Media Analysis Research Articles

Preservation and Development of Russian Language: challenges and prospects for development

The paper is based on an analysis of media and is devoted to the problem of preserving the purity of the Russian language in connection with the preservation of identity and the cultivation of patriotism. The authors identify the connection between the formation and development of language as a factor in the consolidation of the country’s multinational population with cultural and political processes, educational and cultural policies, as well as processes of ensuring internal and external security. The authors draw attention to the low efficiency of mechanisms and tools for implementing state language policy, and formulate proposals for the preservation and development of the Russian language as a factor. The used methods include participant and non-participant observation, document analysis and content analysis. The publication is of interest to cultural, political scientists and to all readers involved into Russian language, history and culture.

A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users’ inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP.

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. This analysis followed systematic review procedures, registered with the Prospective Register of Systematic Reviews. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the Quality Assessment of Diagnostic Accuracy Studies-2 tool for evaluating the quality and robustness of the included studies. Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

Preserving and Promoting Indigenous Languages: Social Media Analysis of Punjabi and Setswana Languages

A treasure of knowledge is lost when a language dies and it is irretrievable. While the majority of indigenous languages are endangered, it is difficult for these languages to survive in the 21st century socially diverse world. This study examines the role of social media in the preservation and promotion of indigenous languages, particularly Punjabi and Setswana, within the context of social media platforms Facebook and X (formally called Twitter). The qualitative approach, using a content analysis method, investigates the linguistic content shared on these platforms. These languages act as channels for language preservation and promotion, navigating time, place and identity in the digital era. Social media platforms, such as X and Facebook, have the potential to rejuvenate and conserve indigenous and endangered languages. The study reveals a rich tapestry of participation, expression, and community development in the digital arena, highlighting the potential of both platforms to preserve and promote indigenous languages. The study also explores aspects such as prosocial impacts, social norms and convincing representations. Overall, this study emphasizes the importance of making fundamental decisions about social media and indigenous language preservation and promotion, emphasizing the mitigation of challenges.

Sentiment analysis of multi social media using machine and deep learning models: a review

Sentiment analysis of multi social media using machine and deep learning models: a review

Fixing things from the outside? Diaspora politicians and transnational political engagement

ABSTRACT Scholarship on diaspora political engagement is unfolding in novel ways exploring the participation of second-generation diaspora individuals and engagement patters of authoritarian and hybrid regimes. Focusing on the unique role of diaspora politicians, I contend that these individuals adeptly leverage their identity to impact politics and advocate for change. The article demonstrates their adaptability in different settings and evolving advocacy strategies. The article employs a multilingual analysis of social media, public interviews, and public appearances in Germany and Bosnia and Herzegovina using an exploratory case study of a German-Bosnian politician. This exploration underscores their flexibility and evolving advocacy dynamics. Furthermore, it illustrates the way these politicians enhance their profiles within the countries where they’ve been elected and in countries of their descent. This serves to strengthen their chances of re-election on one hand while also drawing attention to authoritarian tendencies, potentially clearing a path for democratic advancement on the other.

A Mathematical Formulation of Classified Variables for Enhanced Detection in Natural Language Steganalysis

Steganalysis, which is the counterpart to steganography, is an art and science that is devoted to detecting hidden information that has been concealed within seemingly harmless digital media. With the advancement in technology nowadays, the techniques used in steganography also become more complex, making it necessary to continuously improve steganalysis methods to keep up with emerging threats and ensure digital data's confidentiality, authenticity, and privacy. Steganalysts face the challenge of uncovering hidden information within covert media, requiring analysis of both original and altered media. While efficient steganalysis tools exist for specific approaches, devising a universal solution for all steganography techniques remains challenging. With the utilization of the formulated key, this paper aims to classify and analyze variables used in the steganalytic system. Thus, three views of classified variables are presented to address the pattern of detection. These are known as trade-off value-based, probability of character variable, and Support Vector Machine-based (SVM-based). Hence, it is expected that this scheme will become one of the alternative ways to enhance the steganalytic system for discovering the hidden message in communication. Furthermore, it is suggested the necessity for enhanced steganalysis techniques and tools, as well as the significance of academic and professional education in this area.

Media Framing of African Refugee Migration to Europe: An Analysis of Narrative Constructions From Selected Global Media Houses in 2023

The framing of migrants from Africa to Europe on the global media is complex and quite obscure. The manner in which media tells a story is quite critical as it has the potential to influence the discernment of the public from different parts of the world. The main goal of the study was to examine how the global media houses portrayed African migrants to Europe in 2023.This was guided by two specific objectives:To examine the global media outlets' coverage of African refugee migration to Europe and to analyze the language used by the media to describe African refugees, assessing for bias, stereotypes, or stigmatization. The study was guided by the framing theory. The study employed a qualitative method design to conduct a detailed analysis of media framing surrounding African migration to Europe. Data was collected from all the global media houses with a sampling frame from three major media houses which included: The Cable News Network (CNN), The British Broadcasting Corporation (BBC), and the Eastern Africa. Data was analyzed using thematic content analysis. Findings indicated that, how global media houses cover the migration of people from Africa to Europe exposes the intricate narrative formed by a variety of both negative and positive views. While the CNN and BBC comprise varied perspectives to the issues, The Eastern Africa gives insight into the regional circumstances surrounding the migration problem as well as the humanitarian concerns. Across all the examined media houses (CNN, BBC, The Eastern Africa), there is an inclination towards portraying the African migrants in negative light, underscoring their impact on destination states. There were instances of Bias and stereotypes that could potentially lead to stigmatization. The study recommended that there is need for balanced reporting from media houses, there needs for some ethical considerations in reporting and that media houses should mainly focus on their role of creating awareness and advocacy.

Public perceptions of justification in the deadly police shootings of black men: a content analysis of social media posts in 2016 and 2020

The proliferation of information technologies has revolutionized how people around the world consume news. By 2021, an estimated 8.6 billion mobile subscriptions existed worldwide. In the United States, a majority (84%) of Americans access their news digitally. On social media platforms, Facebook is the most used network for obtaining news. The three major news sources in the United States, CNN, Fox News, and MSNC, have a strong presence on Facebook. The current study analyzes public comments posted on Facebook involving the deadly police shootings of four Black men (i.e., Alton Sterling, Philando Castile, Jonathan Price, Rayshard Brooks) to identify reactions at two points in time. Our work centers on public comments to these posts, not the posts themselves. Using a summative qualitative analysis, we analyzed the first 20 Facebook comments from CNN, Fox News, and MSNBC that are listed as most relevant (e.g., highest number of likes, reactions, replies) to the most recently posted news story regarding the incident. This resulted in a sample size of 223 posts. Public comments were categorized as justifying the killing, perceiving the killing as not justified, or expressing neutrality as to whether the killing was justified or not. We found that most comments, regardless of time period, did not engage in victim-blaming. Overall, the majority (N = 139 or 62%) of all comments perceived these deadly police shootings as not justified. Most (54%) posts on Fox News justified this violence, while the majority of posts on CNN or MSNBC felt the police had used excessive violence. There were more negative comments of Black men and victim-blaming after the Trump presidency (in 2020) than in 2016 (before Trump took office), which may be related to the racial rhetoric of the times.

Pseudo-positive Information and COVID-19: Reasons behind Sharing Fake News (Georgian Social Media Analysis)

A plethora of studies state that a perceptible gap can be observed concerning disinformation influence analysis, which is presented by a lack of research on this phenomenon. In a crisis precipitated by the pandemic disruption, the necessity to scrutinize these topics becomes particularly transparent, as an individual’s critical thinking is incapacitated, which leads to a surge in fake news sharing. The article aims to expose and investigate the characteristics of a new, pseudo-positive disposition of false information and the reasons behind its extensive dissemination. By analyzing the original sources of fake news published on "Facebook", conducting an in-depth interview with eight field experts, integrated with a small survey of 204 Georgians, we identify that to overcome the pandemic-induced stress and create an optimistic environment, any positive information that is apprehensible under these conditions becomes effortlessly shareable and consequently, pseudo-positivity is utilized as a manipulator to foment a wave of disinfodemy. Keywords: disinformation, pseudo-positivity, social media, COVID-19, manipulation, crisis, Facebook, mental health, media psychology

Exploring Causal Relationships in Mental Health Literacy Through Twitter Content: A Machine Learning Approach

IntroductionThe concept of Mental Health Literacy (MHL) is inherently multidimensional. However, the interrelationships among its various dimensions remain insufficiently elucidated. In recent years, the textual analysis of social media posts has emerged as a promising methodological approach for longitudinal research in this domain.ObjectivesThis study aimed to investigate whether temporal causal associations exist between recognition of mental illness (R), mental illness stigma (S), help-seeking efficacy (HE), maintenance of positive mental health (M), and help-seeking attitude (HA).MethodsTweets were collocted at three distinct time points: T1, T2, and T3, spanning the period from November 1, 2021, to December 31, 2022. We employed a machine-learning approach to categorize the posts into five MHL facets. Using these facets, we trained a machine learning model, specifically Bidirectional Encoder Representations from Transformers (BERT), to determine the MHL scores. To be eligible, an account must have an R facet score at T1, and M, S, HE facet scores at T2, as well as an HA facet score at T3. In total, we retrieved 4,471,951 MHL-related tweets from 941 users. We further employed structural equation modeling to validate the causal relationships within the MHL framework.ResultsIn the evaluation, BERT achieved average accuracy scores exceeding 89% across the five MHL facets in the validation set, along with F1-scores ranging between 0.75 and 0.89. Among the five MHL facets—maintenance of positive mental health, recognition of mental illness, help-seeking efficacy, and help-seeking attitudes—each demonstrated a statistically significant positive correlation with the others. Conversely, mental illness stigma exhibited a statistically significant negative correlation with the remaining four facets. In the analysis using single-mediation models, each of the individual mediator variables—namely, mental illness stigma, help-seeking efficacy, and maintenance of positive mental health—exhibited significant indirect effects. In the multiple-mediation model, two mediator variables—help-seeking efficacy and maintenance of positive mental health—demonstrated significant indirect effects. These findings suggested that the recognition of mental illness exerted an influence on help-seeking attitudes through one or more of these mediators.ConclusionsBy leveraging machine learning techniques for the textual analysis of social media and employing a longitudinal research design with panel data, this study elucidates the potential mechanisms through which the MHL framework influences attitudes toward seeking mental health services. These insights hold significant implications for the design of future interventions and the development of targeted policies aimed at promoting help-seeking behaviors.Disclosure of InterestNone Declared

Media analysis of the text on the example of Chinese strategy of information warfare

The topic of media analysis of information wars in the context of the Chinese issue is of academic interest, both for understanding current events and for improving information analysis methods and media strategies. The article assesses the influence of media on China's behavior in the information war, characterizes the challenges associated with media analysis in China's information war, describes methods and metrics for analyzing media content about the information war in China. Information technologies at the current level of development create broad opportunities for managing political conflicts both on the scale of one state and at the regional level. These technologies, which are actively used in information wars, have a number of advantages over traditional means of struggle due to their massive impact on the moral and psychological state of the population of the enemy country or individual parties, movements and political associations. The article uses a discourse analysis of the media text, identifies expressive linguistic means, the semantic meaning of words and expressions, and the communicative orientation of the text. The empirical basis of the research is the article "Celestial Truth: who and why is waging an information war with the PRC". The main conclusion of the study is that modern technologies and social media have become a powerful tool for influencing public opinion and shaping the perspective of events. The information war in China has a global impact, attracting the attention of the international community. Global media and international platforms actively monitor and cover events in the region, which can influence the perception of the conflict beyond its borders. The impact of the information war has a significant impact on public opinion. There is the use of various methods, including fake news, disinformation, to influence public sentiment and views. The Chinese concept of network warfare opens up further prospects and opportunities for the Chinese leadership and military specialists to work on the creation and implementation of the latest technology samples.

The shitposting scattergun approach: A multimodal analysis of the first wave COVID-19 social media messaging from UKGov

This article analyses the type and style of the UK government social media messaging during the first wave of the COVID-19 pandemic using multimodal and social semiotic analysis. Images improve health communication by increasing comprehension of information, enhancing recall of instructions, encouraging positive self-protective attitudes and behaviour and are beneficial to those with low health literacy skills. When done well, visual material can meaningfully improve health outcomes. UKGov’s social media during COVID-19 reveals that production of mental noise and feed flooding can affect perceptions of risk. Recognizing the impact that both visuals and social media campaigns can have in crisis communication, this article first explores the steps in creating the social communication and then analyses social media posts during the first four months of the pandemic from UKGov Facebook and Instagram accounts. This article employs a multimodal social semiotic methodology to organize, code and make sense of the often contradictory and ambiguous meanings contained in a vast trove of visual data. Ultimately, this article argues that the UKGov’s ‘scattergun’ approach to the first wave COVID-19 infographics enhanced polysemic meanings and flooded social media feeds with ‘shitposting’ philosophy, which decreased clarity of understanding and potentially impacted health literacy amongst the UK populous.

An Analysis of True Crime’s Treatment of Murder Victims

True crime is a non-fiction genre that focuses on real crimes and those who were involved. As one may expect, this brings attention to victims of violent crimes. With such a delicate topic, true crime needs to maintain ethical practices. Many have speculated that victims are disrespected in these retellings, but in pre-existing research, no one has measured the extent of this disrespect. As such, the following question presented itself: What is the extent of murder victim maltreatment by true crime media present on Hulu as of 2022? To answer this question, a quantitative, exploratory media analysis was completed through the use of a codebook, which assigns point values that can be combined into an overall maltreatment score. The main focus of this codebook was the narrative and imagery shown throughout an episode. Starting with all true crime series, the sample size was narrowed down to five series. These five series were coded and received an overall maltreatment score. It was found that on average, these five series displayed a substantial amount of maltreatment towards murder victims. Additionally, further analysis showed that the treatment of victims was somewhat dependent on the killers. Not only does this demonstrate an injustice to the victims themselves, but also to those that knew them.

Preparing for the Coming Storm

New age spirituality has long involved ecospirituality and nature religion. It has also typically been associated with left-wing politics. However, I argue that this political assumption is no longer tenable, and that right-wing political positions, particularly libertarianism, are a prominent feature of contemporary new age spirituality. People involved in new age spirituality remain committed to nature and ecospirituality, but they now fuse these commitments with conspiracy theories, providing elaborate explanations for the current ecological crisis beyond those validated by environmental science. The mix of conspiracy theories and new age spirituality has been termed ‘conspirituality’ by scholars, and I argue this overlap accounts for how the ecologism in new age spirituality can become ecofascism. Methodologically grounded in long-term ethnographic fieldwork in Arizona conducted since 2012 and in content analysis of interviews and digital media, I explore how and why environmental consciousness in new age spirituality has taken this direction.

Manipulation of lexical choice aspects in the creation of stereotypes and ideas in Russian propaganda

The aim of the research is to analyse the mechanisms of manipulation of lexical choice aspects in the creation of mass stereotypes and ideas in Russian propaganda (based on examples of the analysis of Russian mass media and blogs). The research employed the methods of quantitative, comparative, and statistical analysis. In the course of the study, results were obtained in the form of a count of lexical units used in five sources of Russian propaganda and an assessment of their meaning. The results give reason to draw a conclusion about the use of certain lexical choices (euphemisms, emotionally charged vocabulary, slogans, hate speech, historical myths and falsifications) by Russian propaganda to redefine military realities and justify the war crimes of the Russian occupation army. The academic novelty of the study is the selected various academic sources to illustrate the imperial thinking of Russian mass media correspondents. Prospects. Further research may diversify the materials for the lexical analysis of the mechanisms of Russian propaganda in mass media.

Unmasking DeepFakes Using Machine Learning

Our task objectives to deal with the trouble of Deepfake videos, which can be inflicting fear because of their capacity to unfold fake records and manage human beings. Deepfake veideos appearance so actual that it`s tough to inform them other than actual ones, making them a severe danger. This sensible look can result in sizable damage .We are using Machine Learning Algorithms discover Deepfake videos. This entails the usage of era to differentiate among actual and manipulated content. By growing this, we lessen the effect of incorrect information and manipulation, safeguarding human beings from cappotential damage as a result of deceptive content. Keywords: Deepfake Detection, Machine Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Facial Analysis, Anomaly Detection, Synthetic Media, Image and Video Analysis, Temporal and Spatial Patterns, Training Datasets, Real-time Detection, Visual Content Security

Decoding paradoxical identities: The discourse construction of left-behind children in news reports

This paper presents an empirical corpus-based study on the identity of left-behind children in China, employing the discourse-historical approach (DHA) framework. It is the first study to combine Latent Dirichlet Allocation (LDA) topic modeling and reported speech analysis to examine how China Daily constructs the identity of left-behind children and explore the cultural and rhetorical factors influencing this identity construction. By integrating computational techniques and qualitative analysis methods, this study provides a comprehensive understanding of the intricate nature of identity construction for left-behind children. This interdisciplinary approach strengthens the theoretical foundations of media analysis and offers fresh insights into the dynamics of media discourse. The findings reveal that China Daily portrays left-behind children in a multifaceted and diverse manner, encompassing both positive and negative aspects, while placing emphasis on their vulnerability and passivity. Furthermore, the media employs communication techniques such as identification by sympathy, antithesis, and inaccuracy to establish emotional resonance and foster audience identification, ultimately influencing the audience’s perspectives on these children.

Global South Responses to China’s BRI Projects: A Case Study of Jakarta-Bandung High-Speed Railway Project

China's rise as a global power in the 21st century, marked by its economic growth and political influence, has been exemplified through initiatives like the Belt and Road Initiative (BRI). The BRI has extended China's geoeconomic and geopolitical influence across Asia, Africa, Latin America, and Europe. While existing literature predominantly focuses on China's actions, this study shifts the spotlight to Global South nations' responses and strategic policies, particularly Indonesia, in the context of China's BRI projects. Drawing upon in-depth interviews, literature review, and social media analysis, this research examines how Indonesia has actively shaped the Jakarta-Bandung High-Speed Railway project within the BRI framework to pursue its objectives and interests. The study's findings highlight that the Indonesian government, in alignment with the principles of the BRI, is actively utilizing the Jakarta-Bandung High-Speed Railway project to advance its New Developmentalism approach under President Jokowi's administration. This approach embodies a narrow perspective of development by focusing on infrastructure and economic growth while ignoring political and environmental considerations. Concurrently, the project has encountered resistance from local communities, underscoring the contrasting interests between the government and the public and adding a nuanced layer to the dynamics of the BRI. This case study sheds light on the complex interplay of interests and power dynamics within the context of China's BRI projects in the Global South. It underscores the importance of considering the responses and agency of host nations and local communities in shaping the outcomes of BRI initiatives.

Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curaçao

Land use change is a major driver of environmental degradation, necessitating appropriate planning to navigate trade-offs between societal objectives and ecological impacts. Sound planning is limited in some regions by data scarcity and incomplete scientific knowledge on local dynamics shaping development of land. In this paper, we present a novel expert-based participatory approach that uses Bayesian networks to determine land use suitability and potential conflicts for emerging land uses. This method encompasses a workshop phase for building suitability models for different sectors, data assembly and preparation, spatialization of networks, and iterative validation with experts. Mapped suitabilities for all land uses were used to assess potential competition for land across sectors and to quantify alignment of the expert-modeled outcomes with established land use policy. Applied to Curaçao, a data-poor environment in the Caribbean facing high land use competition, the method enabled the construction and parameterization of 5 Bayesian networks driven by 35 spatial input datasets generated through various methods from participatory mapping to social media analysis. Overlap in suitable locations for conservation and tourism development along segments of the coastline and roadsides of the western island highlight potential conflict stemming from coincidence of desirable natural amenities and ecologically sensitive areas. Results yield key insights that can drive discussion and inform policymakers and spatial planners as they navigate tradeoffs and seek optimal use of limited land resources. Process-based suitability predictions and knowledge of underlying drivers can also enable exploratory analysis into possible future scenarios of change.