Linguistic Patterns Research Articles

A Sociopragmatic Study of Saudi Arabic Address Terms: Implications for Cross-Cultural Communication

Many studies have investigated terms of address, primarily focusing on Western languages. However, few studies have explored this topic in varieties of the Arabic language. This study examines the use of terms of address in Saudi Arabic, investigating the terms used by Saudi men and women, the effect of the addressees’ age and gender on the selection of address terms, and the linguistic patterns employed by Saudis when addressing each other. Twelve participants from two large cities in Saudi Arabia took part in the study. A semi-structured interview featuring 12 different social situations was conducted to answer the research questions. The interviews were conducted in the Saudi Arabic dialect to elicit authentic and realistic responses. The data were analyzed thematically to identify recurring patterns and trends in the use of address terms. Seven categories of address terms were identified. Furthermore, the age and gender of the addressees were found to influence the choice of terms. Moreover, five linguistic patterns were used by the participants to address each other. These findings provide important insights into professional settings where understanding suitable address terms can lead to more productive and culturally acceptable communication.

Detecting Fake News Using Data Analytics: A Systematic Literature Review And Machine Learning Approach

This study systematically reviews advancements in fake news detection techniques, examining methodologies and frameworks across machine learning, natural language processing, social network analysis, and multimodal approaches. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, resulting in the selection and evaluation of 254 research articles. The findings reveal that supervised learning models, such as Support Vector Machines, Decision Trees, and Naïve Bayes, have shown strong performance in text-based fake news classification, particularly when feature selection is optimized. Deep learning models, including CNNs, RNNs, and transformers, have further advanced detection accuracy by capturing complex linguistic patterns, though challenges with computational demands and model interpretability remain. In scenarios with limited labeled data, unsupervised learning models and semi-supervised approaches offer adaptability, with clustering, anomaly detection, and iterative self-labeling proving effective for evolving misinformation. Additionally, cross-disciplinary approaches, integrating insights from psychology, sociology, and network science, enhance detection models by accounting for user behavior, emotional appeal, and social conformity in the spread of fake news. Case studies from collaborative projects underscore the potential of interdisciplinary efforts to develop robust, adaptable detection frameworks. This review concludes that effective fake news detection requires a multifaceted approach, combining technical advancements with social science insights to address the complexity and adaptive nature of misinformation. The study emphasizes the need for continued research in hybrid models and adaptive, real-time detection solutions to strengthen defenses against fake news in diverse digital environments.

Behaviors and Perceptions of Human-Chatbot Interactions Based on Top Active Users of a Commercial Social Chatbot

Natural language processing is enabling machines to communicate with humans naturally, yet the dynamics of extended user-chatbot interactions remain much unexplored. This study characterizes the conversational styles, demographics, psychologies, and emotional tendencies of the most active users (i.e., top 1% by message count) of a commercial chatbot platform (SimSimi.com), whom we refer to as superusers. We analyzed the linguistic patterns and topics of 1,988,971 messages written by 1,994 superusers over a period of three years. We further surveyed 76 users to observe their emotional dispositions and perceptions towards the chatbot. We find that SimSimi superusers empathize and humanize the chatbot more than less active users, and they show a higher tendency to share personal and negative feelings. Our findings suggest that chatbots require new design considerations for users who are vulnerable due to their high anthropomorphism and openness toward machines. Our work also shows that chatbots should have functions to offer social support when necessary.

Analysis of high-frequency errors and linguistic patterns in EFL medical students’ English writing: Insights from a learner corpus

BackgroundThe perceived language barrier in English is said to hinder, and in certain instances, impede the global dissemination of knowledge, including medical information, to non-native English speakers within medical institutions. As English for medical purposes instructors, we contend that the issue persists in medical universities across various EFL contexts. Medical students face the challenge of presenting their research findings in English for international journals and conferences. Given this, the present research study aimed to compile a comprehensive catalog of high-frequency errors and examine them in recurring linguistic patterns commonly found in the writing of Iranian medical students.MethodsIn conducting the present study, we developed a learner corpus of 1,040 essays (339,040 words and 18,235 sentences in total). Through using the results obtained from Wordsmith Tools 8 and sifting the leaner corpus, we identified 11 high-frequency errors and five commonly used linguistic patterns.ResultsOnly five out of 11 high-frequency errors account for 61% of the total number of errors. Results also showed that a majority of errors were of grammatical nature. In this regard, cohesion and cohesive devices (16%) were the most prevalent errors followed by omission/misusing of articles/determiners (14%). Additionally, results showed that discourse markers were extensively used in the corpus (22.07%), followed by hedges (11.42%).ConclusionsThe outcomes of this study are expected to assist English for medical purposes instructors in designing focused lesson plans and classroom activities. Ultimately, these efforts might contribute to enhancing medical education in non-English speaking universities.

‘Harsh’ SoMa vs ‘Beige’ Castro: The cross-modal construction of contrasting femininities in queer San Francisco

The study of gender in sociolinguistic variation has long concerned the ways linguistic variables pattern with binary gender identities like male and female, with the implicit assumption that masculine individuals will use variants that index masculinity, and feminine individuals will use variants that index femininity. The emerging field of trans linguistics has built upon insights from queer linguistics and challenged the idea that there must be a one-to-one correspondence between linguistic variants and gender identities. Studies of transgender speakers have illuminated that indexing gender is a process of bricolage, where multiple variables collaborate, and only one needs to index gender in a particular way for the speaker's overall semiotic construction to carry that gendered meaning. Here, I investigate cross-modal bricolage, by exploring how visual gender presentation and three different linguistic variables come together to construct contrasting feminine styles among San Francisco drag queens. Linguistic patterns and aesthetic choices illuminate that bricolage involves a semiotic division of labor, in which only some signs (or modalities) need to index gender to give the overall style that gendered meaning. Concurrently, other signs can contribute qualia to a style's overall qualic cloud, that distinguish the style from others in the semiotic landscape. This exploration illuminates the role of the body in conditioning the indexical potential of linguistic signs, destabilizes monolithic essentializations of trans linguistic practice, and acknowledges the varied ways that gender non-normative identity can manifest across communities.

Application Of the National Corpus Of The Kazakh Language In Linguistic Research

National corpora are essential tools in modern linguistics, offering valuable data for analyzing language structures and their connection to the world.The primary goal of this study is to enhance the perception and analysis of the Kazakh language. It introduces a methodological approach that deepens the understanding of the structure and features of Kazakh.The research utilizes frequency analysis, syntactic parsing, and statistical methods to examine Kazakh language texts. The results demonstrate that the National Corpus provides extensive data for studying linguistic patterns. Frequency analysis identifies the most common words and phrases, while syntactic analysis uncovers patterns and connections between words. The study also highlights the variation in word usage and constructions, showing the language's adaptability to modern communication.The findings are applicable to lexicography, semantics, and syntax studies of the Kazakh language. Additionally, they offer practical benefits in language teaching, lexicographic resource creation, machine translation, and natural language processing.In conclusion, the research provides valuable tools for in-depth analysis of the Kazakh language, with broad applications across linguistic studies and practical fields.

Analyzing violent attacker texts: examining ideologies through machine learning-assisted topic modeling

ABSTRACT The rise in extremist violence is a global concern, prompting advancements in threat assessment. This study applies machine learning to dissect publicly available texts from violent attackers with white supremacist, incel, and political extremist ideologies, as well as from those driven by personal grievances. Through natural language processing, the research identifies specific linguistic patterns and themes inherent to each group and explores the intersections among them. It uncovers shared narratives of disenfranchisement and aggression alongside unique ideological signifiers. Additionally, it identifies themes unique to each group, offering a clear thematic distinction between ideologies. These insights contribute to a more accurate identification of radicalization indicators, supporting the development of intervention strategies tailored to the ideological nuances of each group. Enhanced threat assessment tools informed by this research can improve the precision of prevention efforts by law enforcement and mental health professionals, reflecting the complex realities of extremist motivations and actions.

A knowledge-Aware NLP-Driven conversational model to detect deceptive contents on social media posts

The widespread dissemination of deceptive content on social media presents a substantial challenge to preserving authenticity and trust. The epidemic growth of false news is due to the greater use of social media to transmit news, rather than conventional mass media such as newspapers, magazines, radio, and television. Humans' incapacity to differentiate among true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and government credibility. Using combination of advanced methodologies, Deep learning (DL) methods, and Natural Language Processing (NLP) approaches, researchers and technology developers attempt to make robust systems proficient in discerning the subtle nuances that betray deceptive intent. Analysing conversational linguistic patterns of misleading data, these techniques’ purpose to progress the resilience of social platforms against the spread of deceptive content, eventually contributing to an additional informed and trustworthy online platform. This paper proposed a Knowledge-Aware NLP-Driven AlBiruni Earth Radius Optimization Algorithm with Deep Learning Tool for Enhanced Deceptive Content Detection (BER-DLEDCD) algorithm on Social Media. The purpose of the BER-DLEDCD system is to identify and classify the existence of deceptive content utilizing NLP with optimal DL model. In the BER-DLEDCD technique, data pre-processing takes place to change the input data into compatible format. Furthermore, the BER-DLEDCD approach applies hybrid DL technique encompassing Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) methodology for deceptive content detection. Moreover, the BER approach has been deployed to boost hyperparameter choice of the CNN-LSTM technique which leads to enhanced detection performance. The simulation outcome of the BER-DLEDCD system has been examined employing benchmark database. The extensive outcomes stated the BER-DLEDCD system achieved excellent performance with the accuracy of 94 %, 94.83 % precision, 94.30 % F-score with other recent approaches.

Seven million tweets of violence: gendered analysis of Black women representation on social media platforms

ABSTRACT This paper investigates the gendered linguistic patterns of tweets discussing state-sanctioned and vigilante violence against African American women. The paper considers representations of Black women and Black death as engaged on the social media platform formerly known as Twitter. We utilize the theoretical frameworks of Patricia Hill Collins’ (2022. Black Feminist Thought, 30th Anniversary Edition: Knowledge, Consciousness, and the Politics of Empowerment. London: Taylor & Francis) conceptualization of Black feminist epistemology and controlling images and Kimberle Crenshaw’s (2018. “Demarginalizing the Intersection of Race and sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics [1989].” Feminist Legal Theory 1: 57–80) intersectionality to understand the ways that Black women are discussed, remembered and advocated for on social media platforms in relation to their Black men counterparts. The analysis of over seven million tweets utilizes both qualitative content analysis and computational tools. The paper’s findings concluded that there were differences related to passive and active language based on the gender of the victim of state-sanctioned or vigilante-race-based violence, as well as homophily, specifically name events most frequently used within the context of the state-sanctioned killing of Black women versus Black men.

Corpus-Based Studies on Translator’s Style: Retrospect and Prospect

The past 20 years have seen significant advances in the corpus-based studies on translator’s style, an important subfield of corpus-based translation studies. However, there is still a lack of a coherent and clear understanding of the research models, objects of study, and methodologies. This paper classifies different research models, reviews the representative achievements of the studies, analyzes the limitations of current research, and provides suggestions for future studies. This paper argues that the existing research methods need to be expanded, the objects should be enriched, and the research model requires further investigation. Studies in the future should expand the current research methodology from primary quantitative studies to semantic, pragmatic and socio-cultural parameters. Greater focus should be placed on analyzing the translator’s style as demonstrated by linguistic features, such as language collocation and semantic prosody, as well as the subtext of the translated work, and non-linguistic features, such as the translator’s choice of material for translation. Furthermore, attention must be paid to the examination of both the preferred linguistic patterns that the translator uses to reproduce the source text and the recurrence of these patterns across the translator’s other translations of works by different authors.

Linguistic Characteristics In The Research Background And Problem Statement In The Doctoral Thesis Of Arabic EFL Students In Malaysia

This study explores the linguistic features within the move structures of the research background and problem statement sections in Chapter One of PhD theses written by Arab students in Malaysia. The analysis focuses on four key language elements: verb tense, modality, voice (active and passive), and personal pronouns. A corpus of 30 theses was analyzed to determine common linguistic patterns, revealing significant insights into the language choices of these academic writers. The study employs concordance and hand-tagged analysis to examine verb tense and voice, with manual verification for modality and personal pronouns to ensure accuracy. Eight distinct verb tenses were identified, with the present simple tense being the most frequently used, representing 55.30% of the total instances. This indicates a preference for focusing on current knowledge and ongoing research. In contrast, the past simple and future simple tenses were used to describe past methodologies and future implications, respectively. Modal auxiliaries play a crucial role in conveying sentiments and opinions. The study identified seven modal verbs, with 'can' being the most commonly used, suggesting a focus on possibility over obligation. The analysis of voice patterns revealed a higher occurrence of active voice (79.66%), especially in moves that conveyed up-to-date knowledge. In contrast, passive voice was more common in methodological descriptions, indicating a focus on the actions or subjects rather than the actors. The study also examined the usage of personal pronouns, finding a higher frequency of third-person pronouns, suggesting a more objective narrative style. These findings offer valuable insights into the linguistic choices of Arab EFL students in academic writing, providing a deeper understanding of their communicative intentions and alignment with academic discourse conventions. The results can guide future research on linguistic patterns in academic writing and help educators support EFL students in improving their writing skills.

Breach of pacta sunt servanda: A corpus-assisted analysis of newspaper discourse on the AUKUS agreement

The AUKUS agreement,11The abbreviation AUKUS stands for ‘Australia, the United Kingdom, and the United States’. a strategic pact between Australia, the United Kingdom, and the United States, primarily aimed to facilitate Australia's acquisition of eight nuclear-powered submarines from the US and Britain. This agreement led to the abrupt termination of a previous contract with France's state-owned Naval Group. This article examines the language used in media coverage of the AUKUS agreement in newspapers from various Anglophone and Asian countries. Employing a combination of Sentiment Analysis (Crossley et al., 2017) and Corpus-Assisted Discourse Studies (Partington, 2013; Gillings et al., 2023), we focus on identifying key linguistic patterns, themes, and the sentiment embedded in the discourse. Our findings indicate a general positive assessment of AUKUS in the Anglophone media, contrasted with negative portrayals in Chinese publications. Moreover, the analysis of linguistic components such as adjectives, nouns, and verbs reveals underlying complexities and conflicting viewpoints within the Anglophone discourse itself. By applying Corpus-Assisted Discourse Studies, we uncover the contextual and linguistic factors that shape these diverse perspectives.

More spoken or more translated?

Abstract This article explores the features of simultaneous interpreting (SI) from a multidimensional (MD) analysis perspective (Biber 1988), drawing on a newly built comparable intermodal corpus, the LegCo+ corpus. The corpus incorporates Cantonese speeches that are both interpreted and translated into English, as SI and written translation (WT), respectively. Additionally, a third English corpus consisting of English native speeches (NS), without mediation, serves as a benchmark comparison. We aim to examine the extent of similarities and differences between SI, NS and WT in terms of the linguistic patterns they display. Our findings show that: (1) SI is a hybrid language mode, exhibiting features that lie between those of non-mediated spoken language and mediated written language; (2) in terms of its spoken nature, SI resembles NS in certain dimensions where typical features are associated with orality, suggesting a strong modality effect; and (3) in terms of its mediated status, SI demonstrates similarities with WT, despite their perceptibly distinct modalities, pointing to a potential mediation-specific effect. These empirical findings emphasize the necessity of understanding the multidimensionality inherent in interpreted language.

Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry

This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapters reflecting higher readability and more varied sentence structures, while others lean toward simpler linguistic patterns. The findings also highlight thematic variation across chapters, as observed in the distribution of named entities, which contributes to understanding the model’s handling of different contextual content. The research suggests that while GPT-4o maintains a consistent style in its generated text, there are distinguishable characteristics that may serve as indicators of machine authorship. This provides valuable insights for stylometric analysis, authorship attribution, and the identification of machine-generated texts in various contexts. Future research could extend this work by exploring deeper stylometric features, conducting cross-model comparisons, and developing advanced authorship detection algorithms tailored for AI-generated content. Moreover, the ethical implications of stylometric analysis in the context of AI-generated texts warrant further investigation, particularly as machine-generated content becomes increasingly prevalent across different domains.

Deep Learning for Stylometry and Authorship Attribution: a Review of Literature

The application of deep learning techniques to stylometry and authorship attribution has emerged as a promising frontier in computational linguistics, offering new possibilities for understanding literary style and authorship in both historical and contemporary contexts. This review paper synthesizes recent advances in the use of deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer architectures, for identifying and attributing authorship based on stylistic analysis. We examine the effectiveness of these models in comparison to traditional statistical methods, highlighting their ability to capture complex linguistic patterns and nuances that are often overlooked by conventional approaches. Furthermore, we explore how deep learning models handle challenges such as multilingual texts, limited data, and variations across genres and periods. This review also addresses the interpretability of neural networks in the context of stylometry and discusses the implications of these methods for fields ranging from literary studies to digital forensics. By providing a comprehensive overview of the current state of research, this paper identifies key trends, challenges, and future directions for the application of deep learning to stylometry and authorship attribution.

Corpus-based critical discourse analysis of NFT art within mainstream art-market discourse and implications for the political economy of digital art

The recent advent of non-fungible tokens (NFTs) has profoundly disrupted the contemporary art market, but their status remains contested. While advocates tout the democratising potential of NFTs, critics dismiss them as a speculative bubble. This study elucidates the position of NFT art within mainstream art market discourse through an interdisciplinary analysis synthesising critical discourse analysis, corpus linguistics, and the political economy of art. Examining three key 2023 art market reports furnishes empirical insights into how influential institutions frame and valorise NFT art. Linguistic patterns reveal NFTs’ increasingly solid yet variable positioning across art market segments. NFT art appears well-integrated into mainstream discourse but remains emergent at the high end. The analysis reveals a complex picture, with the data showing both positive institutional framing and valorisation of NFT art, as well as the identification of ethical tensions and concerns around issues of accessibility, diversity, and sustainability within the NFT art market discourse. We find NFT art simultaneously amplifies and challenges existing hierarchies, creating new opportunities counterposed by risks.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.

Pre-Trained Language Model Ensemble for Arabic Fake News Detection

Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates transformer-based language models for Arabic FND. While transformer-based models have shown promising performance in various natural language processing tasks, they often struggle with tasks involving complex linguistic patterns and cultural contexts, resulting in unreliable performance and misclassification problems. To overcome these challenges, we investigated an ensemble of transformer-based models. We experimented with five Arabic transformer models: AraBERT, MARBERT, AraELECTRA, AraGPT2, and ARBERT. Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. The results of this study demonstrate the effectiveness of ensemble models in significantly boosting the baseline model performance. An important finding is that ensemble models achieved excellent performance on the Arabic Multisource Fake News Detection (AMFND) dataset, reaching an F1 score of 94% using weighted averages. Moreover, changing the number of models in the ensemble has a slight effect on the performance. These key findings contribute to the advancement of fake news detection in Arabic, offering valuable insights for both academia and the industry

Exploring linguistic features and user engagement in Chinese online mental health counseling

With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of which problem disclosures and social support on these platforms can attract more user attention and engagement. Previous research has primarily focused on social media forums. Our work concentrates on the professional mental health platform, intending to understand the linguistic features present in posts that promote user engagement and interaction. We employ text mining and deep learning techniques to analyze posts consisting of 22,250 questions from help-seekers and 78,328 answers providing social support extracted from the Chinese online mental health counseling platform. Initially, we analyze the high-frequency words and topics of the questions and answers to gain insights into the primary focal points and the range of topics covered in these posts. The results indicate that work-related issues are the most concerning and troublesome for help-seekers, and the topics that users follow are approximately 8 types, including growth, family, in-love, marriage, emotions, human-relations, behavioral-therapy and career. Subsequently, we analyze the language usage in question-and-answer posts with different engagement from three aspects: vocabulary categories, linguistic style matching, and language modeling, aiming to identify which linguistic features can attract more user attention and engagement. The results reveal that high-engagement answer posts exhibit a higher degree of linguistic style matching with the corresponding questions, and the use of vocabulary categories also influences the attention and engagement of the posts. By exploring the linguistic features and patterns displayed in posts with different levels of engagement on the professional online mental health platform, this study offers deep insights into user behavior and the factors that impact counseling effectiveness on the platform and provides valuable knowledge for understanding effective user interactions and engagement.

Unsuccessful and Successful Selfies in the Presentation of Anthropomorphic Metaphors in English-Language Digital Discourse

Introduction. This article is devoted to the analysis of the role of anthropomorphic metaphors in modern digital vocabulary using the example of the word «selfie». The relevance of the work is due to the growing interest of the scientific community in the evolution of digital vocabulary, which includes a variety of lexical units and expressions that reflect modern realities and sociocultural changes.Methodology and sources. The methodological study is based on the productivity of anthropometric cognitive tools in the formation of figurative expressions in English, with theinclusion of the word «selfie». This study is related to the conceptual theory of metaphors developed by famous linguists G. Lakoff and M. Johnson.Results and discussion. In the course of this study, attention is drawn to some expressive phrases associated with the concept of «selfie». Adjectives with negative and positive connotations were selected, then, based on the analysis of empirical material, structures were identified that architecturally represent the «adjective + selfie» model. On this basis, in the context of selfie, negative and positive metaphorical images were identified, which were analyzed in examples.Conclusion. Consideration of the semantics of the expressions under study allows us to better understand how digital vocabulary shows sociocultural trends and changes in society, provides a look at the evolution of language in the era of digitalization and emphasizes the role of digital vocabulary in the formation of new linguistic patterns that reflect the dynamics of sociocultural changes, emphasizing the role of digital vocabulary in the modern world.