Media Analysis Research Articles

Synthetic Media Analysis Using Deep Learning

In an era characterized by the rapid evolution of digital content creation, synthetic media, particularly deepfake videos, present a formidable challenge to the veracity and integrity of online information. Addressing this challenge requires sophisticated analytical techniques capable of discerning between authentic and manipulated media. This research paper presents a comprehensive study on synthetic media analysis leveraging deep learning methodologies. The suggested method integrates advanced deep learning models, including Convolutional Neural Networks (CNNs) like VGG (Visual Geometry Group), alongside recurrent structures such as LSTM (Long Short-Term Memory). These models are trained and evaluated on a meticulously curated dataset, ensuring diversity and relevance in the synthetic media samples. To facilitate experimentation and reproducibility, the dataset is securely hosted on a reliable platform such as Google Drive. Prior to model training, preprocessing steps including frame extraction are employed to extract essential visual features from the video data. The VGG model serves as a feature extractor, capturing high-level representations of visual content, while the LSTM model learns temporal dependencies and contextual information across frames. Following comprehensive experimentation, the proposed method's ability to detect synthetic media is thoroughly assessed, utilizing metrics such as accuracy. This research contributes to the ongoing discourse on digital media forensics by providing insights into the efficacy of deep learning techniques for synthetic media analysis. The findings underscore the importance of continuous research and development in combating the proliferation of synthetic media, thereby safeguarding the authenticity and trustworthiness of online content. Index Terms—CNN, LSTM, VGG

Amplifying victim–survivor voices: media power, collective action, and ultra-Orthodox Jewish identity in the Leifer case

This article underscores the transformative impact of victim–survivor voices in reshaping public discourse on child sexual abuse (CSA). The research project took as the backbone for analysis the Malka Leifer case that spanned 15 years and is linked to the Australian Royal Commission into Institutional Responses to Child Sexual Abuse's report of Case Study 22, which examined responses in ultra-Orthodox Jewish schools to child sexual abuse. Adopting a mixed methods research approach, this study combines qualitative media analysis of 102 news articles and 8 in-depth focus groups to investigate the impact of media outlets in amplifying victim voice and influencing public discourse, and how this impacts the subjects of mediatised public crises. Drawing on the theorising of Couldry and Cottle, the article considers the capacity and limitations of survivor-advocates to leverage media power in the contemporary media system. By exploring the ‘Privileging Victim Voice’ frame, this paper sheds light on how victim–survivor advocates utilised mainstream, local religious, and social media to solidify their central place in the narrative and its reportage. The media analysis served as the foundation for a ‘peer conversation’ style of focus groups with Jewish community members to investigate local impacts of the case's media reportage. The focus group methodology sought to represent this diverse community as wholly as possible. Findings reveal the significant power of journalists’ framing and sourcing practices, and how Jewish institutional child sexual abuse is framed by media outlets within the Australian media landscape. Further, it showcases the broader implications of public inquiries, such as Australia's Royal Commission, in empowering victim–survivors and centreing their narratives in media reportage.

Measuring the evolution of Arab States’ perceptions of the Iranian threat: a quantitative text analysis of Arabic-language state media, 2010–20

ABSTRACT After the Arab Uprisings, inter-state relations in the Middle East became polarized between the two camps of the Saudi-led Gulf Cooperation Council (GCC) and Iran and its allies, which strengthened the threat perception of Iran among Arab states. This study aims to empirically elucidate the changes in the perception of threats against Iran among Arab states through a quantitative text analysis of major state media in 10 Arab states. Through this analysis, we first substantiated that the Arab threat perception of Iran increased, especially in GCC members, after the Arab Uprisings. Second, there were significant differences in threat perception, even among the GCC states, according to their political stance towards key events in international relations. We also propose a new empirical method for studying securitization in critical security theory based on quantitative text analysis.

Travel for women rights: social movements and the construction of travel as activism in social media

ABSTRACT Globalized social movements often require travel, but travel as a symbolic form of transnational activism has only been examined in certain instances. This paper employs the notion of political travel and, through a qualitative content analysis of social media posts during the 2018 pro-choice Irish referendum campaign, it shows how travel can be approached as a form of activism and resistance from a critical feminist angle in the context of a global campaign mediated by social media. Inspired by assemblage theory [De Landa, M. (2016). Assemblage theory. Edinburgh University Press.], our findings show how different elements; people, spaces, objects are assembled to construct the framing and narrative of a social movement that fights for women rights. The individual accounts of travel experiences in social media contributed to the overall ‘aestheticization’ of the social movement. Also, the study offered insights into the meanings that, predominantly female, travellers, assign to their political travel, which are in turn shaped by personal and historical concerns.

(In)Security in social media: exploring Israeli and Palestinian narratives

ABSTRACT The persistence of the Israeli-Palestinian conflict profoundly influences the perceptions of both Israelis and Palestinians, shaping successive generations and posing a complex challenge for security scholarship. This article examines these contrasting socially constructed realities through the lens of Securitisation Theory, analysing public social media discourses on Facebook between 1 January 2021 and 29 April 2022 in Israel and the Palestinian Territories. By bridging high-level theorising with empirically driven methodology, the study enriches the understanding of securitisation processes using Balzacq’s framework and engaging with perspectives from Cote, Stritzel, and Vuori, including the active role of target audiences and the complexity of securitisation as a speech act. Through this approach, the research not only advances theoretical insights into Securitisation but also demonstrates that leveraging social media analysis provides both a valuable means of theory testing and a more comprehensive understanding of securitisation dynamics in the Israeli-Palestinian context.

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

Rogue Hairs: A Mixed-Method Characterization of a Previously Unreported Entity

Plain Language SummaryThis research defines a new term called “rogue hairs” which are unusually long hairs on the face outside of the places where long hairs typically grow. These hairs are common but were not previously reported in medical literature. This study describes how often these hairs occur, where they occur, and other details about the people who have them. People with rogue hairs were identified by dermatologists, and social media platforms were searched to find additional people with these hairs. This study also used surveys and social media websites to learn about people with these hairs. The results showed that these hairs are seen in children and adults, men and women, and across races. Eighteen percent of the surveyed patients reported rogue hairs. Social media analysis showed common experiences of sudden appearance, frequent attempts to remove the hairs, and minimal interaction with dermatologists. The cause for these hairs is not known. This study highlights the potential of social media as a tool for answering clinical questions and better understanding the patient experience. The study provides evidence-based information for individuals experiencing rogue hairs and prompts future research into the causes of this common phenomenon.

DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

In contemporary times, identifying depression at an early stage has become a critical issue within the field of psychology. Mental health problems, notably depression, afflict a significant portion of the global population, with an estimated 300 million individuals currently affected. Leveraging the vast amount of user-generated content on social media platforms, researchers are exploring ways to utilize this data to identify potential mental health issues in users. Depression remains a pressing concern in society and a topic of ongoing research globally. Despite numerous studies attempting to understand various mood disorders such as depression, anxiety, and stress using activity logs from devices like smartphones, accurately predicting depressive episodes remains challenging. Social media analysis has emerged as a popular approach to tackle this challenge. In this paper, we propose a system for assessing depression and detecting suicidal ideation by predicting suicidal tendencies based on the severity of depression. Our study aims to develop effective machine learning algorithms for identifying potential signs of depression in individuals through their social media posts. To achieve this, we trained and tested classifiers to discern whether a person exhibits signs of depression using features extracted from their social media activities. Various machine learning algorithms were employed to train and classify users into different levels of depression on a scale ranging from 0 to 100%. Data in the form of posts were collected and categorized into depressed or non-depressed using machine learning algorithms. Our primary contribution lies in exploring a range of features and their effectiveness in detecting the degree of depression. We aim to build a deep learning model capable of categorizing users with depression by learning from individual-level labels to infer post-level labels. By amalgamating the probabilities of post label categories, we can create temporal posting profiles to classify users with depression. Our findings reveal distinct variations in posting behaviors between depressed and non-depressed users, as indicated by the combined probabilities of post label categories. In our study, we utilized natural language processing (NLP) techniques, employing the BERT algorithm, to efficiently identify potential depression indicators in social media content. This approach offers a more accessible and effective means of detecting depression. Keywords— Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post.

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

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

EVALUATING THE EFFECTIVENESS OF SOCIAL MEDIA MARKETING STRATEGIES IN DRIVING SALES: A CASE STUDY OF SNAPDEAL IN THE INDIAN ONLINE MARKETPLACE

This paper investigates the efficacy of social media marketing strategies in bolstering sales performance, with a specific focus on Snapdeal, a prominent player in the Indian online marketplace. In an era where digital connectivity is paramount, social media platforms have become indispensable tools for businesses seeking to enhance their market presence and drive revenue growth. Through a comprehensive analysis of Snapdeal's social media marketing approach, including targeted advertising, customer engagement, influencer partnerships, and content marketing, this study aims to elucidate the impact of these strategies on driving sales in the Indian e-commerce landscape. By examining Snapdeal's utilization of social media platforms to engage with its diverse consumer base, foster brand loyalty, and influence purchase decisions, this research provides valuable insights into the evolving role of social media in shaping consumer behavior and driving business success in the digital age. Keywords:- Social Media, Marketing Strategies, Snapdeal , India , Online Evaluation

Social Media and Surveillance Capitalism: Facebook, Political Polarization, Orwellian Dystopia, and American Democracy

Social Media and Surveillance Capitalism: Facebook, Political Polarization, Orwellian Dystopia, and American Democracy

Bibliometric Analysis of Social Media Marketing in Hospitals: Trends and Influences

Bibliometric Analysis of Social Media Marketing in Hospitals: Trends and Influences

The Nation on the Corners: The Politics of Street-Naming in Lima during the Second Half of the Nineteenth Century

AbstractRecent studies on the figuration of the nation in nineteenth-century Hispanic America have used sophisticated analyses of different media to suggest that Indigenous references were progressively excluded from urban public space as national symbols. In these spaces, the creole authorities placed themselves and their ancestors centre stage. However, these studies have neglected a highly representative medium: street names. This article demonstrates that street nomenclature was key in figuring the nation in a capital city and shows a different trend from that established using other media. Specifically, after the 1861 municipal reform of street names in Lima, Peru, the majority of official names were Indigenous and did not celebrate creole or military elites. This article examines this reform and the conflicts it provoked.

STRATEGI PENGURANGAN STIGMA TB MEMBUKA JALAN UNTUK PEMBICARAAN KESEHATAN MENTAL

This study aims to analyze strategies for reducing Tuberculosis (TB) stigma and its impact on mental health discussions in Medan, considering the close relationship between TB stigma and mental health issues. A qualitative method was used, involving in-depth interviews, focus groups, document and media analysis, and participatory observations to gain a deep understanding of perceptions, experiences, and the social context related to TB stigma. The results indicate that public education and information, media engagement, social and psychological support, and public policy and advocacy are key strategies in reducing TB stigma. This not only helps reduce TB-related stigma but also opens up space for improved discussions and support for mental health, showing the importance of a comprehensive approach in addressing TB.

A Performance Analysis of CassandraDB and ScyllaDB

Abstract: Big Data encompasses vast amounts of data, reaching exabytes or even zettabytes. In the current landscape, large databases play a crucial role, particularly in generating substantial data for daily analyses of social media and multimedia content. The enormity of Big Data poses challenges, given its extensive volume of structured, semi-structured, and unstructured data, making traditional database systems and software techniques insufficient. Big Data is frequently defined by its 9 V’s: velocity, variety, volume, veracity, validity, variability, volatility, visualization, and value. This complexity highlights the need for a simple information management strategy that integrates various new data types alongside traditional data. The significance of Big Data databases is emphasized by the daily generation of millions of terabytes of data from sources like social media posts and multimedia. This study aims to evaluate the performance of two Wide-Column Store Big Data database systems, Apache CassandraDB and ScyllaDB, using the CassandraStress Benchmarking Tool. Key metrics such as total operation time, operation rate, partition rate, row rate, and maximum latency will be assessed as the number of records and operations increase. The development of these databases, motivated by diverse industry requirements, emphasizes their adaptation to specific needs. The research methodology outlines the tool used to compare these WideColumn Store databases on various parameters, contributing valuable insights into their performance in real-world scenarios

Permeability and Porosity Analysis of a Unique Porous Media Obtained by Bead Width Variation in a MEX Filling Strategy

Permeability and Porosity Analysis of a Unique Porous Media Obtained by Bead Width Variation in a MEX Filling Strategy

Exploring the customer experience of Vietnam airlines via sentiment analysis

The sentiment analysis of social media has gained much attention due to the succinct nature of comments, enabling access to public opinions. Using this technique, the present study aimed to explore customer sentiments regarding their experiences with Vietnam Airlines, the national carrier. According to the Skytrax evaluation, Vietnam Airlines achieved a 4-star rating out of 5, indicating the need for service improvements to attain perfection. The significance of this research lies in providing a comprehensive perspective on customer experience evaluations. To achieve this, we utilized the Valence Aware Dictionary for Reasoner (VADER) to classify sentiments expressed in social networking forum data. The dataset comprised 670 reviews from the Skytrax forum, which underwent a data pre-processing phase. The results exhibited favorable accuracy in detecting ternary and multiple sentiment classes. The comparative analysis between visual customer ratings and their corresponding comments yielded similar outcomes. Additionally, the results highlighted the need for a re-evaluation of the in-flight entertainment system.

Study of the Problems of Determining Public Opinion of the Israeli-Palestinian War in Social Networks

This paper explores the use of neural networks for determining public opinion on the Israeli-Palestinian conflict. In today's world, where numerous user opinions on various topics are posted daily on social media platforms, it is important to be able to analyze this public sentiment. The method of sentiment analysis and weighting of votes in comments is proposed, allowing for the identification of overall audience moods and assessment of their reactions to different events or content. The research results indicate the potential usefulness of these methods for marketing strategies, reputation management, and decision-making in various fields. However, there are challenges associated with data verification and consideration of manipulative interventions in public opinion. This work aims to review the current state of research in this area and identify prospects for further development. In today's digital world, analyzing public opinion on social media is becoming increasingly important. Understanding how users react to various topics and events is crucial for various fields, from marketing to politics. This paper discusses methods of sentiment analysis and vote weighting in comments using the example of analyzing public opinion on the Israeli-Palestinian conflict on the Reddit social network. The use of natural language processing tools, machine learning, and data visualization allows for valuable insights into users' emotional attitudes toward discussed topics and trends in public opinion. However, to achieve the best results, it is necessary to consider not only textual content but also social interaction and user status. The development of effective social media analysis tools opens the way to a deeper understanding of public opinion and making informed decisions in various areas of social life.

Sensitivity analysis of electrodes spacing media for evaluating alkaline electrolyzer performance through CFD modeling

Alkaline Water Electrolyzer (AWE) technology shows promising potential for shifting towards green hydrogen production. With the growing global interest in green hydrogen, understanding the dynamics of AWE systems becomes crucial to improving their performance. Therefore, this paper aims to provide a novel sensitivity analysis aspect to investigate the correlation within parameter variables associated with AWE's electrode separation media. These parameters include electrode-diaphragm gap, temperature, diaphragm thickness, and porosity, aiming to evaluate their impact on AWE current density. The methodology involves the development of a Computational Fluid Dynamics (CFD) model, conducting a parametric study, performing Analysis of Variance (ANOVA), and sensitivity testing within specified parameter ranges. The findings show that diaphragm porosity has a considerable effect, especially between 15 % and 60 % porosity, where the trend levels off at higher values. The electrode-diaphragm gap trend reveals a sensitive, nonlinear increase in cell current density as the gap decreases from its average, with a 75 % decrease yielding over 100 % higher current density, while adjustments beyond 10 mm have minimal impact on current density despite significant variations in other parameters. A 50 % temperature rise increases current density by 40 %, while a 50 % diaphragm width reduction modestly boosts current density by around 10 %. Understanding these sensitivities is vital for optimizing AWE's performance.

Cable news advertising: Applying formal analysis to uncover current trends in self-promotional marketing

The emergence of 24/7 cable news channels within the US revolutionized news consumption, offering viewers accessibility beyond fixed broadcast schedules. While the phenomenon began with CNN, competitors quickly emerged, targeting different political ideologies and mirroring the heightened polarization that has altered American society. Cable news channels must market themselves effectively to their audiences through distinctive product differentiation strategies. Understanding these approaches provides insights into information dissemination, political communication, and audience engagement dynamics. This paper aims to conduct a formal media analysis of current self-promotional advertisements across the big three cable news networks within the US to derive insights into contemporary differentiation strategies crucial for monitoring the industry’s technological transition. As the rest of the world has begun to wrestle with increased polarization, this analysis will be relevant to anticipating how non-US news sources use ideology and values to position themselves within highly competitive markets.