krouglov (2018) mentioned that taboo words are used to show the writer’s idea and to emphasize the author’s point of view. At this point, (MT) does not use such technique as the machine does not have the common sense that people possess. The machine is not culturally specific and cannot determine the need to omit or delete words. The translated words give the implied meaning and feeling that the original text have. So, we can say that the machine translation conveyed the attitude and the feelings of the author of the original text as Stop giving a f*ck.  ( توقف عن الاهتمام.)

5.1.CUsingUnrelatedWords(Baker’sTaxonomy)                                                                                                                                                                                                                                   

 This strategy is also called functional or cultural substitution, which involves replacing the source word with a functionally equivalent or contextually appropriate phrase in the target language, even if it is not semantically related. This aligns with Chesterman’s (1997) pragmatic strategies, where the focus is not on word-level meaning but on preserving the communicative effect and pragmatic function of the original text. In this study, examples such as:

• Who fcked whom?* → تبادل الاتهامات (Exchanging accusations)
• I’m so fcking pissed off* → ما يجعلني منزعجا جدا بل غاضبا  (Which makes me upset and angry)

These examples reflect how the human translator shifts away from literal terms to convey the implied emotional or contextual message; the application of this strategy is significantly important when translating idiomatic taboo expressions that would otherwise be offensive if translated literally.

Google Translate, on the other hand, typically translates these expressions word-for-word (e.g., من ضاجع من؟ or اللهم اللعنة), missing the broader meaning and producing translations that are culturally inappropriate.

By applying functional or cultural substitution, the human translator achieves a pragmatically accurate and culturally sensitive translation, ensuring the target audience receives the intended tone and meaning.

5.1.D Literal Translation

The F*word literal meaning that is related to sex is used only four times in Table 1(see appendix 1). Ávila-Cabrera (2016) assured that word-for-word translation states moving a word or words from the original text to the translated text by obtaining the original text's idioms. Newmark (2004) emphasized that “literal translation is acceptable and should not be avoided if it secures referential and pragmatic equivalence to the original” (p. 69). Sex as a topic is taboo in the Arabic culture but the translator insisted on translating the four mentioned words rather than using the deleting them. As for the machine translation, the computer does not adequately address the exact meaning of these four words and that is why the message is not delivered by Google Translate. For example, the machine has translated :  كشف الخدمات اللوجيستيه للداعر  Unravelling the logistics of f*cking

which is not related at all to the context of the novel whereas the literal translation has succeeded to deliver the intended meaning  الأساليب العلميه لممارسه الجنس

The second example here is "Indiscriminate fucking"  الجنس المنفلت

Here in this example the machine does not adequately express the intended meaning as it is translated into :الداعر العشوائي

This is also evident in" Fuck more" which has been translated into  ضاجع اكثر. Here the machine could not identify the intended meaning and that is why it was translated اللعنه اكثر.

5.1.E Translating using cultural filtering (Chesterman’s model)

Translators might change a word in the target language by introducing expressions that the recipient is familiar with, (Khongbumpen,2007). Translators use cultural filtering to remove taboo words that might be considered offensive or inappropriate in the target language. This is vivid in many examples as

 Jenna Jameson f*cks ( بمهاره جينا جامسون في ممارسه الجنس ) or No f*cks given ( لست مهتما بهذا )

5.1.F Translating using illocutionary change (Chesterman’s model)

 This strategy is used when taboo language in the source text serves a specific social or emotional function (e.g. insulting). The translator may change the speech act to match how that function would be carried out in the target culture. This is obvious in many cases tabulated in Table 1 as

They say f*ck it (الي الجحيم ) 

 Who gives a f*ck?  ) (ما اهميه ذلك

5.1.G Reformulation (Chesterman’s model)

This strategy helps the translator to maintain the communicative effect or the emotional tone of a taboo expression without directly translating the offensive word. This is exemplified in :

Divert our f*cks ( توجيه اهتمامنا ) ,

The world is totally f*cked ( لعالم مكان سئ )

Table 2: The frequency of the strategies used by the human translator (HT) to translate taboo expressions into Arabic:

 5.2 Machine Translation Strategies

Google Translate does not consistently apply meaningful or context-aware strategies. The dominant translation approach was literal or transliterated rendering of the F-word, often resulting in inappropriate, nonsensical, or offensive output.

Many examples are tabulated (Table 1) to show these differences:

1. 1. ST: *Bukuvaski did not give a f*ck*

   *HT:* لم يكن بوكافسكي مباليا (Bukowski did not care) 

   *MT:* بوكافسكي لم يكترث (Bukowski did not care)

     - *Analysis:*  The human translator accurately captures the sense of indifference implied by the original phrase without using vulgarity. The choice of words fits within the cultural norms of Arabic, where explicit language is often avoided in public discourse. The translator used neutral word and reformulation strategies to convey his message by applying both Baker’s taxonomy and Chesterman’s model. Interestingly, the machine translation also manages to avoid explicit vulgarity. However, this seems more coincidental than intentional, as (MT) often struggles with context-specific nuances. This result highlights one of the rare instances where MT aligns closely with HT in both meaning and appropriateness.

2. ST: *Giving a f*ck about more stuff*

   -*HT:* الاهتمام أكثر (Caring more)

   - *MT:* إعطاء f * ck حول المزيد من الأشياء (Giving a f*ck about more things)

     - *Analysis:*  The human translator uses a paraphrased version that maintains the core message without vulgarity by adopting both Baker’s taxonomy, a neutral word, and Chesterman’s model, cultural filtering. This choice respects the cultural sensitivity surrounding explicit language while conveying the intended meaning.

The machine translation does not adequately translate the F*word directly. This shows (MT) limitation in understanding the cultural inappropriateness of vulgar language in Arabic.

3. ST: *Who gives a f*ck?*

   - *HT:* ما اهميه ذلك؟ (What’s the importance of that?

   - *MT:* من يكترث؟ (Who cares?)

     - *Analysis:* The human translator adeptly captures the dismissive tone of the original phrase without using offensive language. This reflects a deep knowledge and understanding of both the source and target cultures by applying two strategies, paraphrasing and illocutionary change.

The machine translation performs well in this instance by conveying the intended meaning without vulgarity. This suggests that (MT) can sometimes handle straightforward phrases correctly.

4. ST: *Then I gave a f*ck about*

   - *HT:* صرت الان مهتما (I became interested)

   - *MT:* ثم أعطيت اللعنة (Then I gave the curse)

     - *Analysis:* The human translator avoids vulgarity by choosing a phrase that conveys engagement by applying both Baker’s taxonomy, less expressive word; Chesterman, Cultural filtering. This shows a careful consideration of the target audience's cultural norms.

The machine translation fails to give the right translation by translating the phrase literally and inappropriately. This highlights how (MT) struggle with idiomatic expressions and context-specific meanings.

5. ST: *I have given a f*ck about many things*

   - *HT:* اهنممت اهتماما زائدا (I cared excessively)

   - *MT:* لقد أعطيت الكثير من الأشياء (I have given many things)

     - *Analysis:* The human translator captures the sentiment of excessive concern without vulgarity, aligning with cultural expectations. The reformulation strategy is applied to keep the meaning and to soften the tone. This translation maintains the original intent while being culturally appropriate.

The machine translation produces a nonsensical phrase, demonstrating a lack of contextual understanding. This highlights the fact that (MT) faces some limitations in handling nuanced language.

6. ST:*We are essentially giving f*cks*

   - *HT:* نهتم اهتماما زائدا (We care excessively)

   - *MT:* نحن في الأساس نعطي الملاعين (We are essentially giving the damned)

     - *Analysis:* The human translator conveys the idea of excessive concern in a culturally sensitive way. The paraphrase and the reformulation strategies are applied. The meaning is implied by preserving emotions and by removing obscenity. This approach avoids explicit language while preserving the original meaning. The machine translation results in a phrase that is both awkward and culturally inappropriate. This underscores the importance of contextual knowledge in translation.

7. ST: *To give too many f*cks is bad*

   - *HT:* المبالغه في الاهتمام (Excessive concern)

   - *MT:* لإعطاء الكثير من الملاعين أمر سيء (To give many damned is bad)

     - *Analysis:* : The human translator skillfully rephrases the original word to fit cultural norms, avoiding vulgarity by using Baker’s taxonomy, paraphrasing into a general warning without significant pragmatic shift. The intended translation communicates the intended message without offending the target audience.

The machine translation produces an inappropriate and unclear phrase, missing the intended meaning. This example highlights (MT) difficulty with idiomatic expressions.

8. ST: *By giving too many f*cks*

   - *HT:* المبالغه في الاهتمام (Excessive concern)

   - *MT:* من خلال إعطاء الكثير من الملاعين (By giving many damned)

     - *Analysis:*  The human translator provides a culturally appropriate translation that captures the intended meaning without vulgarity by using paraphrasing and cultural filtering strategies. Offensiveness is replaced by a neutral phrase. This approach demonstrates an understanding of the target audience’s cultural sensitivity.

The machine translation does not adequately fail convey the intended meaning, producing a literal and inappropriate phrase. This showcases the limitations of (MT) in handling context-specific language.

9. 9. ST: *They give way too many f*cks*

   - *HT:* يهتمون اهنماما زائدا اكتر مما يجب بكتير (They care excessively more than they should)

   - *MT:* تفسح المجال الكثير من اللعين (They give a lot of the damned)

     - *Analysis:*  The human translator effectively communicates the idea of excessive concern, avoiding vulgarity and maintaining cultural appropriateness.

The machine translation results in an awkward and inappropriate phrase, demonstrating a lack of contextual understanding.

10. 10. ST: *Not to give a single f*ck is to*

    - *HT:* عدم الاهتمام علي الاطلاق (Not caring at all)

    - *MT:* عدم إعطاء نكاح واحد هو (Not giving a single f*ck is to)

      - *Analysis:* The human translator expresses the sentiment of complete indifference without using explicit language, aligning with cultural norms by using Baker’s taxonomy, paraphrase + omission; Chesterman, cultural filtering.

The machine translation uses a literal and culturally inappropriate phrase that does not adequately convey the intended meaning.

11. ST: *There is nothing as not giving a single f*ck*

    - *HT:* لا وجود في الواقع الحقيقي لشئ اسمه عدم الاهتمام (There is nothing in reality called not caring)

    - *MT:* لا يوجد شيء مثل عدم إعطاء اللعنة واحدة (There is nothing like not giving a single curse)

      - *Analysis:* The human translator conveys the intended meaning without vulgarity, fitting cultural expectations by using paraphrasing and reformulation strategies in which abstracted ideas are turned into philosophical ones about empathy.

The machine translation uses an inappropriate phrase, missing the intended meaning.

12. ST: *Giving no f*cks whatsoever*

    - *HT:* حاله اللامبالاه و عدم الالهتمام (State of indifference and lack of care)

    - *MT:* لا يضاجع (Does not give a f*ck)

      - *Analysis:* The human translator accurately conveys the sentiment of complete indifference in a culturally appropriate manner. Paraphrasing and cultural filtering strategies aided the translation of the concept without the use of offensive language.

The machine translation uses a literal and culturally inappropriate phrase, does not adequately convey the intended meaning.

13. ST: *When you give better f*cks*

    - *HT:* تركز اهتمامك علي أشياء افضل (Focus your concern on better things)

    - *MT:* عندما تعطي أفضل الملاعين (When you give better damned)

      - *Analysis:*  The human translator replaces the phrase with a culturally appropriate expression by applying the reformulation and the paraphrasing strategies.

The machine translation uses an awkward and inappropriate phrase which does not adequately address the intended meaning.

14. ST: *I will teach you to give fewer f*cks*

    - *HT:* الاهتمام بعدد اقل من الأشياء (Care about fewer things)

    - *MT:* أعط عددًا أقل من الأثداء (Give fewer f*cks)

      - *Analysis:* The human translator conveys the intended meaning without vulgarity, aligning with cultural norms.

The machine translation uses a literal and culturally inappropriate phrase that does not adequately capture the intended meaning.

15. ST: *We give tones of f*cks*

    - *HT:* نهتم كثيرا جدا باشياء كثيره جدا (We care a lot about many things)

    - *MT:* نعطي نغمات الملاعين (We give tones of damned)

      - *Analysis:* :* The human translator accurately conveys the sentiment of excessive concern without vulgarity, fitting cultural expectations by

applying reformulation and paraphrasing strategies to adjust the tone into Arabic norms.

The machine translation produces an awkward and inappropriate phrase that demonstrate a lack of contextual understanding.

16. 16. ST: *People who hand out f*cks*

    - *HT:* يبالون باشياء متعدده اكثر مما يجب (Care about many things more than they should)

    - *MT:* الناس الذين يوزعون الملاعين (People who hand out damned)

      - *Analysis:* The human translator conveys the intended meaning without vulgarity, fitting cultural norms.

The machine translation uses an awkward and inappropriate phrase that does not adequately capture the intended meaning.

17. ST: *No f*cks given*

    - *HT:* لست مهتما بهذا (Not caring about this)

    - *MT:* لا الملاعين (No f*cks given)

      - *Analysis:* The human translator uses the paraphrasing and cultural filtering strategies to convey the sentiment of indifference without vulgarity to align with cultural norms.

 The machine translation uses a literal and culturally inappropriate phrase that does not adequately address the intended meaning.

18. ST: *Magic f*ck-giving fairy dust*

    - *HT:* مسحوق الامبالاه السحري (Magic dust of indifference)

    - *MT:* السحر اللعنة إعطاء الجنية الغبار (Magic f*ck-giving fairy dust)

      - *Analysis:* The human translator replaces the phrase with a culturally appropriate expression by using the reformulation and the cultural substitution strategies.

The machine translation uses a literal and culturally inappropriate phrase that does not adequately capture the intended meaning.

19. ST: *You have a limited amount of f*cks to give*

  - *HT:* مقدارا محددامن الاهتمام (A limited amount of care)

-    MT: لديك كمية محدودة من الملاعين لتقديمها (You have a limited amount of f*cks to give)

- Analysis: * The human translator conveys the intended meaning without vulgarity, fitting cultural norms by applying the paraphrasing and reformulation strategies in which offensiveness is replaced with concept of limited concern.

The machine translation uses a literal and culturally inappropriate phrase that does not adequately reflect the intended meaning.

20. ST: *It was the f*cks not given*

 -*HT: الامبالاه التي ابديتها (The indifference shown)

- *MT:    لقد كانت هذه الملاعين لم تعط         

Analysis: *The human translator accurately conveys the core idea of indifference without vulgarity by using paraphrasing and reformulation strategies

The machine fails to deliver the meaning.

Table 3: Human Translation strategies used in each example given in Table 1

5.3 Comparative Accuracy and Cultural Sensitivity

The human translator shows the ability to translate taboo words into culturally relevant phrases as well as preserving the implied meaning of the text. On the other hand, the machine translation frequently produced awkward translations or inappropriate ones because it does not adequately comprehend or understand the cultural aspect.

Table 4: Google Translate strategies

Table 5: the levels of accuracy between MT and HT

6. Discussion of the Results

The analysis emphasizes how human translation (HT) and machine translation (MT) handle the translation of the F*word and its derivatives from English to Arabic, focusing on linguistic nuances and cultural context. The discussion draws attention to the findings, which indicate that human translation excels in managing cultural- specific language, achieving contextual relevance. From a theoretical perspective, the findings affirm Toury's (1995) findings that translation is inherently a socio-cultural act, in which equivalence is not only linguistic but also functional and cultural. The human translator’s choices of the translation show an understanding of the context itself. For practice, the results stress the necessity of human observation in the translation of sensitive or culturally related terms, particularly in Arabic. These findings align with Chesterman’s (1997) model of pragmatic translation strategies, which stresses the importance of communicative function and cultural acceptability. Google Translate over-relied on lexical equivalence without deeper semantic or pragmatic analysis and this caused many problems. It often led to literal translations that hindered the translation of the exact meaning of the intended text. For instance, expressions like “I don’t give a f*ck” were frequently mistranslated and consequently concluded to an incoherent Arabic output. This aligns with Castilho et al. (2018), who argue that current MT systems lack a mechanism for contextual interpretation beyond annotated data. Despite recent advances in NMT (e.g., Vaswani et al., 2017; Bahdanau et al., 2014), the results suggest that (MT) systems are still unable to match the nuance and flexibility of human cognition as they are unable to produce the intended meaning. The comparative analysis revealed that human translators strategically employed omission, reformulation, and paraphrasing to translate taboo content. Although previous studies such as Ávila-Cabrera (2016) and Hendal (2020) pointed strategies like omission and euphemism in taboo translation, this study adds a nuanced analytical level by distinguishing between semantically related and functionally substituted translations. This distinction gives the chance for precise evaluation of how translators balance between linguistic fidelity and cultural appropriateness.

The findings also highlight a clear divergence in the strategy applied by human translation (HT) and machine translation (MT). Human translators show a flexible, context-sensitive use of strategies, relying on both Baker's and Chesterman's taxonomies. Although many of the strategies fall under Baker's lexical-level categories as omission, paraphrasing, cultural substitution), others clearly align with Chesterman's pragmatic strategies. For instance, the use of functional or cultural substitution in translating "Who f*ckd whom?"  as "تبادل الاتهامات") involves choosing a functionally equivalent expression that communicates the social implication without translating the lexical form. This strategy goes beyond Baker’s semantic paraphrasing and reflects on Chesterman model with the communicative effect. Moreover, reformulation of idiomatic or emotional phrases, such as “mind-f*ck” becoming “يتعب العقل حقاً” (It really tires the mind), represents a strategy that is not used by Baker but it is important in preserving the intended tone of the source text. The integration of Chesterman’s model and Baker’s taxonomy gives a chance for a more cultural understanding of the strategies employed. It reflects the idea supported by Toury (1995) and Hatim & Mason (1997), that translation is not just a linguistic act but a sociocultural and communicative process. The limitations of (MT) are obvious in their handling of cultural references, revealing a gap between literal translations and the intended meaning. This gap highlights the need for human expertise in translating taboo language, where cultural understanding is crucial. The findings shed light on the fact that literary genre has certain characteristics that are different from other genres. The translation of some characteristics of the literary text needs linguistic, and semantic analysis. This novel has lots of literary terms, specifically, the taboo. Taboo words that are translated by the translator require cultural familiarity with the mother tongue and the foreign language. One of the main limitations of (MT) lies in its lack of cultural awareness. Human translators employed techniques such as cultural substitution and euphemism. This study focused on the translation of taboo words that need the transfer of correct cultural elements that machine translation lacks or cannot produce. Machine translations can deal with linguistic issues, but it is difficult to translate concepts. Therefore, machine translation does not adequately translate many taboo words in this book. Human translators are irreplaceable; machines can only assist them. Google Translate, on the other hand, struggles with maintaining the same level of cultural sensitivity. The machine translation often results in literal translations that does notadequately capture the cultural nuances or context of the source text. For example, "not giving a f*ck" might be directly translated to " عدم إعطاء نكاح”  which translates back to "not giving a f*ck" in a literal and often inappropriate manner. Such translations highlight the limitations of (MT) in handling taboo language, as it lacks the cultural understanding and contextual sensitivity that human translators possess.Human translators employ a variety of strategies to adapt the F*word in a culturally sensitive manner. These strategies include paraphrasing with related words, cultural substitution, and omission. For instance, phrases like "not giving a f*ck" are often translated to convey a sense of indifference or lack of concern without explicit vulgarity, such as "   مباليا بوكافسكي يكن  لم " (Bukowski did not care). This approach ensures that the translated text aligns with cultural norms while maintaining the original text’s intent and tone. In translating taboo expressions such as the F*word, human translators act as cultural mediators, employing what Hatim and Mason (1997) call a 'cultural filter', a conscious adaptation of language to align with the sociocultural norms of the target audience. This filter is particularly evident in Arabic, where religious and social values strongly regulate public discourse.  In conclusion, the analysis highlighted that human translators frequently use strategies such as omission, euphemism, and cultural substitution to translate taboo words appropriately. However, (MT) mainly relies on direct translations that often lack contextual sensitivity. Cultural-specific aspects that belong to a certain language cannot be translated by machines as they do not adequately consider this because they are not programmed to do so. As for the universal culture, which is common to most or all languages, the machine can do its job. This can answer the first research question of this paper. As for the second question, Google Translate could not adequately translate taboo words as they are cultural-specific.

This research adds to what previous studies have found by presenting a detailed comparison of human and machine translation of taboo terms, applied to a well-known literary text. While earlier work has showed that machine translation struggles with culturally sensitive language, this study goes an extra mile by combining Baker’s and Chesterman’s models and applying them together. The use of both frameworks, along with a quantitative analysis at how often different strategies were used, provides a clearer and more practical understanding of how these strategies work. This type of combined analysis is not common in existing research and could be a useful guide for future studies that deal with translating sensitive content. This study also suggests ways to improve machine translation in this area. It draws attention to many tools like Latent Semantic Analysis (LSA) and similar advanced methods. These could help translation systems move beyond simply converting words and instead can go deep on meanings of words and phrases in their full context. This can enhance machine translators to better recognize terms and carries a cultural or pragmatic meaning and adjust the translation accordingly.

Table 6: Classification of Translation Strategies

Table 7: Accuracy and Appropriateness of Translation Strategies (Based on 57 instances of the F*word in "The Subtle Art of Not Giving a F*ck")

Table 7 supports the argument that MT lacks contextual pragmatics and cultural prioritization (Baker, 1992), (Chesterman, 2000). This is exemplified

in the following pie chart.

Pie chart 1:  The Acuuracy of Machine Translation & Human translation

The pie chart indicates that the MT is accurate with 12 % while the HT proves 88% accuracy in its translation.

7.ConclusionandRecommendations

It is concluded in this study that  the machine translation can be a useful tool for basic translations, but it falls short in handling culturally sensitive and contextually complex translations like those involving taboo language. Human translators, by contrast, skills in acting as cultural mediators, using strategies that preserve both the semantic content and the cultural appropriateness of the target text. The effectiveness of strategies such as cultural substitution, omission, and functional equivalence sheds light on the necessity of cultural competence in translating taboo content. Translators working with Arabic must act not only as linguistic mediators but also as cultural interpreters. Examining fifty-seven F*word and its derivatives from The Subtle Art of Not Giving a Fck* showed that human translators successfully chose techniques like paraphrasing, omission, and cultural substitution to convey the message without violating sociolinguistic norms. On the other hand, Google Translate often produced literal or culturally inappropriate translations, showing a lack of contextual awareness and pragmatic reasoning. The findings assure the importance of human judgment in translation, especially when dealing with taboo language. There is a gap in (MT)’s ability to translate taboo words when compared to human translation. Although (MT) excels at syntactic and semantic alignment in neutral contexts, it lacks the sensitivity and the awareness needed for translating offensive terms. Computers are programmed by specialists to help humans but not to remove their role. Many elements have a crucial role in the accuracy of the translation as culture and physical context. These factors cannot be identified by the computer. The problem that faces Google Translate is understanding the contextual meaning of words. Arabic translators have a sophisticated role as they are “bound to several religious, cultural, and ideological factors that limit their handling of foreign taboo texts” (Abbas, 2015). Finally, the lack of an annotated Arabic corpus has caused lots of problems and obstacles that can cause many translation problems. Comparing the strategies and translations of the F*word in this study to other translated texts of the same genre by another machine translation program in future studies is crucial. Also, Latent Semantic Analysis is an application that can trace the contextual meaning of words. It goes beyond NLP techniques where only the presence of a specific word has a meaning. In LSA (Latent Semantic Analysis), the absence of a certain word also has a meaning. LSA uses mathematical algorithms to represent the usage of words.  One of the key contributions of this research is that it offers a focused case study on a specific term, the F*word, within a literary context, which has not been tackled in a few studies. The dual application of Baker’s and Chesterman’s frameworks also offers a structured way to analyze both word-level and pragmatic strategies, and could serve as a useful model for further research. This research suggests that improving machine translation in such contexts needs more than technical advances alone. There is a demand for culturally annotated corpora and more work on embedding cultural filters into translation algorithms, especially for languages like Arabic where sociocultural norms strongly shape language use. By addressing these areas, further studies can contribute to a more understanding of the relation between translation technological methods and cultural sensitivity, mainly in languages that are rich with social taboo.

Future research should focus on developing more sophisticated (MT) systems that can better handle cultural contexts, possibly through the integration of advanced AI techniques like Latent Semantic Analysis (LSA). Furthermore, building a comprehensive annotated Arabic corpus could significantly improve the quality of machine translations. This comparative study highlights the ongoing need for human expertise in translation, particularly for languages and cultures with complex taboos and societal norms. It also provides a foundation for future advancements in (MT) technology, aiming for a more nuanced and culturally aware approach to translation. Future research has to be applied by using this method as it can produce different results. Further research is needed to help in the development of machine translation programs. While machine translation has made notable advances in recent years, this study confirms that it still struggles with culturally sensitive and taboo language, particularly when translating from English into Arabic. By comparing human translation strategies to those of Google Translate, the paper shows how human translators can better adapt meaning to fit cultural norms through techniques like omission, paraphrasing, and cultural substitution.

To sum up, the research confirms that while Baker’s strategies offer valuable insights into lexical-level challenges, they are insufficient alone for dealing with the sociocultural complexities of taboo translation. The integration of Chesterman’s model opens the door for a holistic analysis that accounts for tone, function, and audience appropriateness, mainly when translating the F*word from English into Arabic. Therefore, this study suggests that any analysis of taboo language translation, mainly between linguistically and culturally distant languages, should adapt a hybrid approach that combines lexical, functional, and pragmatic dimensions. Future research should explore multilingual corpora and test newer (MT) systems like DeepL or LLAMA for further comparative insights. The frequent failures of Google Translate in handling the F-word suggest a need to train MT systems on culturally annotated data. Additionally, integrating a “cultural filter” mechanism, suggested by Hatim and Mason’s (1997) could help machines detect when euphemism or omission is culturally necessary.