Media Analysis Research Articles

Public conversation on vaccines during the covid-19 pandemic in Argentina, 2021-2022

In the face of declining vaccination coverage and the dissemination of health-related information, conversations in the public/mediatic digital sphere constitute a relevant study area for the field of health communication. Through a qualitative study based on the analysis of government publications, digital press, and social media, we characterize the public conversation on vaccines - in terms of topics, moments, axes, and framings in Argentina during the 2020-2021 period - marked by the debate on covid-19 vaccines. The results show that public conversation focused on covid-19 vaccination, structured in two distinct moments (vaccine production and vaccination campaign), and under moral framings grounded in vaccination as a care practice and science as an authoritative voice. Simultaneously, doubts about the safety and efficacy of vaccines shaped arguments of vaccine hesitancy, which we understand as part of extended practices associated with distrust towards institutions and reinterpretations of scientific knowledge and care.

Образ «чужого» в инфраструктуре российского города (на примере Челябинской области)

The article aims to identify the criteria of the "image" of a migrant and "migrant" infrastructure, to describe the problems of interethnic relations, integration of migrants from Central Asia in a Russian city (using the examples of cities in Chelyabinsk region). The author relies on 540 interviews with the local population of six cities of the region (Chelyabinsk, Kopeysk, Magnitogorsk, Miass, Zlatoust, Troitsk), as well as the results of observations, content analysis of mass media and Internet resources. The quantitative and qualitative characteristics revealed in the course of the research allowed us to characterise the image of the "alien", the structure of urban space in the dichotomy "native"-"alien", to identify the problems of interethnic relations, to describe which objects are given the status of "migrant" and through what markers this happens. Particular attention is paid to the image of the "alien" in the media and various Internet resources, which largely shape public attitudes towards migrants and where the latter are most often endowed with negative characteristics (settling by large families and thus "taking over" entire city districts, illegally occupying jobs and entire spheres of the regional economy). It is concluded that the image of the "alien" and "migrant" infrastructure in the media is formed under the influence of national, ethnic and class stereotyping, a limited set of life contexts, impersonality and passivity of migrants. It was revealed that a significant part of negative attitudes of the local population towards migrants is projected onto quite specific objects of urban infrastructure (markets, educational and medical institutions, "dangerous" neighbourhoods, etc.) and the spheres of activity (shuttle buses, trade, construction, etc.).

Periacetabular osteotomy: an analysis of social media to determine the most common questions asked by the periacetabular osteotomy population

BackgroundThe Bernese Periacetabular Osteotomy (PAO) has become a popular surgery for fixing development dysplasia of the hip, yet the most common concerns of the PAO population remains ambiguous. The aim of this study was to investigate Facebook, Instagram and Twitter to further understand what the most common preoperative and postoperative questions patients undergoing PAO are asking. We hypothesized most questions would be asked by patients in the preoperative timeframe with regards to education surrounding PAO surgery.MethodsFacebook, Instagram and Twitter were queried consecutively from February 1, 2023 to November 23, 2011. Facebook was searched for the two most populated interest groups; “Periacetabular Osteotomy (PAO)” and “Periacetabular Osteotomy Australia”. Instagram and Twitter were queried for the most popular hashtags: “#PAOwarrior”, “#PAOsurgery”, “#periacetabularosteotomy”, “#periacetabularosteotomyrecovery”, and “#paorecovery”. Patient questions were categorized according to preoperative and postoperative questions. Questions were further placed into specific themes in their respective preoperative or postoperative question types.ResultsTwo thousand five hundred and fifty-nine posts were collected, with 849 (33%) posts containing 966 questions. Of the 966 questions, 443 (45.9%) and 523 (54.1%) were preoperative and postoperative questions, respectively. The majority of questions were postoperative complication related (23%) and symptom management (21%). Other postoperative questions included recovery/rehabilitation (21%), and general postoperative questions (18%). The most common preoperative questions were related to PAO education (23%). Rehabilitation (19%), hip dysplasia education (17%), and surgeon selection (12%) were other preoperative questions topics included. Most questions came from Facebook posts. Of 1,054 Facebook posts, 76% were either preoperative or postoperative questions and from the perspective of the patient (87%).ConclusionThe majority of patients in the PAO population sought advice on postoperative complications and symptom management. Some patients asked about education surrounding PAO surgery. Understanding the most common concerns and questions patients have can help providers educate patients and focus on more patient-relevant perioperative conversations.

Revolutionizing Research in Social Sciences: The Transformative Potential of ChatGPT

This paper explores ChatGPT's transformative impact on social science research, focusing on its language generation capabilities. It emphasizes the tool's proficiency in data analysis, idea generation, and questionnaire development, investigating its role in unravelling human behaviour, social dynamics, and cultural phenomena. Notably, ChatGPT integrates into social media analysis, illuminating trends and sentiment shifts. The study critically examines its contribution to streamlining qualitative analysis, highlighting the benefits and risks of automated coding. Ethical considerations, such as bias and algorithmic transparency, are rigorously scrutinized, advocating for interdisciplinary collaboration to fortify ethical foundations. The paper delves into ChatGPT's influence on policy-making, emphasizing caution in relying on AI-generated recommendations in healthcare and education. It calls for a balanced synergy between AI capabilities and human expertise, recognizing their unique strengths. In conclusion, the paper asserts the critical importance of fostering a symbiotic relationship between AI and human researchers as an essential factor for advancing research responsibly within the dynamic technological landscape.

Social media analysis of pain outcomes following targeted muscle reinnervation

AimTMR was developed to improve myoelectric prosthesis control for amputees; however, it has become an area of interest in pain modulation. Studies show evidence that this procedure alleviates chronic pain in amputees. The primary objective of this study is to utilize social media analysis to understand patients’ post-operative pain, satisfaction, and recovery time after TMR. MethodsData was collected from one Facebook group via posts and comments referencing TMR. Posts published between January 1st, 2020, and March 24th, 2023, were analyzed. Data collected included pain prior to surgery, pain in immediate post-op period, and how pain changed after surgery. Results43 individuals commented on their TMR experience. 31 had favorable surgical outcomes, 7 felt surgery worsened their pain or there was not significant change in their pain levels, and the remaining 5 commented during the initial post-operative period. 24 patients described their pain in the immediate post-operative period and 100% said that post-operative pain was worse than chronic pain. Of 28 authors who commented on overall reduction in chronic pain, 24 reported TMR reduced their pain, while 4 had no change or worsened pain. ConclusionsThe 24 patients who reported improvement in chronic pain aligns with current literature suggesting that TMR is a viable treatment option for pain. Current medical management of similar conditions leaves up to 80% of patients unsatisfied with pain control. This analysis supports the evidence that TMR is an effective treatment for patients who are experiencing post-amputation pain.

Activity of a Recombinant Chitinase of the Atta sexdens Ant on Different Forms of Chitin and Its Fungicidal Effect against Lasiodiplodia theobromae.

This study evaluates the activity of a recombinant chitinase from the leaf-cutting ant Atta sexdens (AsChtII-C4B1) against colloidal and solid α- and β-chitin substrates. 1H NMR analyses of the reaction media showed the formation of N-acetylglucosamine (GlcNAc) as the hydrolysis product. Viscometry analyses revealed a reduction in the viscosity of chitin solutions, indicating that the enzyme decreases their molecular masses. Both solid state 13C NMR and XRD analyses showed minor differences in chitin crystallinity pre- and post-reaction, indicative of partial hydrolysis under the studied conditions, resulting in the formation of GlcNAc and a reduction in molecular mass. However, the enzyme was unable to completely degrade the chitin samples, as they retained most of their solid-state structure. It was also observed that the enzyme acts progressively and with a greater activity on α-chitin than on β-chitin. AsChtII-C4B1 significantly changed the hyphae of the phytopathogenic fungus Lasiodiplodia theobromae, hindering its growth in both solid and liquid media and reducing its dry biomass by approximately 61%. The results demonstrate that AsChtII-C4B1 could be applied as an agent for the bioproduction of chitin derivatives and as a potential antifungal agent.

Emerging trends in social media marketing: a retrospective review using data mining and bibliometric analysis

The study conducts a comprehensive retrospective analysis of the social media marketing literature along with text mining and bibliometric analysis using data obtained from the Scopus database. The analysis is conducted for the literature published during 2007–2022 using VOSviewer application and Biblioshiny. The analysis revealed the publication trend and emerging themes in the research landscape of social media marketing. This study has pointed towards important theoretical and practical implications pertaining to the social media marketing. It contributes to the understanding of social media marketing research by identifying and listing the best journal, authors, country, documents, most occurred words, social and intellectual structure, and emerging research trends. The results revealed that social media marketing research is at the focal point of the researchers throughout the word. This study found that there are lack of studies from firm perspective especially small retailers; adoption of disruptive technologies such as AI, ML and block chain and its impact need more exploration.

A model for new media data mining and analysis in online English teaching using long short-term memory (LSTM) network.

To maintain a harmonious teacher-student relationship and enable educators to gain a more insightful understanding of students' learning progress, this study collects data from learners utilizing the software through a network platform. These data are mainly formed by the user's learning characteristics, combined with the screen lighting time, built-in inertial sensor attitude, signal strength, network strength and other multi-dimensional characteristics to form the learning observation value, so as to analyze the corresponding learning state, so that teachers can carry out targeted teaching improvement. The article introduces an intelligent classification approach for learning time series, leveraging long short-term memory (LSTM) as the foundation of a deep network model. This model intelligently recognizes the learning status of students. The test results demonstrate that the proposed model achieves highly precise time series recognition using relatively straightforward features. This precision, exceeding 95%, is of significant importance for future applications in learning state recognition, aiding teachers in gaining an intelligent grasp of students' learning status.

Depoliticization and Necropolitics: A Critical Examination of Lukashenka’s Regime

ABSTRACT This article examines the intertwined logics of depoliticization and necropolitics as central elements of illiberal authoritarian power in post-2020 Belarus. It sheds light on the technologies used by Lukashenka’s regime to maintain its rule, including anti-feminist visuals, violence of mass incarceration, and dehumanization and demonization of political opponents. Relying on media analysis, the article discusses how these technologies function as mechanisms through which the regime enacts its necropolitical power to curtail any political engagement. Juxtaposing politicization with depoliticization and biopolitics with necropolitics, it advances a different understanding of these concepts in illiberal autocracies, emphasizing their mutually reinforcing relationship in Belarus.

Place-oriented digital agency: Residents’ use of digital means to enhance neighbourhood change

The smart city literature mostly focusses on digital initiatives from above. However, digitalisation also reshapes the city from below. Residents use digital means and platforms to empower their agency in the city. This paper aims to explore how residents utilise digital tools to activate their agency and influence local politics. The paper focusses on one neighbourhood in the city of Tel Aviv where different groups of residents struggle to promote their desired political-spatial vision. The main question is whether digitalisation produces new forms of agency on the neighbourhood scale. The paper argues that: (1) digitalisation provides residents with new forms of connective action, creating digital networks at different scales, using representational practices and forming new spaces for political negotiation; (2) through these practices, they manage to influence the symbolic and political status of the neighbourhood and reframe the struggle over its character and future; (3) groups with higher digital agency gain wider visibility of their claims and needs with politicians, the media and public officials. Importantly, this does not only serve middle-class groups. (4) Ultimately, residents become predominant political actors through digital agency. Methodologically, this paper includes two methods: (i) interviews with residents and municipal workers and (ii) social media analysis and online ethnography. The conclusion elaborates the concept of place-oriented digital agency as a particular type of agency aimed at determining change in a specific locale.

Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis

The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.

Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.

Analyzing Japanese Media Representation of The Israel-Hamas Conflict: A Linguistic Study of Online Sources

This paper examines Japanese media coverage and analysis of the recent Israel-Hamas war. Specifically, it argues that the Japanese public, media and government are being exposed to a distorted view of Islam and Muslim countries. For instance, Japanese academia seems captivated by anti-Americanism and anti-Semitism, leading to the radicalization of students. This, in turn, results in distorted perspectives on Muslim affairs for the Japanese people, media and government. This paper will focus on a case study from YouTube conducted by Akari Iiyama, a Japanese author. It will illustrate how her analysis unveils a distorted perspective of the Israel-Hamas war. Further, this study suggests that media reports play a crucial role in our comprehension of language dynamics in contemporary everyday life. It aims to enhance our understanding, especially in the context of Israel-Hamas war reporting, of the theory of speech style in socio-linguistics. Specifically, the theory of “involvement” will be discussed in relation to Japanese web-based data.

Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics

PurposeThe study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.Design/methodology/approachBy conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.Findings(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.Originality/valueThis study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

Exploring the information needs of patients with osteoarthritis of the knee: a content analysis of Facebook group posts

BackgroundEnd-stage osteoarthritis of the knee (OAK) is often treated by total knee arthroplasty (TKA). This intervention can significantly improve quality of life. However, many patients are dissatisfied with the outcome of surgery. One of the factors related to dissatisfaction is the of lack integration of patients’ preferences, habits and values that are not addressed by physicians. To develop realistic expectations, affected patients need evidence-based information. Our aim was to explore the information needs of patients with OAK to support the development of decision aids and consent forms to promote informed decision-making. Additionally, we investigated whether the information needs during the Covid-19 pandemic differ from those before the pandemic.MethodsThe qualitative research design included a social media analysis of Facebook groups. Facebook groups were selected according to the following criteria: Thematic relevance, English or German language, at least one new post per week, from period before and after the start of the Covid-19 pandemic in March 2020. Thematically relevant group posts were analysed according to the content-structuring content analysis of Kuckartz using MaxQDA.ResultsOut of 448 identified Facebook groups, we screened seven for relevant posts and a total of 77 posts out of 6 groups were selected. The following eight categories were derived during the coding process: access to health care, disease information, TKA indication and contraindication, TKA outcome and quality of life, information needs regarding conservative therapy, strain, attitude towards TKA and attitude towards conservative therapy. The analysis showed that patients with OAK need information about the benefits and risks of TKA and conservative therapies.ConclusionThis study provides information on the information needs of patients with OAK in order to decide between TKA or conservative therapy. Patients need information about treatment options in due consideration of their immediate living situation to be reliably able to assess potential outcomes. Such Information about TKA should enable patients to assess the individual prognosis with comprehensible and relevant outcome measures. Also, they should be formulated with the living environment of the patients in mind and be linked to possible fears and negative previous experiences with treatments.

Secreted Protein Profiling of Human Aortic Smooth Muscle Cells Identifies Vascular Disease Associations.

Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular disease, the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a contractile to a synthetic phenotype characterized by an increased proliferation, migration, production of ECM (extracellular matrix) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of cardiovascular disease, including coronary artery disease, stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies. Using human aortic SMCs from 123 multiancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted liquid chromatography-tandem mass spectrometry-based proteomic analysis of the conditioned media. We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 (latent-transforming growth factor beta-binding protein 1) in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions. Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.

The framings of the coexistence of agrifood models: a computational analysis of French media

The framings of the coexistence of agrifood models: a computational analysis of French media

Text emotional analysis in Natural Language Processing

Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence, aimed at enabling computers to understand and process human natural language. Emotional analysis refers to the use of computer technology to automatically recognize and classify the emotional tendencies expressed in text, such as positive, negative, or neutral. This technology can be applied to fields such as social media analysis, public opinion monitoring, market research, etc. The goal is to enable computers to understand and interpret emotional information in text like humans. Text sentiment analysis, as a key task of NLP, aims to identify and classify emotional tendencies expressed in text. This article will delve into the field of text sentiment analysis in natural language processing, focusing on the role and application of standard sentiment dictionaries and corpora in sentiment analysis. Reviewing relevant research conclusions, providing reference and inspiration for achieving more accurate and comprehensive text sentiment analysis through research and exploration.

An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.

Everyday Europeanization and Bottom-Up Geopolitics at the Ukrainian-Polish Border

ABSTRACT Relations between Ukraine and Poland, despite the burden of a difficult past and sometimes diverging economic interests, have proved their geostrategic significance for both sides. The importance of the local dimension of these relations – multiple horizontal ties and cooperation projects, mass cross-border movement, dense social networks and transnational family relationships – became particularly clear in spring 2022, when the border turned into a life-saving corridor and a site of everyday solidarity. This article explores how the Ukrainian-Polish border, historically a product of great power deals and nowadays an external border of the NATO and the European Union, is lived, experienced, and perceived from below in two Ukrainian near-border towns, Sambir and Chervonohrad. The article is based on a conceptual approach that combines ‘everyday Europeanization’ and ‘bottom-up geopolitics’ with the notion of the border as a historically multi-layered and dynamic phenomenon. Drawing on interviews with local elites and residents, participant observation, and content analysis of the local media, as well as focus groups conducted in 2021 and in 2023, I show how the geopolitical conflict between Russia and the West is experienced in a region where it strongly resonates with the traumatic past and where the livelihood of the population depends on cross-border small trade and labour migration.