Media Texts Research Articles

The Changing Meanings of Political Terms and Their Reflection in Dictionaries

When observing media texts, one cannot help but notice that some political terms are used with a meaning widely different from that offered by mainstream middle-size dictionaries, which generally stick to the initial, sometimes etymological senses. Corpora analysis confirms the shift in senses and divergence of use. The aim of this paper is to analyse the meaning and use of two terms ubiquitous in the public sphere: fascist/fascism and liberal/liberalism, their original meanings, and subsequent changes in meaning and use. Though the limited space available for defining terms in a general explanatory dictionary makes it extremely difficult to reflect all ideological tinges and meanings, where a frequently used political term seems to have developed stable, different, even opposite meanings, this should be reflected by introducing ideological polysemy in dictionary definitions.

Enhanced earthquake impact analysis based on social media texts via large language model

Social media aids disaster response but suffers from noise, hindering accurate impact assessment and decision making for resilient cities, which few studies considered. To address the problem, this study proposes the first domain-specific LLM model and an integrated method for rapid earthquake impact assessment. First, a few categories are introduced to classify and filter microblogs considering their relationship to the physical and social impacts of earthquakes, and a dataset comprising 7282 earthquake-related microblogs from twenty earthquakes in different locations is developed as well. Then, with a systematic analysis of various influential factors, QuakeBERT, a domain-specific large language model (LLM), is developed and fine-tuned for accurate classification and filtering of microblogs. Meanwhile, an integrated method integrating public opinion trend analysis, sentiment analysis, and keyword-based physical impact quantification is introduced to assess both the physical and social impacts of earthquakes based on social media texts. Experiments show that data diversity and data volume dominate the performance of QuakeBERT and increase the macro average F1 score by 27 %, while the best classification model QuakeBERT outperforms the CNN- or RNN-based models by improving the macro average F1 score from 60.87 % to 84.33 %. Finally, the proposed approach is applied to assess two earthquakes with the same magnitude and focal depth. Results show that the proposed approach can effectively enhance the impact assessment process by accurate detection of noisy microblogs, which enables effective post-disaster emergency responses to create more resilient cities.

Этические аспекты подготовки журналистских материалов о детях и для детской аудитории: гуманистический подход

Thе article examines a two-pronged problem: the need to develop fundamental documents on the Russian media’s observance of humanistic principles, when preparing materials about childhood, as well as the lack of a professional journalistic school within which such approaches are formed and supported. The author turns to scientific research in the field of deontology and philosophy of journalism and comes to the conclusion, that the implementation of tasks to humanize the information space depends not only on the level of the development of media ethics, but also on the will of journalists, the political situation, and the structure of the media market at the stage of its economic transformation. In an aggressive media environment and when the professional journalistic community ignores ethical principles, the most vulnerable audience group subject to negative influence are minors. Today, some editorial offices deliberately deviate from the norms of morality and ethics in the process of creating a potentially “saleable” information product, while children and adolescents, according to statistics, are regularly indirect consumers of non-target content. That is why the author of the article chooses media texts from professional media as an empirical basis, since their authors, unlike many bloggers and content creators on social networks, as editorial staff, are aware of the responsibility not only at the legislative but also at the corporate level. Certain ethical charters and standards ratified by journalistic organizations in Russia contain recommendations for the preparation of media materials that address the topic of childhood directly or indirectly. A detailed analysis of the presented documents allows us to conclude that some provisions adopted in the early 2000s have lost their relevance, and today it is necessary not only to revise their content, but also to raise the question among practitioners about the need to show greater attention to measures of deterring aggressive rhetoric, as well as the question about the introduction of new self-regulation mechanisms.

Deep parallel contextual analysis framework based emotion prediction in community wellness communications on social media

Understanding public emotion on social media about community wellness is crucial for enhancing health awareness and guiding policy-making. In order to more fully mine the deep contextual semantical information of short texts and further enhance the effectiveness of emotion prediction in social media, we propose the Deep Parallel Contextual Analysis Framework (DPCAF) in the community wellness domain, specifically addressing the challenges of limited text length and available semantical features in social media text. Specifically, at the embedding layer, we first utilize two different word embedding techniques to generate high-quality vector representations, aiming to achieve more comprehensive semantical capture, stronger generalization ability, and more robust model performance. Subsequently, in the deep contextual layer, the obtained representations are fused with POS and locational representations, and processed through a deep parallel layer composed of Convolutional Neural Networks and Bidirectional Long Short-Term Memory Network. An attention model is then used to further extract semantical features of social media texts. Finally, these deep parallel contextual representations are post-integrated for emotion prediction. Experiments on a dataset collected from social media regarding community wellness demonstrate that compared to benchmark models, DPCAF achieves at least a 4.81 % increase in Precision, a 3.44 % increase in Recall, and a 10.81 % increase in F1-score. Relative to the most advanced models, DPCAF shows a minimum improvement of 2.65 % in Precision, 3.02 % in Recall, and 2.53 % in F1-score.

Beyond extraction accuracy: addressing the quality of geographical named entity through advanced recognition and correction models using a modified BERT framework

ABSTRACT In the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity.

Examining the Linguistic Tapestry of Globalization: Structural Borrowings

Purpose: Redefining economies, cultures, language dynamics, and societies worldwide, globalization has become one of the hallmarks of the contemporary era. The impact of this phenomenon in language is examined in this article, with a particular emphasis on structural borrowings from English into Albanian. Theoretical Framework: Scientific criteria have been chosen to determine whether the linguistic data are internal constructions of the language or structural borrowings from English, and to examine the extent to which a global language like that has influenced the structure of 'small' languages like Albanian. Methodology: Thirty-three new constructions were chosen from Albanian textual media, which were then split into groups examined in detail according to their semantic and syntactic features. Findings: Fifty-five percent of native speakers accept semantic-syntactic borrowings from English, which exhibit synonymy and compete with existing normative constructions. Borrowings enhancing the semantics of Albanian are welcome, while those affecting syntactic structure and leading to ambiguity, meaninglessness, and ungrammaticality are not. Research Practical and Social Implication: The knowledge of how syntactic-semantic systems can be modified and impacted by interaction with other language systems is increased by this work. Simultaneously, it advances our understanding of the social dimensions of language use, including language planning and identity maintenance in the area of globalization. Originality/Value: The connections between ‘small’ languages like Albanian and world culture have not received adequate consideration. This study advances our understanding of how globalization impacts communication between speakers of diverse languages and cultural backgrounds by shedding light on the process of structural borrowings.

Cultural netizenship as platformization of popular culture in Nigeria

The Instagram content of Nigerian digital creator Olalekan Olaleye illustrates how cultural netizenship stresses the centrality of platformization to the articulation of new genres of African popular representations. This article develops the argument by examining how platform aesthetics shape the trajectories and performative acts of cultural producers, something Olaleye's Nigerian context also modulates. By lingering on the digital and aesthetic labors of the cultural netizen, the article pinpoints the interactions between creative adaptations and social media texts. Olaleye's Instagram content demonstrates the way digital platforms have become integral to the production and circulation of popular forms and genres and the artistic responses everyday digital subjects make to institutions of the state.

Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.

Researching Emotional Experiences as Discursive Elements – A Suggested Qualitative Method

As scholars in the fields of political science, media research, and social psychology endeavor to understand crucial aspects of emotionality in the media, there is a growing need to methodologically address the communicative and discursive aspects of affective constructions in media texts. This article argues that by breaking down mediated emotional experiences represented through language in a set of identifiable elements, such as subject, emotion type, valence, intensity, proposed action, and object, those experiences could be used as workable and potent units of analysis when studying discursive and ideological media constructs of emotionality. By connecting insights from emotion science, the sociology of emotions, and media sociology, the qualitative method outlined, Emotional Experience Profiling (EEP), provides a flexible research tool conducive to an understanding of how emotional experiences represented through language in media texts are constructed and how they shape political discourse, identity, and emotional cultures. To demonstrate and illustrate the application of the method, the article provides examples from a pilot study which explores the research question: How do emotional experiences featured in Swedish far-right alternative news media construct and articulate political discourse?

Stylistic functions of anglicisms in German radio

Although English has no official status in countries of the expanding circle such as Germany (Kachru, 1985), English words and phrases, which are also referred to as anglicisms, can often be found in German mass media texts (Czech Rogoyska & Zboch, 2016; Fiedler, 2022; Glahn, 2002; Knospe, 2014; Onysko, 2004, 2007). In this context, previous research has paid much attention to the stylistic functions of anglicisms used by the mass media, often analysing brevity of expression and pragmatic functions of anglicisms, in particular how anglicisms are used to convey modernity (amongst others Piller, 2001; Winter–Froemel, Onysko & Calude, 2014). While such studies are usually exclusively based on analyses of German media corpora, this article enhances the current discourse on the developments around stylistic functions of English in German media texts by providing some insights from a larger research project which investigates shaping factors on radio content production and radio journalists’ perspectives on the use of anglicisms in the newsroom. In line with the overall results of the larger project, the analysis presented in this paper provides a brief overview of selected interview statements by adult contemporary radio journalists on whether anglicisms are used for reasons of brevity of expression and on their attitudes towards the use of English for conveying modernity on radio. The format of adult contemporary radio in Germany, in particular, has had a strong connection to the English language and Anglo-American culture from its very beginning, which makes it a prime example for such an analysis.

CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts

Social media platforms play an essential role in crisis communication, but analysing crisis-related social media texts is challenging due to their informal nature. Transformer-based pre-trained models like BERT and RoBERTa have shown success in various NLP tasks, but they are not tailored for crisis-related texts. Furthermore, general-purpose sentence encoders are used to generate sentence embeddings, regardless of the textual complexities in crisis-related texts. Advances in applications like text classification, semantic search, and clustering contribute to the effective processing of crisis-related texts, which is essential for emergency responders to gain a comprehensive view of a crisis event, whether historical or real-time. To address these gaps in crisis informatics literature, this study introduces CrisisTransformers, an ensemble of pre-trained language models and sentence encoders trained on an extensive corpus of over 15 billion word tokens from tweets associated with more than 30 crisis events, including disease outbreaks, natural disasters, conflicts, and other critical incidents. We evaluate existing models and CrisisTransformers on 18 crisis-specific public datasets. Our pre-trained models outperform strong baselines across all datasets in classification tasks, and our best-performing sentence encoder improves the state-of-the-art by 17.43% in sentence encoding tasks. Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings. The models are publicly available at: https://huggingface.co/crisistransformers.

Multi-class multi-label classification of social media texts for typhoon damage assessment: a two-stage model fully integrating the outputs of the hidden layers of BERT

ABSTRACT With the development of social media, it has become increasingly important to quickly and accurately identify social media texts related to disasters (e.g. typhoon) to aid in rescue and recovery efforts. Currently, multi-class classification and pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) are widely used for text classification. However, most studies on typhoon damage classification are multi-class single-label, which contradicts to the reality that a social media text may correspond to multiple types of damage. Moreover, the outputs of the hidden layers of BERT are not fully utilized. This paper proposes a two-stage multi-class multi-label classification method for typhoon damage assessment by fully integrating the outputs of the hidden layers of BERT. In the first stage, sentence vectors are adopted to identify typhoon damage-related texts. In the second stage, word matrices are applied for multi-class multi-label classification to further classify the texts into five damage categories (i.e. transportation, public, electricity, forestry, and waterlogging). The two stages are trained end-to-end to identify typhoon damage from social media texts. Experiments on SinaWeibo texts during typhoon landfall in Chinese coastal regions demonstrate that the proposed method can effectively improve the accuracy of text classification and comprehensively assess typhoon damage.

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.

Harnessing large language models over transformer models for detecting Bengali depressive social media text: A comprehensive study

In an era where the silent struggle of underdiagnosed depression pervades globally, our research delves into the crucial link between mental health and social media. This work focuses on early detection of depression, particularly in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT, SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into “Depressive” and “Non-Depressive” segments, translated into Bengali by native speakers with expertise in mental health, resulting in the creation of the Bengali Social Media Depressive Dataset (BSMDD). Our work provides full architecture details for each model and a methodical way to assess their performance in Bengali depressive text categorization using zero-shot and few-shot learning techniques. Our work demonstrates the superiority of SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also tackles explainability issues with transformer models and emphasizes the effectiveness of LLMs, especially DepGPT (GPT 3.5 fine-tuned), demonstrating flexibility and competence in a range of learning contexts. According to the experiment results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in zero-shot and few-shot scenarios but also every other model, achieving a near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B show relatively poorer effectiveness in zero-shot and few-shot situations. The work emphasizes the effectiveness and flexibility of LLMs in a variety of linguistic circumstances, providing insightful information about the complex field of depression detection models.

“And All I Gotta Do Is Act Naturally”

This article analyses musicians’ performances on film, using the iconic Beatles film Help! (Richard Lester 1965) as a foundational case study. The study investigates distinctions between film acting and musical performance while exploring the defining characteristics of musicians’ performances within the context of cinema. Building upon established concepts of persona (Auslander 2021), metafiction and metareference (Waugh 1984; Wolf 2009), the author introduces novel conceptual frameworks of “metaperformance” and “intramedial transmediality”. “Metaperformance” refers to the doubling of the act of performance, whereas “intramedial transmediality” describes the coexistence of diverse media texts within a single media text. Due to their off-screen musical persona, musical stars in films often provoke a pronounced transtextual and transmedial network and convey an implicit claim to reality. Compared to film actors, musical stars on film frequently provide metaperformances, embodying not only (fictional) characters but also performing their (“real”) musical persona within them. Additional case studies of Ed Sheeran’s performance in Danny Boyle’s Yesterday (2019) and the Spice Girls’ performance in Bob Spiers’s Spice World (1997) further clarify these theoretical insights. Working deductively, these films serve as contemporary illustrations of the theoretical concepts under examination. The findings of this essay contribute to an enriched understanding of the intricacies in musicians’ performances on film and shed light on the interplay between music, cinema, and artists’ on-screen personas.

Organization of the educational process for the formation of media and information literacy based on media text

Introduction. In conditions of heterogeneity and polyphony of the media space, it is necessary to methodologically substantiate and technologically skilfully organize the educational process to develop media and information literacy of students. The source and transmitter of information, knowledge, representative of cultural and value guidelines in modern media reality is the media text. On its basis, the formation of media and information literacy is integrated into the process of spiritual and moral development of the individual. This process is carried out based on the content of media texts and within the possible context. The purpose of the study – scientific substantiation and solution of the problem of using media text in the process of formation of media and information literacy of students on the basis of axiological understanding of the information space. Materials and methods. The research uses fundamental international and Russian documents that reflect the issues of media education, the formation of media and information literacy, the national security of the Russian Federation, scientific and methodological articles, monographs by Russian and foreign authors on this issue, media texts of various types and genres that are in the public domain. Research results. The expediency of an axiological approach to the organization of the process of formation of media and information literacy is proved. Based on this approach, the principles of media text selection are defined (axiological fullness and cultural conformity, problematic, informational significance, communicative value). The educational strategy that allows to implement the axiological approach is contextualization, within the framework of which multi-level tasks for working with media text for the formation of media and information literacy are formulated. Conclusion. The results obtained outline further research objectives. Let's list them: the definition of diagnostic tools to identify the level of formation of media and information literacy, the systematization of techniques and methods for the formation of media and information literacy, the preparation of detailed recommendations for improving the organization of the process of formation of media and information literacy of students.

Characteristics of modern network communication in the aspect of the formation of media and information literacy of students (using the example of the analysis of media texts of social networks)

Problem and objective. Problematic issues of communication in the Internet space, on social networks in particular, are among the most pressing today, attracting the attention of teachers, scientists of various specialties, and Internet users. For this reason, studying the features of the content, language, and ethical standards of media texts on social networks, as the most popular among young people, is an urgent problem of modern science and education. The purpose of the presented research is to point out the media texts contents problematic issues, linguistic design and speech ethics, to substantiate the need to develop students' media literacy and suggest some practical work in this direction. Material and methods. The research material was media texts from social networks that are of educational nature. To study them, the following methods were used: the method of collecting media text data, the method of processing the obtained data (generalization, comparison, classification, systematization); method for interpreting research results. Research results. Within the framework of this study, it was possible to identify the main negative trends in network communication: simplification of the content of media texts, deviation from the norms of literary language and even their complete ignoring, colloquialization, use of rude colloquial, slang, obscene vocabulary; low quality language game, language cynicism. The analysis of educational and cultural-educational media texts makes it possible to outline some areas of practical activity aimed at fighting current negative tendencies of the Internet communication. Conclusion. The conclusion is made about the need to improve students’ media literacy as a means of representatives of the younger generation adequate interaction with the global information environment. A simulator will serve these purposes, which should include media texts of various genres and tasks for them. A distinctive feature of this simulator is that it takes into account modern trends in Russian education aimed at preserving and enhancing traditional spiritual and moral values. Media literacy is a step towards media culture, the formation of which is possible only in the case of a personal attitude to the native language.

KEY CONCEPTS OF MODERN ENGLISH FINANCIAL DISCOURSE

The study is devoted to the analysis of key concepts of financial English discourse contained in media texts. The research material is based on financial texts posted on the website theguardian.com. The article is aimed at identifying the impact of the selected concepts on a wide audience. The main purpose of the study is to systematize and identify the main concepts that characterize the specificity of the linguistic image of financial concepts in the media context. To achieve this goal, the analysis methodology is used, which involves a thorough study and identification of the key elements of financial English discourse in media texts. The main theses of the study include the analysis of the use of specialized terminology and linguistic features of speech in media materials. The concept of "concepts of financial English discourse" is defined in the article as key ideas, concepts or topics that characterize the specificity of the speech image in the field of finance in the English-speaking environment. These concepts cover financial terms, concepts, principles, theories, strategies and other aspects that are actively used in the speech in the field of finance and economics in English-speaking countries. It has been found that the main concepts implemented in the financial texts on theguardian.com are RISK AND RISK MANAGEMENT, BANKING SERVICES, INVESTMENTS, FINANCIAL MARKETS, FINANCIAL INSTRUMENTS AND PRODUCTS, FINANCIAL INDICATORS AND REPORTS, MONEY AND CURRENCY. The selected concepts reflect various aspects of financial activities, such as risk management, investment, banking, financial analysis, international finance, budgeting, as well as economic concepts that affect financial markets and the global economy. The results of the study demonstrate the significant influence of the information space on the formation of financial English-language discourse for a wide audience of media platforms. The conclusions emphasize the importance of understanding financial terminology and the ability to analyze media texts in order to improve the financial literacy of society.

Improving English Reading Skills through the Audiovisual Text Media

This study aims to analyze "Improving English Reading Skills through the Audiovisual Text Media" among first-semester students of the English Language and Culture Study Program at Sumbawa University of Technology. This research is a quantitative study using a pre-experimental design approach (nondesign). The design consists of a one-group pretest-posttest design with a single predetermined group. The research sample comprises 10 students. The research instrument used to collect data is a test, which includes a pretest and a posttest. The results of this study indicate that the use of the Audiovisual Text Media can improve students' reading ability. This is supported by the analysis using the Paired Test, where the t-value is -42.052 with a significance value (2-tailed) of 0.000. Comparing this with the tabulated significance value (0.05), it is evident that there is a difference between the pretest and posttest scores regarding improving English reading skills through the audiovisual text media.

Bidirectional LSTM-based Sentiment Analysis for Assamese Text

With the enhanced exploration of the new generation of the web, people are free to state their opinion on any particular topic like product, services, organization and even on other people online in different social media platform and thus an innumerous amount of user generated contents are being created each moment. Hence, the need for mining this information has become the priority of the researcher so that they can identify the user’s sentiments and guide other people in various fields. Sentiment analysis deals with analyzing the review, opinion, attitude and emotions of a person from a given set of text by categorizing those on the basis of polarity as positive, negative and neutral. In this paper, sentiment of the social media text in Assamese Language is being analyzed because most of the communication is done through regional language and as a researcher from this region it is utmost concern to mine this information. To analyze the sentiments from the manually prepared datasets, LSTM- deep learning algorithm is used and implemented it in Python environment and also overall performance is measured in terms of accuracy, precision, recall and f1-score.