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METHODOLOGY OF CORPUS ANALYSIS OF ELLE MAGAZINE (UK)
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Інформація про роботу
Рік:
2025
Тип роботи:
Курсова робота
Предмет:
Філологія
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CONTENTS
INTRODUCTION
4
CHAPTER 1. THEORETICAL FOUNDATIONS OF BEAUTY NEWS LANGUAGE RESEARCH
6
1.1 Corpus linguistics: modern approaches and methodology
6
1.1.1 Definition and Key Principles of Corpus Linguistics
6
1.2 Specifics of beauty magazine language
11
CHAPTER 2. METHODOLOGY OF CORPUS ANALYSIS OF ELLE MAGAZINE (UK)
17
2.1 Research corpus formation
17
CONCLUSIONS
25
LIST OF REFERENCES
27
ANNOTATION
This thesis explores the language of beauty news in Elle magazine (UK) through corpus analysis. The study examines linguistic patterns, stylistic features, and recurring themes in beauty-related articles, shedding light on how language constructs beauty narratives. A corpus of selected articles is analyzed using computational and qualitative methods to identify key lexical choices, rhetorical strategies, and discourse tendencies. The research highlights the persuasive techniques and branding language employed in beauty journalism, offering insights into the role of media in shaping beauty perceptions. The findings contribute to media linguistics and discourse studies by revealing the linguistic mechanisms behind beauty-related content in a leading fashion magazine.
Keywords. beauty journalism, corpus analysis, discourse analysis, media linguistics, Elle magazine.
INTRODUCTION
Relevance of the topic. The language of beauty journalism, particularly in prominent publications like Elle (UK), plays a crucial role in shaping aesthetic standards, commercial narratives, and sociocultural perceptions of beauty. In the context of rapid digital media development and the growing influence of the beauty industry, studying the language of such texts is highly relevant, as it reveals mechanisms of persuasion, audience engagement, and the reflection of gender and cultural codes through linguistic means.
The aim of this paper is to conduct a corpus-based study of the linguistic features of beauty news in the UK edition of Elle magazine, to identify lexical, grammatical, and stylistic patterns, and to characterize current trends in presenting information within the framework of modern media discourse.
In accordance with the aim, the following objectives are set:
- to analyze the main approaches of corpus linguistics and their application in media research;
- to identify the linguistic features of beauty magazine discourse based on the example of Elle (UK);
- to construct and process a research corpus of beauty news articles using corpus analysis tools.
The object of the study is the language used in beauty news texts of contemporary British glossy magazines.
The subject of the study is the lexical, grammatical, and stylistic features of beauty news discourse in Elle (UK), identified through corpus-based analysis.
The research methods include methods of corpus linguistics, contextual-interpretative analysis, descriptive and quantitative analysis.
The research material comprises a self-compiled corpus of beauty news articles published in the UK edition of Elle magazine between 2022 and 2024. The texts were collected from the official website of Elle UK (https://www.elle.com/uk/) by selecting articles categorized under the "Beauty News" section. A total of 100 articles were extracted in .txt format and processed using AntConc software for linguistic analysis. The selection criteria focused on articles containing product reviews, trend reports, expert commentary, and promotional content to ensure thematic consistency and representativeness of modern beauty discourse.
Practical significance. The findings of this study may be applied in the development of courses in media linguistics, stylistics, and journalistic writing. They can also support the training of journalists and editors working in beauty-related communication. Moreover, the conclusions may serve to improve the quality of content in women’s magazines by deepening the understanding of the linguistic strategies used to influence target audiences.
Scientific novelty. This research offers an original approach to analyzing beauty journalism language by integrating corpus linguistic methods with stylistic analysis. For the first time, a systematic corpus-based analysis of beauty news in Elle (UK) is carried out, contributing to a broader understanding of media language in the beauty industry and establishing a foundation for future studies in media linguistics.
CHAPTER 1. THEORETICAL FOUNDATIONS OF BEAUTY NEWS LANGUAGE RESEARCH
1.1 Corpus Linguistics: Modern Approaches and Methodology
Corpus linguistics is a data-driven approach to the study of language that relies on large collections of text (corpora) to analyze linguistic patterns, frequencies, and structures. By systematically examining naturally occurring language data, corpus linguistics provides empirical insights into language use across various contexts, including media discourse and beauty journalism. This methodology has gained significant recognition in contemporary linguistic research due to its ability to integrate quantitative and qualitative analyses, thereby offering a comprehensive framework for studying linguistic phenomena.
Furthermore, corpus linguistics has been instrumental in refining natural language processing (NLP) applications, improving machine translation, and enhancing automated text analysis. The ongoing advancements in computational linguistics have significantly contributed to the increased scope and efficiency of corpus-based studies.
1.1.1 Definition and Key Principles of Corpus Linguistics
Corpus linguistics is a methodological approach to studying language that relies on large, structured collections of texts, known as corpora. These corpora serve as representative samples of real-world language use, encompassing various genres, registers, and modes, including written, spoken, and multimodal data. Researchers analyze linguistic patterns through computational tools that can process millions of words efficiently. This enables the identification of frequency, distribution, and collocational behavior of lexical and grammatical structures. Unlike introspective or anecdotal methods, corpus linguistics emphasizes empirical evidence drawn from authentic usage. Its objectivity contributes to robust and replicable findings [5, p. 11].
The origins of corpus linguistics date back to the 1960s, but the field has gained significant momentum with advances in digital storage and processing capabilities. Early corpora such as the Brown Corpus laid the groundwork for more complex datasets like the British National Corpus and COCA. These developments have allowed for both synchronic and diachronic analyses across different varieties of English. The field is now applied in disciplines as diverse as forensic linguistics, healthcare communication, and artificial intelligence [1, p. 87]. Contemporary research tools like AntConc and Sketch Engine have further facilitated access to corpus-based insights [6]. These platforms offer visualization features and statistical tests that streamline linguistic interpretation.
Corpus linguistics supports both quantitative and qualitative analyses. Quantitative methods focus on measuring linguistic phenomena using statistical models, often leading to frequency-based conclusions. On the other hand, qualitative techniques allow for close reading and interpretation of discourse patterns, especially in critical discourse studies [7, p. 45]. This duality makes corpus linguistics particularly versatile in answering both descriptive and explanatory questions. Its applications range from tracing semantic change in scientific discourse [4, p. 546] to uncovering implicit ideologies in media and political speech. Moreover, hybrid approaches increasingly blend the strengths of both paradigms for a comprehensive view.
A key principle in corpus linguistics is representativeness, which ensures that the corpus reflects the linguistic features of the population or context under study. Balance and size are also crucial to avoid overrepresentation of particular styles or genres. Corpora may be general-purpose, like the British National Corpus, or domain-specific, such as those focused on medical or legal texts [3, p. 11]. Specialized corpora enable the analysis of field-specific terminology and communicative practices. Researchers also construct learner corpora to investigate interlanguage and error patterns in second language acquisition [10, p. 87]. This granularity enables linguists to draw more precise conclusions about language variation and development.
An essential component of corpus linguistics is annotation, which refers to the enrichment of corpus texts with linguistic metadata. These annotations may include part-of-speech tags, syntactic parses, semantic categories, or even pragmatic labels. Annotated corpora allow for more detailed queries and help to test hypotheses about language structure and use. For instance, researchers can examine passive constructions across disciplines or study the frequency of hedging in academic writing [11, p. 18]. Annotation standards like XML and TEI ensure consistency and interoperability across corpora. This structured approach enhances the validity and depth of corpus-based research.
The field has also embraced innovative cross-disciplinary integrations, such as combining corpus linguistics with cognitive science, digital humanities, and social sciences. In particular, distributional semantics uses corpora to model word meaning through co-occurrence patterns [2, p. 234]. These methods are increasingly applied in natural language processing (NLP) and machine learning. Corpus data also inform evidence-based approaches in medicine and education, where understanding linguistic behavior supports more effective communication and instruction [25, p. 596]. Such integrations demonstrate the adaptability of corpus linguistics to real-world problems. As a result, it has become an indispensable tool in both academic and applied research.
In summary, corpus linguistics offers a data-driven, empirical, and reproducible method for investigating language. Its foundation on authentic usage data provides a more objective understanding of linguistic phenomena than traditional intuition-based methods. With its dual focus on form and function, corpus linguistics has transformed fields such as lexicography, stylistics, translation studies, and second language acquisition. As corpora become more multimodal and interactive, the scope of the field continues to expand. Corpus linguistics thus serves as both a lens and a laboratory for exploring the dynamic nature of language in context. It is a cornerstone of modern linguistic inquiry, connecting theory with usage in unprecedented ways.
Corpus linguistics is governed by several core principles that shape its methodological and analytical framework. One of the most fundamental is authenticity, which emphasizes the use of real-life language data rather than constructed examples. This approach ensures that the findings reflect actual language usage in natural communicative contexts [5, p. 11]. Authentic texts are gathered from diverse sources such as newspapers, academic writing, transcripts of speech, or social media. The emphasis on authenticity distinguishes corpus linguistics from traditional linguistic methods that rely on intuition. As a result, research outcomes tend to be more empirically grounded and contextually rich.
Another foundational principle is representativeness, which refers to how well a corpus captures the language variety or domain being investigated. A well-constructed corpus should balance various text types, registers, and genres to reflect the diversity within a language. For example, a general corpus may include fiction, non-fiction, blogs, and spoken data to ensure linguistic coverage [24, p. 89]. Specialized corpora, such as those in healthcare or legal discourse, are tailored to represent specific professional domains [1, p. 87]. The representativeness of a corpus determines the validity of generalizations made from its analysis. It also plays a crucial role in cross-cultural or diachronic linguistic comparisons.
Frequency-based analysis is another central concept in corpus linguistics. It allows researchers to identify common patterns, collocations, and grammatical structures by examining word frequency and distribution. Repeated patterns often reveal linguistic norms, while deviations may suggest stylistic or contextual variation [11, p. 16]. This quantitative approach is especially useful in lexicography, language teaching, and discourse studies. Frequency analysis also supports data-driven learning by showing how words and structures function in actual contexts. Tools like AntConc facilitate this by offering frequency lists and concordance lines for further interpretation [6].
The principle of reproducibility ensures that corpus-based research can be replicated and verified by other scholars. Transparency in corpus design, data selection, and analysis parameters allows for consistency across studies. This contributes to the scientific rigor of corpus linguistics and enhances its reliability as a method [3, p. 11]. Researchers are encouraged to document their processes and, where possible, make corpora publicly available. Such practices support open science and foster collaborative work within the linguistic community. Reproducibility also helps validate theoretical claims through empirical testing.
Annotation is another crucial principle that enhances the functionality of corpora. Annotated corpora contain linguistic metadata such as part-of-speech tags, syntactic structures, or semantic roles. These enrichments enable more sophisticated searches and statistical modeling of language phenomena. For instance, part-of-speech tagging helps distinguish between homographs like run as a noun and run as a verb [17, p. 12]. Modern annotation often employs machine learning algorithms for efficiency and scalability. Standard formats such as XML and JSON ensure compatibility across platforms.
The integration of computational tools is essential to handle the vast size and complexity of modern corpora. Software applications like Sketch Engine, AntConc, and R provide functionalities for concordance, collocation, keyword analysis, and visualization. These tools help linguists interpret language patterns on a large scale, often involving millions or billions of words [13, p. 22]. Computational methods also facilitate statistical tests to assess the significance of observed phenomena. As a result, corpus linguistics has become increasingly aligned with fields like data science and computational linguistics. The synergy of linguistics and technology enhances both depth and scalability in research.
In conclusion, the key principles of corpus linguistics—authenticity, representativeness, frequency-based analysis, reproducibility, annotation, and computational analysis—form the foundation of the field. Together, these elements allow for a comprehensive and empirical study of language use across different contexts and communities. The methodological rigor promoted by these principles contributes to the credibility and applicability of corpus-based research. As corpora continue to evolve, incorporating multimodal and multilingual content, the importance of adhering to these principles grows. Their application ensures that linguistic inquiries remain both data-driven and contextually grounded. Corpus linguistics thus continues to play a vital role in advancing modern linguistic theory and practice.
In sum, corpus linguistics offers a data-driven, systematic approach to understanding language as it is used in authentic contexts, making it an essential tool in modern linguistic research.
1.2 Specifics of Beauty Magazine Language
Corpus linguistics has emerged as a pivotal methodology in contemporary linguistic research, offering a systematic and data-driven approach to the study of language. By relying on large, digitized collections of authentic texts—known as corpora—this field allows scholars to observe how language is actually used across various contexts, genres, and time periods. The use of computational tools enables precise analysis of linguistic patterns, making corpus linguistics a versatile framework for theoretical and applied studies alike. Its interdisciplinary relevance extends to discourse analysis, language teaching, translation studies, and lexicography, underscoring its significance in both academic and professional domains.
Corpus linguistics is a field of applied linguistics that focuses on the empirical analysis of language through large, structured datasets known as corpora. These corpora consist of naturally occurring texts—written, spoken, or multimodal—that reflect authentic language usage. The discipline seeks to uncover patterns in vocabulary, grammar, and discourse by employing computer-assisted methods. Researchers use software tools to extract quantitative data on language behavior, including frequency, collocation, and distribution. This approach contrasts with traditional introspective methods, relying instead on observable and verifiable language evidence [24, p. 12]. Corpus linguistics thus provides a more data-driven foundation for linguistic inquiry.
A central characteristic of corpus linguistics is its emphasis on authenticity. Corpora are constructed from real-life communication, making the data ecologically valid and contextually rich. They may include newspaper articles, academic writing, transcripts of interviews, or casual conversations. By analyzing naturally occurring language, researchers gain insights into how language is actually used, not just how it is prescribed. This authenticity strengthens the applicability of findings in fields like language education, lexicography, and translation [11, p. 15]. It also allows for the discovery of emerging patterns and linguistic innovation in real time.
Corpus linguistics supports both synchronic and diachronic analysis. Synchronic studies examine language at a specific point in time, allowing researchers to investigate contemporary usage and variation. In contrast, diachronic research analyzes language change over time by comparing texts from different historical periods [4, p. 546]. This dual capability makes corpus linguistics valuable in historical linguistics, stylistics, and sociolinguistic research. Digital corpora such as COHA (Corpus of Historical American English) have made diachronic analysis increasingly accessible. As a result, linguistic theories can be empirically tested across temporal dimensions.
Another key advantage of corpus linguistics is its interdisciplinary utility. The method has been applied in diverse areas such as legal discourse, political rhetoric, healthcare communication, and media studies. For instance, studies in nursing and medicine use corpora to analyze how language reflects professional identity or constructs patient relationships [1, p. 87]. This demonstrates that corpus-based analysis extends beyond pure linguistics into the social sciences and humanities. It allows scholars to interrogate how language shapes ideologies, power structures, and cultural values. The approach’s flexibility makes it adaptable to both descriptive and critical frameworks.
Corpus linguistics also contributes significantly to language teaching and learning. By analyzing learner corpora, educators can identify common errors and interlanguage patterns in second language acquisition. This data-driven insight helps design more effective instructional materials and assessment tools [10, p. 92]. Additionally, corpora of native-speaker language inform curriculum development and usage-based grammar instruction. Teachers can expose learners to authentic input and highlight frequent usage patterns. This supports a usage-based model of language learning, which aligns with current trends in applied linguistics. Corpus-informed teaching thus bridges theory with classroom practice.
The computational aspect of corpus linguistics is integral to its methodology. Modern tools such as AntConc, Sketch Engine, and LancsBox allow researchers to perform frequency counts, collocation analysis, concordancing, and keyword extraction [6]. These tools enable the handling of large-scale linguistic data that would be impossible to analyze manually. Advances in natural language processing have further enhanced the ability to annotate, parse, and model corpora. Such innovations support deeper analysis and facilitate cross-linguistic comparison. As computational power increases, so does the scope and precision of corpus-based studies.
A foundational concept in corpus linguistics is frequency. The frequency with which words, phrases, or constructions occur can reveal patterns of normativity and deviation in usage. Frequent items tend to represent linguistic norms, while rare or marked items may suggest stylistic, regional, or genre-specific variation [2, p. 234]. Frequency data is essential in building dictionaries, grammar references, and language models. It also underpins probabilistic approaches to language learning and processing. Thus, frequency serves as both an analytic tool and a theoretical lens within corpus studies.
Another principle is annotation, which enriches raw corpus data with linguistic metadata. Annotated corpora include part-of-speech tags, syntactic structures, semantic roles, or discourse markers. These enhancements allow for more targeted queries and refined statistical analysis [17, p. 28]. Annotated corpora are essential in applied research areas such as sentiment analysis, stylistics, and language technology. Standards in annotation (e.g., XML or JSON) ensure consistency across projects. As annotation tools become more automated, their integration into corpus construction becomes more efficient.
In summary, corpus linguistics is a powerful method of analyzing language that combines authenticity, empirical analysis, and computational precision. It facilitates both quantitative exploration and qualitative interpretation, offering robust insights into how language functions across contexts. Its applicability spans multiple domains, from education and translation to sociolinguistics and discourse analysis. The field continues to evolve through technological innovation and interdisciplinary expansion. As language data becomes more accessible and diverse, corpus linguistics remains at the forefront of evidence-based linguistic research. Its emphasis on real-world usage grounds it firmly in the realities of human communication.
Corpus linguistics is a data-driven approach to language analysis based on large collections of authentic language data. Its methodological framework is grounded in several fundamental principles that guide the design, compilation, and interpretation of corpora. Among these, the principle of authenticity is paramount, ensuring that language data reflect real-world use rather than artificially constructed examples. Authenticity guarantees that the patterns observed in corpora are representative of natural language in context. This principle strengthens the empirical foundation of linguistic research [24, p. 7]. It also supports more valid applications in education, lexicography, and discourse studies.
A second essential principle is representativeness, which ensures that the corpus captures the variability and diversity of the target language or register. A well-constructed corpus should account for genre, register, modality, and demographic diversity. This balance allows researchers to draw reliable conclusions about the linguistic system being studied [11, p. 18]. Representativeness is especially important when building corpora for specialized fields, such as medical or legal discourse. If the corpus is not representative, the findings may be skewed or lack generalizability. This principle underlies the design of widely used corpora such as the British National Corpus and COCA.
Systematicity is another cornerstone of corpus linguistics. It refers to the consistency and replicability of data collection and analysis procedures. Systematic methodologies enable researchers to document their processes transparently and make their studies reproducible by others [3, p. 11]. This enhances the scientific reliability of corpus-based findings. Systematic corpus construction often includes clear sampling criteria, annotation protocols, and metadata documentation. As a result, the data can be re-used for multiple studies or compared across different corpora.
The principle of frequency analysis plays a central role in corpus linguistics. Linguists use frequency data to identify common patterns and statistically significant variations in vocabulary and structure. Frequent forms often indicate linguistic norms, while infrequent ones may suggest stylistic or regional differences [2, p. 234]. This allows for the development of usage-based grammar and lexicon descriptions. Frequency analysis is also crucial in critical discourse analysis and sociolinguistics, where it helps reveal ideological patterns and group-specific usages. Tools like AntConc and Sketch Engine support such analyses by generating frequency lists and keyword distributions [6].
Annotation adds interpretive depth to corpora by tagging elements such as parts of speech, syntactic structure, or semantic roles. This enrichment facilitates complex linguistic queries and enables the testing of syntactic or pragmatic hypotheses [17, p. 21]. Annotated corpora also serve as training data for natural language processing applications. Different annotation schemes, such as Penn Treebank or Universal Dependencies, ensure consistency across datasets. Annotation can be manual or automated, but in both cases, it must adhere to predefined linguistic criteria. The quality of annotation often determines the accuracy of corpus-based findings.
Another important principle is transparency, which ensures that all steps of corpus design, processing, and analysis are clearly documented and available for scrutiny. Transparency supports academic integrity and allows other researchers to evaluate, replicate, or extend a study [25, p. 596]. It also enables the peer review of corpus tools and methods. In open-access research, transparent practices encourage collaboration and innovation. Datasets, codebooks, and scripts are often shared to promote reproducibility. This culture of openness contributes to the credibility and progress of corpus linguistics as a scientific discipline.
Tool-assisted analysis is a defining feature of corpus linguistics. Researchers rely on specialized software to handle large datasets and conduct advanced linguistic analyses. These tools enable concordancing, collocation analysis, keyword extraction, and visualization [7, p. 35]. Computational tools also allow for the exploration of multi-billion-word corpora in real time [13, p. 20]. Their use democratizes access to linguistic data and enhances analytical precision. As computational linguistics and corpus linguistics converge, tool-assisted analysis continues to evolve.
Corpus linguistics also values the interplay between quantitative and qualitative approaches. While frequency counts and distributional patterns offer statistical insights, qualitative analysis of concordance lines provides contextual interpretation. This dual approach enables researchers to identify not only what patterns exist, but why they occur. For example, examining collocates of a term may reveal underlying ideologies or discourse strategies [8, p. 249]. Such integration supports richer interpretations in fields like media studies, gender linguistics, and political discourse analysis. It affirms the relevance of corpus linguistics to critical and applied research.
The key principles of corpus linguistics—authenticity, representativeness, systematicity, frequency analysis, annotation, transparency, and computational support—form a robust methodological foundation. These principles ensure that corpus-based studies are reliable, replicable, and insightful. They also enable linguistic analysis that is both quantitatively rigorous and qualitatively meaningful. As corpus linguistics continues to expand into interdisciplinary domains, adherence to these principles remains essential. They provide the structure needed to navigate the complexity of language data. Ultimately, these principles contribute to the discipline’s enduring relevance and impact in modern linguistics.
In conclusion, corpus linguistics provides a rigorous, empirical foundation for analyzing authentic language use, thereby advancing linguistic theory and its practical applications.
CHAPTER 2. METHODOLOGY OF CORPUS ANALYSIS OF ELLE MAGAZINE (UK)
2.1 Research Corpus Formation
The present study is based on a self-compiled corpus constructed from beauty news articles published in the UK edition of Elle magazine. The data were collected from the official Elle UK website (https://www.elle.com/uk/) between 2022 and 2024, focusing exclusively on materials tagged under the “Beauty News” section. This selection criterion was applied to ensure thematic consistency and to reflect current trends in the British beauty industry.
The goal was to create a corpus that could serve as a representative sample of beauty-related media discourse in a contemporary lifestyle publication. As a result, the corpus includes 100 individual articles, stored in plain text (.txt) format. These articles were chosen for their relevance, recency, and adherence to common sub-genres in beauty journalism.
The thematic diversity of the analyzed articles demonstrates the multifunctional nature of beauty discourse. Articles often combine informative, persuasive, and evaluative functions, making them fertile ground for stylistic and lexical analysis. For instance, the frequent use of imperative structures such as "Try this now" or "Don’t miss out" reflects a marketing-driven intent to influence reader behavior. At the same time, descriptive adjectives like "hydrating", "glowy", or "age-defying" serve a dual role of informing and enticing the audience. This blend of functions exemplifies the genre's hybrid register, where advertising merges seamlessly with journalistic narration.
Lexical choices within the corpus reveal a marked preference for buzzwords and neologisms, particularly in trend-focused articles. Terms such as "skinimalism", "slugging", and "glass skin" indicate how beauty journalism adopts and disseminates niche vocabulary from social media and industry insiders. These coinages often lack formal dictionary definitions but gain widespread recognition through repeated media exposure. Their use signals in-group awareness, positioning the reader as someone familiar with current beauty trends. Additionally, the presence of technical jargon—such as "niacinamide", "peptides", and "hyaluronic acid"—suggests an effort to lend scientific credibility to product claims.
Stylistically, the texts display a preference for a conversational tone, marked by second-person address and rhetorical questions (e.g., "Struggling with dull skin? We've got you covered!"). This strategy creates a sense of intimacy between writer and reader, aligning with the genre’s goal of building brand trust and consumer engagement. Furthermore, narrative elements—such as personal anecdotes from celebrities or beauty editors—are commonly integrated into product descriptions. These narratives function not only to entertain but also to validate product efficacy through relatable storytelling. The interplay of these features highlights the dynamic and multi-voiced character of beauty media texts, reinforcing their value in corpus-based stylistic inquiry.
In the process of corpus construction, particular attention was paid to ensuring data integrity and genre consistency. Articles were selected from the "Beauty News" section of the official Elle UK website to maintain a coherent thematic focus. Each article underwent a thorough pre-processing phase, during which non-linguistic elements such as advertisements, embedded videos, and unrelated hyperlinks were excluded. The aim was to retain only linguistically relevant content, including titles, subheadings, and article body text, thereby enhancing the overall cleanliness and authenticity of the corpus.
After cleaning the texts, metadata was systematically recorded in a separate spreadsheet. This included the article title, author name (if available), and publication date. Such contextual tagging enables the corpus to be filtered and analysed according to temporal parameters or authorship patterns, facilitating deeper discourse analysis. The organisation of metadata in tabular format further supports cross-referencing with linguistic findings from the corpus, such as examining how the use of evaluative adjectives or persuasive verbs evolved over time.
The final corpus, comprising 100 cleaned articles, was imported into AntConc 3.5.8 for analysis. The software was used to generate word frequency lists, identify key words in context (KWIC), and detect collocational patterns of frequently used beauty-related terms (e.g., hydrate, radiance, plump). Through concordance analysis, recurring lexical items and stylistic tendencies were identified, reflecting the promotional and aesthetic focus of the beauty discourse. These findings provided insights into the discursive strategies employed in beauty journalism and informed the broader interpretation of language use in lifestyle media.
To ensure a balanced representation in the corpus, careful attention was given to chronological and thematic diversity. The articles were systematically selected to cover three distinct years: 2022, 2023, and 2024, with approximately 33 articles from each year. This deliberate stratification aimed to capture the linguistic evolution of beauty discourse over time, reflecting changes in vocabulary, tone, and style. By maintaining this temporal balance, the corpus can be analyzed to observe shifts in language patterns, such as the rise of new beauty trends or shifts in terminology reflecting broader cultural and societal changes.
Moreover, the corpus includes a wide range of sub-genres, ensuring that the content reflects a variety of textual formats commonly found in beauty journalism. For example, long-form articles like “The Future of Sustainable Beauty” provided an in-depth, analytical approach to beauty topics, showcasing formal, structured language and expert insights. In contrast, list-based formats, such as “5 Foundations That Work on Every Skin Type”, utilized a more concise and persuasive writing style, focusing on practical, accessible advice for readers. The inclusion of diverse textual genres allows for a more comprehensive understanding of how beauty topics are presented across different media structures.
In addition to written articles, the corpus incorporated interview-based content, which introduced a more conversational and spoken quality into the data. Pieces like “Charlotte Tilbury Talks Glow and Glam” offered direct quotations and dialogical exchanges that contrasted with the more formal writing style of typical beauty articles. This approach enabled a comparison between the written and spoken modes of beauty discourse, revealing how language adapts when transitioning from a structured, edited format to a more fluid and interactive one. By including these variations, the corpus provides a fuller picture of beauty media as a multi-voiced, multifaceted field.
The average length of the articles ranged from 500 to 800 words, which provided a balanced and manageable size for the corpus. This range was selected to ensure that each article was long enough to contain sufficient linguistic data for analysis, while still being short enough to maintain focus on key patterns without overwhelming the corpus with excessive detail. With a total corpus size of approximately 65,000–75,000 words, the dataset was large enough to allow for the extraction of statistically significant patterns while ensuring that the analysis remained feasible within the project’s scope. The choice of article length thus helped to strike a balance between data richness and analytical manageability, ensuring that the linguistic data captured was both diverse and representative of the broader beauty discourse.
In cases where articles exceeded 1,200 words, they were either excluded or truncated to maintain consistency across the dataset. This length normalization ensured that longer articles, which might contain more elaborate explanations or additional topics, did not skew the keyword frequency distributions or collocational patterns. Truncating or excluding longer articles was essential in minimizing the bias that could arise from articles with disproportionate amounts of content, ensuring that the dataset reflected a more consistent linguistic profile across all articles. This practice helped in maintaining the overall statistical balance of the corpus, allowing for more accurate and reliable analysis of trends and patterns in language
17.05.2025 14:05-
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