Linguistic Expertise Research Articles

Translation and validation of the Tinnitus Functional Index (TFI) into Kannada: a tool for clinical and research use in the Indian population

Purpose The Tinnitus Functional Index (TFI) is a widely recognized measure for evaluating tinnitus and is frequently used in both therapeutic and research contexts. Unfortunately, TFI is currently not available in any Indian languages. In a multilingual country like India, cross-cultural adaptation, translation, and validation of such scales in regional languages are critical to improving their use for clinical and research purposes. Therefore, this study focused on translating and validating the TFI in Kannada for use among the Indian population. Materials and methods The original version of TFI was translated with the help of audiologists and linguistic experts who were proficient in Kannada. The translated version was content validation by five audiologists and a cognitive debriefing by native speakers to check the familiarity of words. The final version (TFI-K) was then administered to 181 participants with tinnitus. Along with the TFI, other scales like Tinnitus Handicap Inventory (THI) were also obtained to check the convergent validity. The obtained data was then subjected to various statistical analyses like internal consistency, convergent and discriminant validity, and factor analysis. Results The TFI-K was found to have an excellent internal consistency (Cronbach’s α = 0.974) and good convergent validity (p < 0.001; r = 0.778) with THI-K. A principal component analysis with varimax rotation showed communalities ranging from 0.64 to 0.91. Scree plots with the TFI-K component showed a sharp decline after the first factor and approximately four factors above the eigenvalue of 1. The factor analysis results suggest that the TFI-K has a structurally intact 8-factor loading, with a few exceptions that are discussed in detail. Conclusion The TFI-K has demonstrated remarkable dependability and satisfactory integrity, making it a valuable tool for assessment and treatment planning in clinical and research settings.

Exemplification and reformulation in expert linguists’ writings: Elaborative metadiscourse between disciplinarity and individuality

Exemplification and reformulation are key elaborative strategies that empower clarity and argumentation in academic writing. Most research investigating their use in academic writing argues that writers’ choices in terms of frequency and marker type are conditioned by disciplinary variations and the level of expertise of the writers. This has led to an understanding of discourse communities as being homogeneous groups of novice versus expert writers and of separate soft versus hard disciplines. Through the analysis of 90 research articles and book chapters single-authored by six leading linguists, this study shows that these successful authors deviate from the common practices in linguistics and their choices deviate from each other significantly. However, the data does not reveal any steady increasing or decreasing frequencies in the use of exemplification and reformulation throughout their career stages, which makes it hard to claim that the frequency of using the two discourse functions evolves with experience. The study argues that individuality and style preferences within the same discourse community should be acknowledged as an additional variable affecting discursive choices and calls for a more nuanced understanding of disciplinarity and writing expertise.

SURFUCE STRUCTURE DAN DEEP STRUCTURE MAKNA QIRADAH

This research explores the concepts of deep structure and surface structure in the context of the interpretation of the word “qiradah” based on the analysis of Noam Chomsky's theory on Surah Al-Baqarah 65. Chomsky, a linguistic expert, developed the transformative-generative theory, providing a profound understanding of language. In this theory, Chomsky emphasizes that language consists of the surface structure, which is the visible part of language, and the deep structure, which is the core meaning of language. The author employs a qualitative method, reviewing relevant prior works for this discussion. The aim is to validate the applicability of Noam Chomsky's linguistic theory in understanding language. The study reveals that language indeed has both surface and deep structures, particularly in the interpretation of the word “qiradah” in Surah Al-Baqarah 65. The surface structure signifies the physical transformation into apes that happened to the Children of Israel, while the deep structure or core meaning indicated by the word “qiradah” is the transformation of the heart and behavior resembling apes, not the physical change itself.

Artificial Intelligence Tools and Bias in Journalism-related Content Generation: Comparison Between Chat GPT-3.5, GPT-4 and Bing

This study explores the biases present in artificial intelligence (AI) tools, focusing on GPT-3.5, GPT-4, and Bing. The performance of the tools has been compared with a group of experts in linguistics, and journalists specialized in breaking news and international affairs. It reveals that GPT-3.5, widely accessible and free, exhibits a higher tendency rate in its word generation, suggesting an intrinsic bias within the tool itself rather than in the input data. Comparatively, GPT-4 and Bing demonstrate differing patterns in term generation and subjectivity, with GPT-4 aligning more closely with expert opinions and producing fewer opinative words. The research highlights the extensive use of generative AI in media and among the general populace, emphasizing the need for careful reliance on AI-generated content. The findings stress the risks of misinformation and biased reporting inherent in unexamined AI outputs. The challenge for journalists and information professionals is to ensure accuracy and ethical judgment in content creation to maintain the quality and diversity of content in journalistic practices.

Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning

Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.

A needs analysis of ESP courses in colleges of art and design: Consensus and divergence.

This study presents a comprehensive needs analysis of English for Specific Purposes (ESP) courses in colleges of art and design in China. By examining the perspectives of undergraduate students, graduate students, art teachers, and English teachers, the research identifies consensus and divergence in ESP needs and implementation challenges. The analysis reveals a strong demand among students for specialized English learning, with a particular emphasis on receptive and interactive skills. However, there is a widespread acknowledgement of students' difficulties across ESP skills, indicating a gap between student competencies and the professional demands, thus highlighting the need for targeted educational interventions to effectively address the difficulties. Notably, reading and writing abilities emerge as the most lacking skills. While there is unanimous recognition of the positive impact of ESP on students' professional abilities and international competitiveness, a significant disparity exists in the enthusiasm for implementing ESP courses, paticularly with resistence from English teachers. The study highlights the critical challenges in ESP teaching, with teacher qualifications identified as the most prominent issue. Interestingly, English teachers attribute difficulties primarily to teaching materials, indicating a potential lack of readiness for ESP course delivery. A gap is also observed between teachers' evaluations of student abilities and students' self-assessment, especially among undergraduate freshmen, suggesting an overestimation of their English proficiency in professional contexts. The paper concludes with the implication that for effective ESP course implementation in colleges of art and design in China, targeted faculty development programs and a collaborative approach involving English teachers, art teachers, and professionals are essential. The collaboration should aim to develop materials that integrate specific professional knowledge with linguistic expertise. Additionally, a balanced approach combining general English proficiency and specialized English training is recommended to address both foundational and specialized language skills. Overall, the study underscores the need for targeted ESP courses in art and design education, tailored to bridge the gap between current proficiency levels and professional language requirements.

Stance Detection with Collaborative Role-Infused LLM-Based Agents

Stance detection automatically detects the stance in a text towards a target, vital for content analysis in web and social media research. Despite their promising capabilities, LLMs encounter challenges when directly applied to stance detection. First, stance detection demands multi-aspect knowledge, from deciphering event-related terminologies to understanding the expression styles in social media platforms. Second, stance detection requires advanced reasoning to infer authors' implicit viewpoints, as stances are often subtly embedded rather than overtly stated in the text. To address these challenges, we design a three-stage framework COLA (short for Collaborative rOle-infused LLM-based Agents) in which LLMs are designated distinct roles, creating a collaborative system where each role contributes uniquely. Initially, in the multidimensional text analysis stage, we configure the LLMs to act as a linguistic expert, a domain specialist, and a social media veteran to get a multifaceted analysis of texts, thus overcoming the first challenge. Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge. Finally, in the stance conclusion stage, a final decision maker agent consolidates prior insights to determine the stance. Our approach avoids extra annotated data and model training and is highly usable. We achieve state-of-the-art performance across multiple datasets. Ablation studies validate the effectiveness of each role design in handling stance detection. Further experiments have demonstrated the explainability and the versatility of our approach. Our approach excels in usability, accuracy, effectiveness, explainability and versatility, highlighting its value.

Current Trends in the Use of Machine Learning for Error Correction in Ukrainian Texts

The article's authors have provided a detailed problem description of identifying and correcting errors in Ukrainian-language texts. This paper provides a detailed analysis of the latest research and publications aimed at solving the problems of identifying and correcting errors in Ukrainian-language texts. The analysis of modern tools related to error correction in texts is presented along with a comparative description. Investigated the existing data corpora for the Ukrainian language so that they are relevant to solving GEC tasks. Discovered the need to create a large annotated data corpus, which will be prepared by a special team with linguistic expertise. Analysed the opportunities, advantages and disadvantages of modern machine learning models that interpret the task of detecting and correcting errors in texts as classification or machine translation. Introduced the need to develop a machine-learning algorithm that will take into account the specifics of morphologically complex languages, such as Ukrainian. Demonstrated the work of the modern models and provided screenshots. Revealed the need for further research in the Ukrainian segment of machine learning to solve the problems of correcting errors in texts using various methods and approaches.

Вопросы экспертного сопровождения расследования преступлений, предусмотренных статьей 207.3 Уголовного кодекса Российской Федерации

The article provides statistical information on the judicial practice of considering criminal cases of crimes provided for in Articles 207.3, 280.3, 284.2 of the Criminal Code of the Russian Federation. The issues related to the appointment of forensic linguistic expertise in the investigation of crimes under Article 207.3 of the Criminal Code of the Russian Federation are considered.

A Study of Language Variation Analysis - A Review

Dialectology is the research of dialects, which are varieties of a language that differ in pronunciation, vocabulary, and sometimes grammar. It explores regional, social, and historical variations within a language, aiming to understand how and why these differences occur . This paper has its focus on studying the difference between the regional language and standard language. The data is collected by using various recording devices and mobile phones. The primary goal of this paper is to explore how language changes in different situations. The focus is on examining variations in language usage, especially the choice of words (vocabulary), across various contexts. By carefully analyzing these linguistic differences, the study intends to offer valuable insights into understanding the specific context in which an individual is communicating. This can shed light on how language adapts to different environments and provides a deeper understanding of communication patterns. Analysis of dialects can help identify the geographic or social origin of a speaker, providing insights into their background. This can be useful in cases where the origin of a suspect or victim is uncertain. Linguistic experts may analyze speech patterns, vocabulary, and pronunciation to draw conclusions about a person's likely dialect, contributing to the overall forensic investigation

A Cross-Cultural Adaptation of the Czech Version of the Developmental Coordination Disorder Questionnaire: The Content Validity Part.

The Developmental Coordination Disorder Questionnaire (DCDQ) is a widely used parent questionnaire for screening motor coordination disorders in children aged 5-15 years. Despite increasing motor difficulties in children, a validated version is lacking in Central Europe. In addition, previous studies pointed out that several DCDQ items were shown to be problematic in different cultural environments. We found that the majority of these studies did not assess the item's content validity approach for keeping the semantic form and linguistic intelligibility of the original items. Therefore, this study aimed to translate the DCDQ, determine the content validity of items, and adapt the DCDQ for Czech children aged 6-10 years, where the identification of motor difficulties is crucial. Back-translation was employed, and face validity was consulted with linguistic experts and occupational therapists. A sample of 25 bilingual parents and practitioners evaluated the translated version, with content validity assessed using the Content Validity Ratio coefficient (CVR). Initial CVR scores ranged from 0.6 to 1.0. Lower scores were found for items 14 and 15, which were shown to be problematic in previous studies. The reason for the lower content validity in these items was due to double negation. Following linguistic modifications, the CVR values improved (range: 0.87-1.0), indicating content and semantic stability. Our findings underscore the importance of considering content validity and language specificity, including issues like double negation, during cross-cultural questionnaire validation to mitigate potential psychometric concerns in the future. The adapted Czech version exhibits significant content validity, thereby warranting further validation of its psychometric properties.

Situation Analysis of Female Academics' Career Advancement in Higher Education in Syria

This research examines factors which may be influencing female academics career advancement at Higher Education Institutions (HEIs) in Syria. The paper conducts situation analysis on academic environment at HEI in Syria, exploring factors which may be impacting female academics experience and career progression and lack of academic representation at senior management levels. The paper examines a private HEIs investigating the environment in Syria highlighting the situation after 2011. The methodology applied in diagnosing the situation, is based on data collected from a capacity building in higher education project (CBHE), funded by Erasmus+. The methodology employs the design of questionnaire survey administered at the research target group. A one-way translation from English to Arabic is performed by expert academics and verified by linguistic experts. The sample design employs nonprobability sampling techniques. The size of the sample is 47 observations for academics. The findings of the analysis are reported across individual, and institutional levels. The research results signify that interest in research is a strong motivator; while career satisfaction still reveals an area of future development. There is still little awareness on gender issues topics, as well as little research works being performed in the scope gender research issues. The concept of gender equality has a high potential for evolution and expansion on the intermediate and long term at the institutional and socio cultural contests in Syria. The future direction of this research emphasizes developing a conceptual framework on the factors which may be affecting female academics experience and career advancement in HEIs in Syria. Furthermore, future research recommends the development of institutional and national policies in the scope of gender equality, which could be reflected at human resources practices, environment of the workplace, infusion of gender at university mission and planning, curriculum, research, and university governance.

A Comprehensive Study to Build Immersive Virtual Reality-Powered Language Learning

In the rapidly evolving landscape of education technology, this project explores the integration of Immersive Virtual Reality (IVR) to revolutionize language learning experiences. Leveraging the power of virtual reality, this innovative approach aims to provide a dynamic and immersive language acquisition platform that transcends traditional methods. The project focuses on creating a virtual environment where language learners can engage with authentic scenarios, fostering practical language application and cultural understanding. Users will navigate through lifelike settings, interact with virtual native speakers, and participate in real-world conversations, thus enhancing their linguistic proficiency in a contextualized manner. Key features include personalized learning paths, adaptive challenges, and gamified elements to sustain user motivation and accommodate diverse learning styles. The incorporation of artificial intelligence ensures tailored feedback, tracking individual progress, and dynamically adjusting the learning curriculum to suit each user's proficiency level. The virtual reality language learning experience is designed to be accessible and user-friendly, catering to a broad demographic. Emphasis is placed on creating a sense of presence and cultural immersion, allowing learners to develop language skills in an environment that mirrors real-life scenarios. Through a combination of cutting-edge virtual reality technology, linguistic expertise, and pedagogical principles, this project aspires to redefine language education. The research and development aim to assess the efficacy of this immersive approach, comparing learning outcomes with traditional methods, with a vision to contribute to the ongoing discourse on the future of education technology and language acquisition.

Enabling Natural Language Processing and AI Research in Low-Resource Languages: Development and Description of an Assamese UPoS Tagged Dataset

This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.

Self-mention in Chinese linguistic MA novices’ and experts’ academic writing: A corpus-driven investigation of ‘we’

The research aims to conduct a comparative analysis of self-mention, particularly the usage of self-mention ‘we’, as a means of academic persuasion between Chinese linguistic MA novices and linguistic experts. Self-mentions serve various rhetorical functions in academic persuasion. However, for second language writers, mastering these rhetorical functions represents an advanced writing skill, which is contingent upon a proficient command of the structural aspects of self-mention phrases. In light of this issue, this study undertakes a collocation and chunk analysis. The objective is to analyze the collocation characteristics and chunk features of self-mention ‘we’ in international journal articles (ILJA_C) and Chinese MA theses (CLMA_C). This objective informs the choice of the research subject – identifying similarities and differences in the utilization of self-mention ‘we’ in two databases: ILJA_C and CLMA_C. This study’s methodology utilizes a corpus-driven approach alongside comparative academic discourse analysis within academic writing. The novelty of this research lies in its investigation of the collocation characteristics and chunk features of self-mention ‘we’ in CLMA_C and ILJA_C. This study represents a substantial contribution to the fields of second language acquisition and comparative linguistics by enhancing our understanding of self-mention in academic persuasion. Findings reveal significant disparities in the usage of ‘we’ between Chinese MA novices and linguistic experts. Novices tend to focus on constructing discourse logic, whereas experts prioritize establishing academic positions. Analysis of chunk structures exposes varying approaches to discourse and interpersonal functions, underscoring the necessity for novices to emulate expert usage patterns.

Diverse Integration of Simulated Patients in Medical Education for Communication, Language, and Clinical Skills in Hungary.

Medical education and communication training has been undergoing substantial changes recently in our globalized environment. Multidisciplinary simulation-based methods worldwide focus on improving effective clinical skills including history taking, physical examination, diagnostic skills, critical thinking, therapeutic skills, and others via interactions between medical students, trainees, and patients. Recently, Hungary has joined such global trends. The first simulated patient program in Hungary was developed at the University of Pécs Medical School in 2019 to aid effective patient-interviewing skills in language and communication classes. Under the supervision of linguists, communication specialists and medical professionals, the multidisciplinary program uses lay people to perform as simulated patients while using the languages of Hungarian, German, and English. Our simulated patient program plays a specific role in supporting students to learn languages for medical purposes, aiming to prepare them for handling the medical, linguistic, at the same, time emotional and sociocultural difficulties encountered while taking patient histories. Medical and linguistic experts evaluate student performance, provide feedback, and give tailored instruction so that students can advance their communicative and professional skills. This study discusses working formats and the role of constructive feedback exploring potential advantages and disadvantages, sharing ideas, and proposing recommendations on language- and communication-based integration of simulated patients. In our elective communication courses, undergraduate medical students learn to cope with a variety of patient situations through practicing medical emergencies, misunderstandings, and disagreements in a safe atmosphere provided by the MediSkillsLab. Among the benefits, we should emphasize that any course with a growing number of students can be accommodated by carefully designing the program, which allows for interprofessional collaboration. This program contributes to higher-quality medical education, promoting more skilled and compassionate healthcare specialists.

Юрислингвистика в Беларуси: специфика и перспективы развития

The article is aimed at research of the current state of legal linguistics as a branch of linguistics in the Republic of Belarus, which is characterized by polyparadigmality and the formation of its own methodological system. The purpose of the scientific work is to determine the specifics of legal linguistics in Belarus and outline a range of promising areas that require fundamental development. The material for the study was the scientific works of Western and Russian scientists who had the greatest influence on the formation of the subject area of legal linguistics. The methodology of the research included general scientific methods of generalization and systematization, as well as descriptive, analytical and comparative methods.The specifics of the subject area of scientific research is manifested in all spheres of legal linguistics developing in Belarus. For example, within the framework of linguistic controlling, the text of a legal act is evaluated for compliance with the norms of both Russian and Belarusian literary languages. Judicial rhetoric defines the requirements for the range of subjects of public speeches in accordance with the procedural legislation. In line with legal terminology, the unique units of the terminological system of Belarusian law are studied. Within the framework of linguistic expertise, tools are being developed that allow for the proper correlation of signs of speech offense, the composition of which is reflected in the Belarusian legislation, with appropriate linguistic markers.The conducted research shows that the development of Western and Russian scientists had a significant impact on the formation of the subject area of domestic legal linguistics, however, some areas of the integrative industry still have not found proper development. Promising areas for the development of legal linguistics in Belarus are the establishment of the authorship of the text, the fight against plagiarism, the study of trademarks (service marks) and advertising slogans for the coincidence of designations.

Designing a Data Literacy-Based Speaking Skills Assessment Instrument for High School Students

This study aims to develop a data literacy-based speaking skill assessment instrument for students at the high school level. Currently, speaking skills are one of the 21st century competencies that must be developed. Students of the 21st century have demands to master both written and oral communication competencies. In addition, data literacy skills are one of the new literacies that students must master as a complement to communication skills. This research uses a research and development method with the Plomp model which consists of five phases, namely the initial investigation phase, design phase, realisation phase, test, evaluation, and revision phase, and implementation phase. Data collection techniques were conducted by interview, document analysis, and questionnaire. The participants of this research involved teachers, assessment experts, linguistic experts, and speaking skill experts. The results of expert validation of the developed speaking skills assessment instrument obtained good results with a very feasible category so that the speaking skills assessment instrument product is feasible to be used to assess students' presentation skills in presenting scientific work at the high school level.

Certainty Stance Adverbs in Chinese Linguistic Academic Writing: A Corpus-based Study

This study delves into the intricate usage of certainty stance adverbs in the academic writings of the Chinese. The object of this study is to conduct a contrastive analysis of certainty stance adverbs between Chinese linguistic MA novices and linguistic experts. The subject of this study is to explore the similarities and differences in the use of these adverbs within the academic discourse of the two groups mentioned. In addressing these disparities, the study seeks to analyze them from the perspective of interlanguage and interpersonal function, with the ultimate goal of enhancing the production of high-quality academic papers by the Chinese. The study employs two key methods: the contrastive analysis method and the corpus-based method. The novelty of the study lies in its contrastive examination of certainty stance adverbs between Chinese linguistic MA novices and experts. Through an in-depth analysis of theoretical frameworks and linguistic data extracted from the MA theses of Chinese linguistic novices and research articles of linguistic experts, the study underscores the importance of considering the similarities and differences in the use of certainty stance adverbs within the realm of second language acquisition and pragmatic studies. This consideration aims to refine learners’ syntactical and pragmatic command of certainty stance adverbs to align with expert academic discourse, ultimately fostering effective interpersonal communication in academic writing. The findings of this research offer valuable insights into the specific linguistic challenges encountered by the Chinese, thereby laying a solid groundwork for the development of targeted pedagogical strategies to bolster their academic writing skills.

Picture analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic

Today, advertising science is a tool that helps advertisers to design their advertising to meet the needs of the audience. In this regard, knowing and understanding the audience is one of the most important points that advertisers should pay attention to before advertising in order to better attract the audience. This study has been done with the aim of billboards and infographics analysis related to promoting preventive behaviors and vaccination against the Coronavirus disease pandemic and investigating the opinion of the general adult population of Iran. The method used in this research is the qualitative method. In this research, according to the type of data and research goals, Kress and Van Leeuwen’s discourse theory method has been used. The sample size includes 36 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). Then, an online survey about promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach’s alpha internal consistency coefficient, yielded a coefficient of 0.968. In this study, data analysis was conducted using IBM SPSS Statistics software, version 15.0 (IBM Corp., Armonk, NY, USA). Finally, users’ opinions about of billboards and infographics were analyzed using descriptive statistics. The results of component analysis and surveys show that visual components such as «The staring look at the spectator (Demand)», «Head-on Shot (inclusion)», «Down Shot (Creating a sense of participation for the represented person)», «Close-up (intimate/individual relationship)», «Level Shot (equality)» and «High-Angle Shot (Presenting power)» in medical advertising has had a great impact in arousing public opinion to create a positive attitude towards preventive measures and vaccination during the Coronavirus disease epidemic. The results of this research show that in visual communication, visual components play a significant role in creating and maintaining target ideologies. Also, advertising in the field of preventive measures in medical sciences requires certain rules that determine people’s culture and the main foundation of their attitude and thinking. Therefore, it is necessary to know such knowledge and learn it by the medical staff to deal with critical situations.