Linguistic Patterns Research Articles

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo.

This study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic. The analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16-24 and the linguistic characteristics of the posts. The study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of "Affect" showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as "Negative emotion" and "Sadness" also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress. The findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment.

Parameterization of manipulative media discourse: possibilities and problems of automatic diagnosis

Abstract The issue of quantitative measurement and automatic processing is a significant problem in determining the markers of the manipulative potential of media texts, since linguistic indicators are the basis of machine parameterization. The purpose of the research is to analyse the possibilities of the main language parameters of the manipulativeness of media discourse, which can be identified using machine learning. To achieve the research goals, the following methods were used: system, content analysis, computer modelling, and comparative. The results of the article determined that such language indicators as use of the subjunctive mood of verbs, capital letters, high frequency of use of the ‘not’ particle, punctuation marks, questions, or exclamations of a rhetorical nature, use of quotation marks for the purpose of irony, double negative sentences, use of the word ‘no’, and verbal structures calling to action act as computer classification parameters. In order to cover the above purpose, PYTHON software was implemented that allowed texts to be analysed and visualized in algorithmic and lexical-vocabulary ways. In addition, it was determined that by integrating the PYTHON tool, it became possible to use language transformation markers that formed linguistic patterns in the analysed text. The list of parameters for diagnosing manipulative texts is non-exhaustive, which emphasizes the possibility of machine measurement of the manipulative component of mass media discourse.

Social Phenomena Related to Fraudulent General Election Results in Indonesia 2024: Systemic Functional Linguistics Perspective

This research delves into the societal implications of fraudulent general election results in Indonesia, with a specific focus on the 2024 presidential election. Applying Systemic Functional Linguistics, the study examines the language use in elections, the repercussions of fraudulent activities on public trust, and the broader impact on Indonesian society. Through detailed data collection and analysis methods, the study uncovers linguistic patterns in fraudulent clauses, discourse strategies employed, and the manipulation of language to influence public sentiment. By identifying fraudulent clauses in news reports, the research sheds light on the intricate nature of election fraud and its historical context. The findings underscore the significant role of language in shaping perceptions and behaviors within the electoral system, emphasizing the need for valid data in understanding and addressing fraudulent practices. Ultimately, this study contributes to a deeper understanding of election fraud's complexities and its far-reaching consequences on Indonesian society.

Intellectualisation of Northern Sotho through English terminology adaptation

This study aimed to investigate and propose a pragmatic approach in the adaptation of English terminologies for scientific purposes into Northern Sotho. This is necessary because of the lack of terminologies to describe and define scientific phenomena in the language. Languages are constantly evolving, and speakers drive their evolution. The study aimed to overcome the scientific terminology development challenge for Northern Sotho by analysing existing data and using corpus linguistics as a method. The Multilingual Natural Science and Technology Dictionary Grade 4–6 (2013) was used to provide illustrative examples and clarify linguistic complexities in scientific terminology. The study revealed the complexity of the linguistic adaptation of Northern Sotho and the challenges and pitfalls linked to the integration of borrowed English terminology into academic discourse, encompassing apprehensions concerning accuracy, clarity and cultural appropriateness. Furthermore, the findings indicated that the puristic term adaptation approach tends to be perplexing.Contribution: This study contributes to the intellectualisation and revitalisation of Northern Sotho by enhancing the language and equipping its speakers to engage more efficiently in scientific contexts, illuminating intricacies and potential misrepresentations inherent in the process of adaptation. Moreover, the research underscored the significance of employing adaptation strategies that are suitable for the Northern Sotho context and are consistent with linguistic patterns and semantics.

THE USE OF VERSATEXT IN TEACHING ENGLISH FOR SPECIFIC PURPOSES AT MEDICAL UNIVERSITIES

This article explores the integration of data-driven learning tools, as exemplified by VersaText, into the process of teaching English for Specific Purposes in medical education. The study investigates the efficacy of VersaText’s functions (Word Cloud, Concordance, and Profiler) in enhancing language acquisition and proficiency development among medical students. Through a series of classroom-based activities and exercises, students engage with authentic medical texts, analyze linguistic patterns, and develop a deeper understanding of specialized medical terminology. This study discusses the benefits of integrating open-access materials sourced from PubMed and other databases with VersaText's features, further enriching the learning experience by exposing students to real-world medical discourse. By incorporating such authentic materials, educators can enhance students' awareness of current research literature and reinforce their understanding of medical terminology within a contextually relevant framework. The pedagogical implications of using VersaText in ESP teaching are discussed, highlighting its potential for fostering active engagement, collaborative learning, and critical thinking skills. Furthermore, the article emphasizes the need for ongoing research to further refine and expand the use of data-driven learning tools in ESP teaching methodologies. This study contributes to the growing body of literature on technology-enhanced language learning and provides valuable insights for educators seeking to optimize ESP instruction in various professional contexts.

Linguistic patterning of laughter in human-socialbot interactions

Laughter is a social behavior that conveys a variety of emotional states and is also intricately intertwined with linguistic communication. As people increasingly engage with voice-activated artificially intelligent (voice-AI) systems, an open question is how laughter patterns during spoken language interactions with technology. In Experiment 1, we collected a corpus of recorded short conversations (~10 min in length) between users (n = 76) and Amazon Alexa socialbots (a voice-AI interface designed to mimic human conversational interactions) and analyzed the interactional and pragmatic contexts in which laughter occurred. Laughter was coded for placement in the interaction relative to various speech acts, as well as for phonetic patterning such as duration and voicing. Our analyses reveal that laughter is most commonly found when the content of Alexa’s speech is considered inappropriate for the discourse context. Laughter in the corpus was also largely short in length and unvoiced– characteristics which are commonly associated with negative social valence. In Experiment 2, we found that a separate group of listeners did not distinguish between positive and negative laughter from our dataset, though we find that laughs rated as more positive are also rated as more excited and authentic. Overall, we discuss our findings for models of human-computer interaction and applications for the use of laughter in socialbot conversations.

Family Language Policy in the minority and migration contexts of Cyprus

This article investigates the Family Language Policy (FLP) of minority and immigrant families in Cyprus. The participants in our study were 30 immigrant and minority families in Cyprus with various first language (L1) backgrounds, namely, Russian, Georgian, Ukrainian, Bulgarian, Romanian, English, and Arabic and minority speakers of Armenian, Lebanese, and Pontic Greek. The data were collected via written questionnaires and oral, in-depth, semi-structured interviews with a focus on the FLP, cultural and linguistic identities, heritage language (HL) (incomplete) acquisition, attrition, use maintenance and transmission, linguistic interaction patterns and the material culture at home. The analysis of the data revealed that minority and immigrant speakers in Cyprus had hybrid language and cultural identities, different perceptions regarding citizenship, inclusion, and belonging, as well as the attrition, use, maintenance, and transmission of the HL, which reflected their FLPs, agency, practices, and negotiations. They attempted to assimilate into the target society but also had strong links to the community of residence, to their L1 country, and to their heritage or home language. The participants also employed hybrid language practices, as they used mixed/multiple languages both in the home and outside it. Overall, they had a positive attitude towards multilingualism in Cyprus.

Spam Review Detection using Machine Learning

Customer opinions play a vital role in buying decisions. These days most customers post their opinions about products on blogs, e-commerce sites, review sites, and social networking sites. The above information is consumed by business or corporate organizations, as they are eagerly interested in analyzing consumer views about their products, services, and support. As people buy products after reading the reviews, the kind of reviews that a product attracts are of concern to the sellers. This means that a positive review on the product would bring in sales and a negative one would reduce them. The project leverages Natural Language Processing (NLP) techniques and supervised learning algorithms to build a robust spam review detection system. The system is trained on a dataset comprising genuine and spam reviews, and it extracts various features from the textual content of reviews, such as sentiment analysis, linguistic patterns, and semantic meaning.

È un complimento?, Ist das ein Kompliment für dich?. Implicit Compliments in Italian and German Social Networks

Compliment is one of the most widely studied speech acts, as the extensive literature devoted to this topic shows. However, researches mainly focus on formulaic complimenting, probably because explicit compliments are far more frequent than the implicit ones. The present contribution aims to investigate implicit compliments, i.e., those where hearers «need to infer the corresponding implicature for their interpretation» (MAÍZ-ARÉVALO, 2012, p. 983; cf. also BOYLE, 2000), in a corpus of digital exchanges in Italian and German social networking sites, Instagram and WhatsApp. Firstly, the study examines and compares, from both a qualitative and quantitative point of view, the linguistic patterns used to express implicit compliments in the two languages; then, through a detailed analysis of the reactions of compliments’ addressees it explores how these speech acts are perceived by Italian and German social network users and what response types are mainly used by the complimented person in order to react to the implicit positive evaluation.

Deciphering Speculative Discourse in MH370 News Coverage: A Corpus-based Critical Discourse Study of Selected Local and Foreign News Outlets

This research explores how selected local and foreign news outlets depict speculative discourse in their coverage of the MH370 tragedy through corpus-based critical discourse analysis. It begins by contextualising the prevalence of speculative language in modern journalism and its potential impact on journalistic principles, drawing on previous research to highlight the rise of interpretive journalism and its association with speculative reporting. The MH370 incident serves as a case study, revealing how imbalanced news coverage has led to the emergence of rumours, speculation, and conspiracy theories, particularly in the shifting narrative surrounding the Malaysian government's response. The study focuses on linguistic patterns of speculative discourse, employing Wiebe et al.'s (2001) framework for classifying speculative language to analyse news headlines and reports from selected local and foreign news outlets. By examining the presence and utilisation of speculative language, the study sheds light on how journalists convey uncertainty and speculation in their reporting, influencing public perception and understanding of the MH370 tragedy. The findings confirm the widespread presence of speculative discourse in both local and foreign news coverage of the MH370 tragedy, with journalists from both spheres employing subjective interpretation in their reporting. The analysis highlighted a significant usage of hedging strategies such as modal verbs expressing probability, particularly in foreign headlines, which exhibited less certainty than local ones. Both local and foreign headlines commonly featured hypothetical questions and unnecessary quotation marks, amplifying uncertainty in reporting on MH370. Notably, 64% of MH370 news headlines were identified as speculative, emphasising the prevalence of speculative language in influencing public perception and encouraging contemplation of multiple assumptions and uncertainties surrounding the tragedy. Overall, this research contributes to a deeper understanding of how speculative discourse shapes news coverage and public discourse surrounding major events like the MH370 tragedy.

Characterizing Developers' Linguistic Behaviors in Open Source Development across Their Social Statuses

Open Source Software (OSS) development has attracted numerous developers. As a typical complex sociotechnical system, an OSS project often forms a hierarchical social structure where a few developers are elite while the rest are non-elite. Differences in social status may result in distinct language use behaviors in interpersonal communication. Characterizing such behaviors is critical for supporting efficient and effective communication among developers with different social statuses. This study empirically compared elite and non-elite developers' language behaviors in their communication. We compiled a corpus of - 216,000 discourses collected from 20 large projects on GitHub. We investigated the linguistic differences in three aspects, namely, linguistic styles and characters, main concerns, and sentence patterns. Our findings reveal that elite and non-elite developers showed different linguistic patterns and had different concerns in their discourses. Their discourses also reflect the variation of the main focuses in the development process. Furthermore, elite and non-elite developers exhibited noticeable patterns in their linguistic behaviors in accordance with their roles and corresponding divisions of labor in the production process, no matter which semantic contexts. These findings provide implications for supporting communication that crosses social statuses in OSS development.

Computer-Mediated Communication and Orthographic Competence: A Linguistic Pattern Analysis among Senior High School Students

Computer-Mediated Communication and Orthographic Competence: A Linguistic Pattern Analysis among Senior High School Students

Preparing Pharmacists for the Digital Age: How Pharmacy Courses are Adapting to Challenges and Opportunities

ObjectiveAs the digitalization of health accelerates, the fusion of pharmacy and informatics becomes crucial. Pharmacy education must adapt to equip professionals for this evolving landscape. This study aims to compare pharmacy curricula in Brazil and the United States of America, focusing on health informatics, to uncover challenges and opportunities in training pharmacists for the digital era. MethodsA cross-sectional, descriptive analysis was conducted on pharmacy curricula from leading Brazilian and American universities in early 2024. Two independent researchers collected data, concentrating on health informatics–related courses. Curriculum analysis used the n-gram technique for linguistic pattern identification in course descriptions. ResultsThe analysis included curricula from 147 Brazilian and 140 American institutions. American programs had more health informatics courses, with greater integration into pharmacy and higher workloads. Brazilian courses were fewer, less specialized, and less integrated with pharmacy practice. Bi-gram analysis showed that the United States emphasized pharmaceutical practice and technologies, while Brazil focused more broadly on public health. Challenges include Brazil’s slower integration of health informatics, impacting competitiveness. The study highlights opportunities to enhance curricula in both countries, emphasizing the importance of health informatics courses. ConclusionUS pharmacy programs are further developed by providing specialized, high-quality digital health education with extensive coursework, reflecting a curriculum aligned with digital advancements. This stands in stark contrast to Brazilian programs, which show a need for comprehensive curriculum revision to effectively prepare pharmacists for the digital age. This study underscores the urgency for global pharmacy education reform and its alignment with the rapid evolution of digital health.

Evolution of Natural Language Processing: A Review

Over the years, Natural Language Processing (NLP) has evolved dramatically, moving from early rule based systems to the current era dominated by advanced deep learning models. An overview of the significant turning points and patterns that have influenced the development of NLP is given in this study. In the early days of Natural Language Processing (NLP), rule based methods were the main focus. Linguists would manually create rules to analyze and comprehend human language. Although these systems were somewhat successful, they were unable to handle the complexity and unpredictability of spoken language. A major change was brought about by the introduction of probabilistic models and machine learning techniques with the emergence of statistical approaches. The development of methods like n gram models and hidden Markov models during this time allowed computers to handle linguistic patterns. Large scale linguistic resources like word embeddings and annotated corpora started to appear, which further accelerated the development of NLP. Machine learning algorithms have led to notable advancements in tasks such as machine translation, named entity recognition, and part of speech tagging. NLP has seen a revolution in recent years thanks to deep learning, which uses neural networks to learn intricate language representations. Sequential dependencies in language can now be better understood because of models like Long Short Term Memory Networks (LSTMs) and Recurrent Neural Networks (RNNs). The addition of attention mechanisms, as demonstrated by Transformer and other models, improves the model's ability to manage long range dependencies and perform better on a variety of NLP tasks. In the future, NLP will develop in ways that go beyond performance measurements, exploring interpretability, ethical issues, and the incorporation of multimodal data. As the area develops, it becomes increasingly important to eliminate biases, ensure ethical AI deployment, and improve user centric experiences. This introduction lays the groundwork for an in depth examination of the development of NLP, highlighting significant turning points, difficulties, and potential future directions in this vibrant and quickly developing subject.

Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform

This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin’s community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins’ online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

Transitivity and agency in Richard Jefferies’s rural essays: an ecostylistic analysis

Abstract This essay aims to provide an ecostylistic analysis of transitivity and agency in selected extracts from Richard Jefferies’s late nineteenth-century rural essays, which were collected in his so-called country books. Jefferies’s portrayal of the interconnection between humans and the natural environment has been variously described as “pantheistic”, a “pantheist revery”, or an “ecstatic communion”. The present study proposes to validate these claims by showing that Jefferies’s essays utilise patterns of transitivity to depict manifold relationships between human and non-human agents in placid landscape descriptions. These can be regarded as positive discourse praising the rural environment and humans’ entanglement with other animal and vegetable organisms. An in-depth scrutiny of the linguistic patterns of Jefferies’s texts, corroborated by the theoretical and methodological framework of (eco)stylistics and related disciplines (i.e. Systemic Functional Grammar), offers useful insights into the author’s ecosophy and holistic understanding of the human and non-human world. By conceptualising the various constitutive elements of the physical environment as active/agentive participants in particular types of processes, Jefferies gives special prominence to ecocentric, rather than anthropocentric positions.

An English Translation Error Correction System Based on Multi-Feature Fusion

Multi-feature fusion is an advanced computational technique that combines multiple sources of information or characteristics to improve the performance of a system or model. In various fields such as computer vision, natural language processing, and machine learning, this approach integrates diverse features like visual cues, textual data, and contextual information to enhance accuracy, robustness, and efficiency. Multi-feature fusion-based English translation represents a cutting-edge approach in natural language processing, where diverse linguistic features are amalgamated to refine the accuracy and quality of translation outcomes. By integrating various elements such as syntax, semantics, context, and linguistic patterns, this method significantly enhances the translation process, ensuring more nuanced and contextually precise results. This paper introduces an innovative English Translation Error Correction System (ETECS) founded on Multi-Feature Fusion, supplemented by a bi-gram Statistical Classification Neural Network (n-gramCNN). In response to the growing demand for accurate and efficient translation error detection and correction, this research endeavors to enhance the quality of English translations through a comprehensive and advanced methodology. Using a dataset of 1,000 English text samples with corresponding translations, ETECS demonstrates an impressive error detection accuracy of 95%. Upon identifying errors, the system achieves a correction accuracy of 92%, effectively rectifying grammatical, syntactical, and semantic inconsistencies. Moreover, ETECS outperforms baseline error correction systems by a significant margin, showcasing its superiority in handling complex translation errors. The bi-gram Statistical Classification Neural Network (n-gramCNN) contributes to this success by achieving an F1 score of 0.94, highlighting its effectiveness in classifying and correcting translation errors.

Analisis frekuensi, durasi, dan intensitas suara laki-laki dan perempuan bahasa Banjar menggunakan perangkat lunak Praat

This study aims to identify and analyze differences in the frequency, duration, and intensity of sound between male and female speakers of the Banjarnese. The research method is an experimental approach that utilizes a mixed method combining qualitative and quantitative analyses. Additionally, the study utilizes an instrumental approach, where the researcher employs the Praat program to analyze the frequency, intensity, and duration of sound produced by male and female speakers of the Banjarnese. Data were collected from one male and one female speaker of the Banjarnese. The analysis indicates that the frequency of sound produced by male speaker of the Banjarnese tends to be lower than that of female speaker. The duration of sound produced by male speaker is also shorter compared to that of female speaker. Furthermore, the intensity of sound produced by male speaker tends to be lower than that of female speaker. This study confirms the presence of significant differences in vocal characteristics between male and female speakers of the Banjarnese. These results suggest the existence of distinct linguistic patterns among genders within the Banjarnese community. Keywords: Banjarnese, Durations, Frequencies, Intensities, Praat Application

Thanking Expressions in Spoken Nigerian English: A Conversational Analysis

This paper investigates Conversational Routines of Thanking in Spoken Nigerian English. Everyday utterances are made through the use of language whether spoken or written. Thanking is prevalent basically in conversations. The selected speech acts for this study is thanking. Subjects for this study were drawn from three federal Universities from the South-South Region in Nigeria. They included: University of Uyo, University of Calabar and University of Port Harcourt. Data for this study were collected from three different speech interactions, namely: face-to-face conversations (FTFC), telephone conversations (TC) and vote of thanks (VOT). Data were collected through the use of a mini-tape recorder. Here, a Corpus of conversations were selected using purposive sampling method and surreptitiously collected from strategic points. This enabled the researcher to collect the Corpus in its original form. They were collected at different points where the students meet: lecture halls, hostels, canteens, relaxation centres among others. The framework for the analysis of this study is provided by the Speech Act theory. Frequency of the distribution between the male and female subjects, were calculated using the mean statistical method. This study found out that beyond the main functions of thanking, it also performs an array of other functions. Data have also revealed that the multilingual situation in Nigeria has affected the choices of language use, resulting in features such as code-mixing, slangs, Nigerian English-based pidgin, among others. This study has specifically shown that apart from classifying thanking as expressive speech act, it can further be classified as declarative, commissive and requestive speech acts. Therefore, this study concludes that with regard to thanking, the co-existence of English with other indigenous languages have given rise to linguistic patterns and forms, identifying Nigerian English as a distinct variety of the world Englishes. Keywords: Conversational routines, Thanking, Spoken Nigerian English, Nigerian Identity, Speech Acts.

Sociolinguistic Study of Language, Social Identity and Behavioural Patterns of Keke Operators in Lafia Metropolis

It is safe to state that sociolinguistics is the study of the language identity and behaviour of a speaker. Such a study of sociolinguistics will normally and apparently rely on the individual’s considerable conscious or unconscious degree of choice of the linguistic variables or forms they use in their daily speech. These choices could make essential contribution to the indexing of personal stance, identity, and communicative function of language use since the belief is that language(s) do not exist outside the societies in which they are used. ‘Sociolinguistic study of language, social identity, and behavioural patterns of keke operators in Lafia metropolis is a study of how tricycle operators, called ‘keke napep’ construct their social identity through their linguistic patterns to suit their job. The study also identified the linguistic behaviours and peculiarities of the group. Being a qualitative study, a primary approach was employed with the deployment of a participant observatory method. The ethnography of communication was applied as the basis of the framework of the study, which relied on first-hand observations of the behaviour of the keke operators. The framework itself was an adaptation of the Sapir-Whorf theory of relativity hypothesis concerned with social problems that invade the field of language. The findings showed that language served the function of social identity and aspiration, while the environment in which the group operate does influence their speech, behaviour, and attitude.