Media Analysis Research Articles

Comparing traditional surveys and web-scraped data to understand the pigeon racing industry in Southern California

Recent outbreaks of virulent Newcastle disease (vND) in domestic poultry in Southern California have raised questions as to the source(s) and the mode(s) of transmission. While pigeons have been found to be capable of transmitting a variant of Newcastle disease virus (NDV) known as Pigeon paramyxovirus-1 (PPMV-1) to other avian species including poultry, the potential geographic distribution and scope of transmission from racing pigeons to other domestic poultry in Southern California is poorly understood. In order to better understand the potential risk of transmission of vND from racing pigeons to domestic poultry, an extension-initiated survey was conducted in addition to social-media based analysis of racing pigeon activity associated with postings on a web-based popular classified advertisement (CAW) in Southern California. Likely due to the insularity of the pigeon racing industry in Southern California, only 1 survey response was completed. However, monitoring results from the CAW demonstrated a much higher level of social media activity associated with racing pigeon sales in Southern California. Specifically, 191 unique posts associated with pigeon sales were flagged between 12/21/2020 and 1/21/2021 demonstrating the potential for the analysis of social media as an extension-based tool. Data from the CAW showed that the majority of pigeon related posts and sales came from both the Inland Empire region of Southern California and San Diego county which overlapped with the established vND control zone. From an extension perspective, while traditional-based surveys can still provide valuable information, web-scraping of social media can provide a continuous source of spatio-temporal data that can facilitate extension-based observations and analysis among hard to reach stakeholder groups.

Chemical Analysis of Plasma-Activated Culture Media by Ion Chromatography

Background: Currently, the procedures and methods applied in biological and medical fields for the determination of reactive oxygen and nitrogen species (RONS), primarily rely on spectrophotometric techniques, which involve the use of colorimetric reagents. While these methods are widely accepted, they exhibit significant limitations from an analytical standpoint, particularly due to potential inaccuracies, artifacts, and pronounced susceptibility to matrix effects. The purpose of this Technical Note is to demonstrate the application of ion chromatography—a robust and well-established analytical technique—for the quantification of RONS produced in cell culture media through the exposure to cold atmospheric plasma (CAP), an innovative therapeutic approach for cancer treatment, known as CAP indirect treatment. In addition, the present protocol proposes to apply the pharmacokinetics principles to the RONS generated in plasma-treated liquids (PTLs) following CAP exposure. Methods: The strategy involves elucidating the kinetic profiles of certain characteristic species by evaluating their half-life in the specific media used for cell cultures and investigating their “pharmacokinetic” (PK) profile. In this approach the drug dose is represented by the plasma power and the infusion time corresponds to the exposure time of the culture medium to CAP. Volume-dependent results were shown, focusing on nitrites and nitrates activities, justifying cellular inhibition. Results: This methodology enables the correlation of the PTL biological effects on different cell lines with the PK profiles (dose/time) obtained via ion chromatography. Conclusions: In conclusion, being a simple and green method, it could be used as an alternative to toxic reactions and analytical techniques with higher detection limits, while achieving good resolution.

517 An analysis of social media and e-cigarette use by middle and high school students surveyed by the national youth tobacco survey in 2023

517 An analysis of social media and e-cigarette use by middle and high school students surveyed by the national youth tobacco survey in 2023

Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis

ABSTRACT When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers.

The global actorness of the EU seen from the Korean media and elites: a capability-expectations gap or an expectations deficit?

ABSTRACT This paper examines how the EU’s capability stood in relation to the expectations held by Korean society based on the results of newly conducted media analysis and elite interviews from 2020 to 2022. The paper focuses on the EU’s global actorness and argues that not only was there a capability-expectations gap (CEG) and/or an expectations deficit, as was typically found in many other similar studies done for other countries, but also there was a third phenomenon in the Korean case study: capability-expectations matching (CEM). The CEM was discovered in one aspect of the EU’s global actorness, namely the EU as an economic and trade actor. An additional new finding was that another feature of the EU’s global actorness – the EU as a norm promoter – started to become more popular in the eyes of Korean society.

Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study.

The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored. Identifying DDEs is crucial for understanding medication adherence and patient outcomes. The aim of this study is to provide a flexible framework for investigating various clinical research questions in data-sparse environments. We provide an example of the utility of this framework by identifying DDEs and their root causes in an open-source web-based forum, MedHelp, and by releasing the first open-source DDE datasets to aid further research in this domain. We used several LLMs, including GPT-4 Turbo, GPT-4o, DeBERTa (Decoding-Enhanced Bidirectional Encoder Representations from Transformer with Disentangled Attention), and BART, among others, to detect and determine the root causes of DDEs in user comments posted on MedHelp. Our study design included the use of zero-shot classification, which allows these models to make predictions without task-specific training. We split user comments into sentences and applied different classification strategies to assess the performance of these models in identifying DDEs and their root causes. Among the selected models, GPT-4o performed the best at determining the root causes of DDEs, predicting only 12.9% of root causes incorrectly (hamming loss). Among the open-source models tested, BART demonstrated the best performance in detecting DDEs, achieving an F1-score of 0.86, a false positive rate of 2.8%, and a false negative rate of 6.5%, all without any fine-tuning. The dataset included 10.7% (107/1000) DDEs, emphasizing the models' robustness in an imbalanced data context. This study demonstrated the effectiveness of open- and closed-source LLMs, such as GPT-4o and BART, for detecting DDEs and their root causes from publicly accessible data through zero-shot classification. The robust and scalable framework we propose can aid researchers in addressing data-sparse clinical research questions. The launch of open-access DDE datasets has the potential to stimulate further research and novel discoveries in this field.

Media Representation and Evaluation of Non-travelers

Although about a quarter of the German population are non-travelers, the economic and social consequences that may result from non-travel are still unexplored. For this reason, this study aims to examine via a media analysis the representation and evaluation of non-travelers in German newspapers since the turn of the millennium and gain first insights into the social consequences that non-travelers face. By conducting a discourse analysis, a part of the social reality of non-travelers can be reconstructed. The study concludes that travel is predominantly constructed as a norm in the media and that deviation from this norm leads to negative social sanctions. Over the last 20 years, however, mainly three events have had an impact on the media's assessment of non-travel: the terrorist attacks of 9/11 in 2001, the financial crisis in 2008-09 and the COVID-19 pandemic in 2020-21. The COVID-19 pandemic led to a paradigm shift in the evaluation of the phenomenon of non-travel, during which a normalization process of non-travel occurred, at least temporarily. This media analysis thus provides the first indications of the social consequences for non-travelers in the past 20 years in Germany.

Who talks about climate, peace and security? A social media analysis to identify key global actors

Uncovering key actors within a policy network provides pathways for engagement, consensus-building, partnership development, and understanding the diffusion of knowledge in a given debate. Given the unprecedented scale of the climate emergency, the emerging field of climate security has rapidly gained centrality in academic and policy fora, as well as in the public debate. Yet, a systematic analysis of the main actors engaged in this space is missing. This study draws from digital methods and network analysis techniques to employ a method for identifying relevant actors, focusing on Twitter (now X) from 2014 to 2022, with the objective of systematically spotting the major actors driving and shaping public discussions around climate security. The research also demonstrates how institutions can position themselves within such issue networks through a case study of the CGIAR, the largest publicly funded global research partnership for a food-secure future dedicated to transforming food, land, and water systems in a climate crisis that has recently positioned itself in this community. Results reveal that the climate security debate on social media is predominantly institutional, with research bodies and international organizations from the Global North as central elements. While CGIAR is a relatively new actor, it is already centrally located in the network, maintaining strong connections with other major players, which places it in a strategic position to enhance its influence and reach. Understanding this discursive landscape is crucial for institutions and organizations to identify opportunities for effective engagement, partnership, and positioning in such an increasingly salient field of research and practice.

Students' digital media literacy abilities in Islamic religious education lessons reviewed from learning style

This research is considered necessary because it is in line with the current digital era where many of the students certainly take advantage of the digital media around them. With a sufficient understanding of digital media literacy, it is hoped that it will help them in choosing and evaluating information wisely, thereby reducing the risk of exposure to inaccurate or dangerous information because it is seen from the rapidly increasing digital development. This study aims to find out the extent of students' digital media literacy and also the various learning styles of students and the analysis of students' digital media literacy according to their respective learning styles (visual, auditory and kinesthetic). The method used descriptive qualitative research with 48 respondents. The data collection technique uses questionnaires (multiple choice digital media literacy questionnaire and learning style questionnaire to determine the types of students' learning styles including visual, auditory, kinesthetic, and mixed) and analysis using the help of SPSS version 25. The results of this study show that the learning style that has a higher understanding of digital media literacy than other learning styles is the auditory learning style with an average of 19.80, the second is the visual learning style with an average of 19.75, the third is the mixed learning style with an average of 19.57, and the last is the kinesthetic learning style with an average of 19.17.

Innovation Digital Marketing In Increase Interest Buy Martabak Terang Bulan Arum Consumer

This study aims to analyze the impact of digital marketing innovation on increasing consumer purchasing interest in Martabak Terang Bulan Arum products. In the digital era, the use of technology-based marketing strategies is one of the keys to winning competition in the culinary market. The research method used is a quantitative approach with data collection techniques through surveys, interviews, and social media analysis. The results of the study show that the application of digital marketing innovation, such as the use of social media, creative content creation, and collaboration with influencers, significantly increases consumer purchasing interest. In addition, factors of service personalization and direct interaction with consumers also contribute positively to customer loyalty. This study provides recommendations for culinary business actors to further utilize digital platforms as an effective and sustainable marketing tool.

Contestations and cooperation in Muslim–Jewish neighbourhood encounters: A London case study

Religious spaces in the London borough of Barnet provide a lens through which to understand Muslim–Jewish encounters. This case study, pre-dating the 2023 escalation in the Israel–Gaza conflict, examines community relations in the context of the hegemonic discourses that play into racialisations, power dynamics and cultural connectedness through minority religious and ethnic identities in superdiverse urban centres. It focuses on a mosque’s application for planning permission in an area with a sizable Jewish population. Contestations and cooperation developed between the mosque and local Jewish communities, with some offering support while others mobilised, eventually successfully, to prevent planning permission being granted. Power differentials around race, class, religious affiliation and access to political power structures emerge in these instances, in which the impacts of racialisations, societal anxiety and communal hierarchy are sometimes overt and sometimes subtle. These complex and multifaceted events can be productively viewed through the narratives that circulate through local relations, social hierarchies, national discourses and culturally charged communal entanglements. This article draws on mixed methods of interviewing, press and social media analysis, and ethnographic observations to explore religious spaces as a lens to local encounters, in a manner that seeks to avoid direct involvement in an already complex incident.

Optimizing Deep Learning-Based Natural Language Processing for Sentiment Analysis

Deep learning (DL)-based natural language processing (NLP) technologies are applied and optimized for the analysis of emotional implications in text data. Understanding emotional intricacies in text has become necessary for a variety of industries, like social media analysis, customer feedback interpretation, and mental health monitoring, due to the exponential rise of digital material. The study emphasizes the difficulties associated with the automation of emotion analysis, including the diversities of the text formats and the NLP complexities. This research gathers that text-based emotion analysis data and pre-processing techniques are utilized for the analysis of emotion, including normalizing methods such as tokenization and lemmatization, along with the removal of stop words and punctuation. The model incorporates pre-trained embeddings and attention mechanisms to capture contextual information and emotional subtleties for any language. The study proposes the hybrid model using tuned crow search optimized dynamic graph neural network (TCSO-DGNN) on an automatically optimized process to enhance the accuracy of emotion detection. The proposed model results on emotion analysis data and superior results are obtained, indicating better performance in precision (94.72%), accuracy (96.43%), recall (94.36%), and F1-score (93.02%). The findings indicate that the inclusion of TCSO-DGNN significantly enhances the identification of complex emotional expressions concerning traditional NLP techniques. This study explores the potential of integrated DL approaches to refine the capabilities of emotion analysis tools, particularly in contexts requiring refined interpretations of human emotions expressed online. Additionally, it contributes to academic discourse in the field of emotion analysis while providing practical insights for developers of NLP applications across business and public service sectors.

Perceptions of China in the Age of COVID-19: Findings from Pakistan's Media Spheres

The COVID-19 pandemic served as a test for China's soft power, drawing diverse reactions from its friends and foes. This article investigates the impact of the COVID-19 outbreak on China's image, delving specifically into sentiments within one of the most China-friendly nations in the world, Pakistan. This study conducts a qualitative content analysis of Pakistan's news media and X sphere and investigates sentiments towards China and Chinese vaccines from 2020 to 2021. Media narratives were found to be distinctly more negative towards Chinese vaccines than China in the aftermath of the pandemic. An assessment of dominant media narratives offers possible explanations for the divergence in sentiments, mainly attributing them to “China's support of Pakistan,” “China's domestic containment,” “China's international role,” and “conspiracy theories and misinformation.” This article provides insights into China's vaccine diplomacy, its soft power dynamics, and public sentiment sway within its most amicable partnership in the international arena.

Fostering creativity and critical literacy: transforming EFL classes with engaging critical media literacy integration

PurposeThis study aims to examine the effect of critical media literacy integration in the context of English language teaching (ELT) in Indonesian high school students on improving students’ creativity and essential literacy skills.Design/methodology/approachThe study was conducted in an Indonesian ELT class consisting of 32 high school students. The methodology involved instructing students to analyze various media sources and articles to demonstrate their critical reading abilities before writing creative argumentative essays. Data collection instruments included students’ argumentative essays, teaching observations and students’ self-reflections on their learning experiences.FindingsThe study revealed that students were capable of creating creative and well-structured argumentative essays. However, their conclusions were often unclear and lacked sound judgments. This indicates that Indonesian EFL students require more practice in forming well-rounded judgments. Media analysis and discussion sessions were found to be significantly beneficial for enhancing critical literacy instruction.Research limitations/implicationsOne limitation of the study is its focus on a single class of 32 students, which may not be representative of all Indonesian high school students. Future studies should consider expanding the sample size and incorporating diverse educational contexts.Practical implicationsThe findings show that integrating critical media literacy (CML) into ELT not only boosts students’ creativity and critical thinking but also helps them tackle real-world challenges, like identifying misinformation in Indonesia’s digital landscape. To make this approach more practical, the study highlights the importance of teacher-led strategies, such as using guiding questions during discussions and modeling how to create media context. These methods align well with Indonesia’s Merdeka Belajar policy, which encourages flexible, student-centered learning. This research provides valuable insights for educators, policymakers and curriculum designers looking to adapt media literacy to the needs of 21st-century learners across Indonesia’s diverse educational settings.Social implicationsThe study highlights the importance of critical media literacy in fostering a more critically aware and socially responsible youth. By enhancing students’ ability to analyze media and creatively develop well-structured arguments critically, critical media literacy integration can contribute to a more informed and engaged citizenry. This is particularly relevant in today’s media-saturated environment, where the ability to discern and evaluate information critically is crucial for democratic participation and social cohesion.Originality/valueThis research contributes to the limited research on the role of media literacy integration in enhancing students’ creativity and critical literacy within ELT. It highlights the importance of teacher-led discussions in fostering critical thinking skills, providing valuable insights for teachers, practitioners and policymakers aiming to improve media literacy instruction in similar contexts.

Sentiments and sustainability: stakeholder perceptions of sustainable fashion on social media

PurposeThis study aims to deepen the understanding of what stakeholders talk about when it comes to sustainable fashion on social media and how. Sustainable fashion is a broad umbrella term, which can distract attention from the differences between the individual subtopics and the sentiments ascribed to them. However, little systematic research exists on how the stakeholder activity and dominant sentiments vary across different sustainable fashion topics.Design/methodology/approachThis study is based on a social media analysis of 19,179 tweets authored by 1,819 distinct stakeholders on Twitter (now “X”) from 2007 to 2022. A large language model, a type of artificial intelligence (AI) that focuses on understanding and generating human language, is used to conduct a sentiment analysis of six stakeholder groups and 81 keywords linked to sustainable fashion. Two case examples are used to highlight the differences in stakeholder perceptions of sustainable fashion.FindingsThe social media analysis demonstrates how subcategories of sustainable fashion significantly differ in terms of stakeholder interest, activity and sentiments. For instance, tweets on circular economy and relevant subcategories (closed loop, recycling, upcycling, etc.) are popular, whereas issues linked to environmental, social and governance (ESG) and due diligence receive little attention on social media. While sentiments toward sustainable fashion are in general positive, discussions on topics such as labor rights issues are consistently associated with negative sentiments across most stakeholder groups.Originality/valueThis study contributes to the literature by demonstrating how stakeholders and sentiments vary across different topics linked to sustainable fashion on social media, which has become one of the main channels for communicating sustainability content. The findings thereby shed new light on dominant stakeholder positions regarding a wide variety of sustainable fashion topics.

A comparative critical analysis of official and personal social media videos on being and becoming old in China

ABSTRACT This study delves into ageing as it is depicted through social media videos in China. Through a critical analysis of 40 short-form videos, it compares how older people are discursively constructed by official agents and individual content creators to uncover their underlying ideological interests. Findings show that official videos tend to portray older people as socially disconnected and lonely whereas personal videos tend to showcase the jovial and inspirational aspects of growing old. They also reveal that official videos are exclusively situated in urban settings while personal videos also depict older people in rural and relatively modest settings. These findings reflect and reinforce the pathologisation and othering of older people and reveal ideologies linked to communitarianism and governmentality and the commodification of old age in the Chinese socio-political and socio-cultural ecosystem.

Comparative Analysis of Growth Media for Cultivating Acanthamoeba: Implications for Laboratory Diagnostics

Background Acanthamoeba, a genus of free-living amoebae found in soil, freshwater, and marine habitats, is crucial in environmental studies and medical microbiology due to its potential to cause severe human infections. Cultivating Acanthamoeba is crucial for diagnosing infections, guiding treatment, and assessing exposure risks to pathogenic strains. This study aims to compare different culture media for cultivating Acanthamoeba under laboratory conditions. Materials and Methods The study examined ten culture media for Acanthamoeba growth, including TSB, Trypticase soy agar (TSA), Trypticase soy yeast (TSY), Trypticase yeast iron-extracted (TYI), Trypticase, yeast maltose extract (TYM), Pepton Yeast Glucose Extract (PYG), Dulbecco's modified Eagle's medium (DMEM), RPMI 1640, Serum Casein Glucose Yeast Extract (SCGY), Coconut powder suspension in NNA medium, as well as non-nutrient agar (NNA) as the primary & control culture. Results The results showed varied growth rates of Acanthamoeba when cultured in different media, highlighting important trends in their adaptability and growth based on the nutritional composition of the media provided. In TSB, TSA, DMEM/F12 and SCGY media, no growth was observed, suggesting that the combination may lack essential nutrients or conditions necessary for fostering Acanthamoeba proliferation. TYI showed an excellent growth rate for Acanthamoeba, particularly notable during the first 24-72 hours. The use of maltose as a carbon source and FBS in the NNA environment appears to promote rapid proliferation of Acanthamoeba. Coconut powder suspension in NNA medium led to excellent growth rates of Acanthamoeba. Conclusion The effective cultivation of Acanthamoeba in TYI, TYM, and coconut powder highlights the significance of refining nutrient composition to enhance culturing methods.

Analysis of longitudinal social media for monitoring symptoms during a pandemic.

Current studies leveraging social media data for disease monitoring face challenges like noisy colloquial language and insufficient tracking of user disease progression in longitudinal data settings. This study aims to develop a pipeline for collecting, cleaning, and analyzing large-scale longitudinal social media data for disease monitoring, with a focus on COVID-19 pandemic. This pipeline initiates by screening COVID-19 cases from tweets spanning February 1, 2020, to April 30, 2022. Longitudinal data is collected for each patient, two months before and three months after self-reporting. Symptoms are extracted using Name Entity Recognition (NER), followed by denoising with a combination of Graph Convolutional Network (GCN) and Bidirectional Encoder Representations from Transformers (BERT) model to retain only User-experienced Symptom Mentions (USM). Subsequently, symptoms are mapped to standardized medical concepts using the Unified Medical Language System (UMLS). Finally, this study conducts symptom pattern analysis and visualization to illustrate temporal changes in symptom prevalence and co-occurrence. This study identified 191,096 self-reported COVID-19-positive cases from COVID-19-related tweets and retrospectively collected 811,398,280 historical tweets, of which 2,120,964 contained symptoms information. After denoising, 39% (832,287) of symptom-sharing tweets reflected user-experienced mentions. The trained USM model achieved an average F1 score of 0.927. Further analysis revealed a higher prevalence of upper respiratory tract symptoms during the Omicron period compared to the Delta and Wild-type periods. Additionally, there was a pronounced co-occurrence of lower respiratory tract and nervous system symptoms in the Wild-type strain and Delta variant. This study established a robust framework for analyzing longitudinal social media data to monitor symptoms during a pandemic. By integrating denoising of user-experienced symptom mentions, our findings reveal the duration of different symptoms over time and by variant within a cohort of nearly 200,000 patients, providing critical insights into symptom trends that are often difficult to capture through traditional data source.

Assessing justice in sustainable mobility transitions: narratives from transport policies in Jakarta

ABSTRACT Despite the recent growing concern for justice in the sustainable mobility transition, individuals’ experiences of injustice remain inadvertently neglected in favour of maximising the common good for society. As a result, identifying and assessing injustices remain elusive and tricky to operationalise into actionable policy. This article proposes an analytic and evaluation framework for capturing different narratives of injustice resulting from three transport policies aiming for a sustainable mobility transition in Jakarta: (1) traffic restriction policy, (2) integrated public transport, and (3) improvement of walking and cycling infrastructures. Using content analysis of news media to identify how these policies are perceived by and affect various segments of the population, our findings revealed that a broader scope of injustice is experienced by the low-income populace, including those with ‘forced’ motorcycle ownership and captive public transport users. These individualised injustices can result from policies aiming to improve the overall transport condition, claiming sustainable mobility values. Further, our proposed framework highlights shortcomings of current urban and transport planning assessments, which tend to quantify how justice ought to be distributed. The article concludes with policy insights to address transport injustices in Jakarta and other growing Asian metropolises facing a widening socio-economic inequality.

COVERAGE OF THE VOLUNTEER MOVEMENT IN THE MIDST OF THE CONTEMPORARY RUSSO-UKRAINIAN WAR 2014 – 2021

The article is dedicated to a deep analysis of the impact of volunteering on society during the current military conflict between Russia and Ukraine. The article explores volunteering as a complex social phenomenon, revealing its key features and influence on the formation of civil society in crisis conditions.Primarily, the author extensively examines the peculiarities of volunteer activities in Ukraine, uncovering the motivation of volunteers and their role in supporting the armed forces and the civilian population. The article offers a comparative analysis of global volunteerism experiences to identify trends and considers unique aspects of the Ukrainian context. It includes an analysis of various methods of state regulation of volunteer activities in Ukraine, determining how these regulatory measures affect the organizational structure and efficiency of volunteer initiatives.The author explores the motivation of volunteers actively involved in addressing issues related to the military conflict, emphasizing that volunteering becomes a crucial instrument in providing humanitarian aid, medical support, and socio-psychological rehabilitation for the affected individuals.In the context of global volunteering experience, the article highlights the peculiarities of the Ukrainian volunteer movement, emphasizing its role in supporting the army, treating the wounded, and providing material assistance. An analysis of the challenges and opportunities that volunteers face in the conditions of military events reveals their steadfastness and dedication to the cause of communityassistance.The article also explores the mechanisms of state regulation of volunteer activities, identifying the advantages and disadvantages of existing approaches. Content analysis of mass media allows obtaining an objective picture of public opinion about volunteers, identifying stereotypes, and gauging the public's reaction to their activities.The article makes a significant contribution to understanding the role of volunteering in the context of the war between Russia and Ukraine, revealing its socio-cultural and political impact. The research can serve as a foundation for the development of more effective strategies for state support and regulation of volunteer activities in conflict situations and contribute to shaping a positive image of volunteers in society.