Media Networks Research Articles

Social Media Network Analysis Corruption Harvey Moies on Twitter

Social Media Network Analysis is a way of mapping and describing network structures within social media conversations. However, the focus of this analysis is to find out the variety of netizen interactions that have emerged related to the Harvey Moies Corruption Case of Sandra Goddess's Husband on Twitter. This research uses qualitative methods. The topics considered in this study were social interactions on Twitter, from tweets, retweets, and comments. Each account has different characteristics and perspectives. The variety of conversations that have emerged tends to build public opinion and make the public suspect the legal handling of the case. The account's strength, especially in forming intense interaction, comes from the content posted from the account

Understanding the current backlash against LGBTIQ+ rights through the lens of heteroactivism: A case study of the International Organization for the Family’s transnational norm diffusion on Twitter

Abstract A new generation of transnational anti-gender actors are framing themselves as human rights champions and protectors of the rights of the “natural family”. To better understand these actors’ norm diffusion, including their re-styling of anti-gender narratives in contemporary iterations of heteroactivism and its potential threat to LGBTIQ+ rights, in this article we analyse the social media tactics of a key transnational anti-gender actor: the International Organization for the Family (IOF). The analysis is focused on the organisation’s Twitter (now X) account and we draw on theories of network media logic, connective action, and connective emotions. Two periods of activism – the first a low-intensity period in 2021 and the second a high-intensity period in 2022 – were purposefully selected for the analysis. In this article, we identify differences between the two periods, noting that the period covering the flagship event, the World Congress of Families (WCF) in 2022, was markedly less LGBTIQ-hostile and adversarial than the first period. IOF thus appears to be inspired by heteroactivist frames during the WCF and abides by logic that should trigger user interaction and content spreading. The lack of interactions and engagement with IOF Twitter content in either period indicates the need for more research on which logics apply to anti-gender audiences. We discuss the findings and what they may imply in a context like Sweden, a country whose self-image as a global champion for women’s and LGBTIQ+ rights may make it ill-equipped to counter the onslaught of transnational actors’ norm entrepreneurial activities.

Non-fragile cubature Kalman filtering under innovation-based weighted try-once-discard protocol with channel noise

In this paper, the problem of non-fragile cubature Kalman filtering is addressed for nonlinear systems with stochastic uncertainties under innovation-based weighted try-once-discard protocol (WTODP) affected by channel noise. To reduce transmission burden and improve communication efficiency, the innovation-based WTODP, which is constructed by a set of quadratic functions regarding innovation, is employed in the network channel between the sensor and the filter to regulate the transmission of data signals. Meanwhile, Gaussian-distributed channel noise is taken into account during the signal propagation process to provide a better representation of the network media. An upper bound of the filtering error covariance (FEC) is derived by solving certain matrix difference equations, and the minimization of the obtained upper bound is achieved through the design of an appropriate filter gain. Furthermore, the uniform boundedness of the FEC's upper bound is investigated using the matrix theory to evaluate the performance of the filtering scheme. The effectiveness and superiority of the proposed non-fragile cubature Kalman filtering algorithm are demonstrated through two simulation experiments with comparisons.

Flip the narrative

As the United States approaches the 2024 presidential election, its citizens are experiencing extreme political and religious schism. Separation of church and state is no longer the mantra of the US population. Instead, declarations of which political candidate is “anointed” by God fill the airwaves as well as social media networks. Individuals on both sides of the schism are convinced they know and hear the voice of God and are convinced their favored candidate is the one ordained by God to be the next president of the United States. The confusion created by this rhetoric is intensified by declarations from both sides that democracy is at risk. This article strives to answer the question, “What is really at stake?”

Disney Business Logic Analysis

This paper analyses the development of the Disney Company since its founding in 1923, discusses the evolution of its main business, the risks, and opportunities it faces, and looks forward to the future development direction of Disney. As a giant in the global entertainment industry, Disney has successfully grown from a single animation studio to a global enterprise with diversified businesses including media networks, theme parks and resorts, consumer products business, and film and television entertainment services. The article provides a detailed analysis of how Disney has maintained its business growth and market competitiveness by adapting to digital transformation and globalization challenges while continuously expanding its IP library and strengthening its industry leadership through M&A strategies. It also explores Disney's risk management strategies in the face of global economic volatility, digital transformation challenges and intellectual property protection, as well as new opportunities arising from global expansion, technological innovation, and cross-border cooperation. Through a comprehensive analysis of Disney, this paper aims to provide a case study on how innovation and strategy can achieve sustainable business development in the era of globalization and digitalization.

Transitions in social media use and social media use for transition

High school and college students are known to be heavy users of social media. Much of their use is focused on peer social interactions and entertainment, but social media is also a potential platform for informal networked learning. In this study, we examine how college students used social media to learn about and prepare for college, how they use it in the university context, and how their social media use has changed as they mature and transition from one educational setting to another. Undergraduate students at a large public research university completed an online survey about their social media uses and perceptions. In terms of using social media to support the high school to college transition, preliminary findings show that for many students social media provided a valuable source of information about colleges, and in particular helped them plan and begin to build their social and activity networks in their new setting. Once at college, social media continued to help provide connections, serving as a source of information about campus events and supporting social connections. Social media was less frequently used as a means of networking with instructors. Many students noted that their use of social media had changed in meaningful ways as they transitioned to college. These students indicated that their networks were now different, that their attitudes and uses had matured, and that they were better able to use their social media networks as a platform for awareness and change. Whether they perceived meaningful changes in their use of social media across the transition, most participants alluded to a desire to minimize drama, social comparison, and other ill effects of social media. As data analysis continues, we will be exploring differences across platforms and relationships in college-related social media use based on intensity of use and sense of belonging. The study has implications for how institutions offer and support the development of social media networks among prospective and current students and provides insight into mindsets that can support or inhibit greater use of social media as a platform for informal networked learning as youth transition from high school to college and eventually on to careers.

The Influence of Social Media on Improving the Pragmatic Skills of EFL Saudi University Students

Social media platforms can be a gateway through which English as a foreign language (EFL) students gain access to the world of the target language, communicate with native speakers, and learn more about their culture. Against this background, the present study aims to investigate whether EFL students’ use of social media has impacted the development of their pragmatic competence and conversational skills. Sixty Saudi EFL students, aged 17 or older, studying in the English Language and Translation Department at the Saudi Electronic University participated in the study. Data were collected through an online questionnaire that assessed the participants’ perceptions of using social media networks to practice their English conversation skills. The results showed that social media use had a positive impact on the students’ pragmatic and conversational skills. Specifically, a significant proportion of the participants reported an awareness of conversational and pragmatic improvements resulting from social media use that may not be available in traditional classroom settings. These findings suggest that technology-assisted language learning, particularly learning using social media, should be incorporated into language instruction to improve EFL students’ pragmatic and conversational knowledge. In this regard, social media networks are especially valuable in providing authentic and meaningful language and interaction opportunities. Future research could examine other forms of technology-assisted language learning beyond social media and employ a mixed-methods approach to gain a more comprehensive understanding of the impact of mobile-assisted language learning (MALL) on EFL learners’ pragmatic and conversational proficiency.

Psychometric properties of the ultra-brief self-report Patient Health Questionnaire-4 (PHQ-4) to assess anxiety and depression in Arabic-speaking adults

BackgroundAnxiety and depression are psychiatric disorders that often coexist and share some features. Developing a simple and cost-effective tool to assess anxiety and depression in the Arabic-speaking population, predominantly residing in low- and middle-income nations where research can be arduous, would be immensely beneficial. The study aimed to translate the four-item composite Patient Health Questionnaire – 4 (PHQ-4) into Arabic and evaluate its psychometric properties, including internal reliability, sex invariance, composite reliability, and correlation with measures of psychological distress.Methods587 Arabic-speaking adults were recruited between February and March 2023. An anonymous self-administered Google Forms link was distributed via social media networks. We utilized the FACTOR software to explore the factor structure of the Arabic PHQ-4.ResultsConfirmatory factor analysis (CFA) indicated that fit of the two-factor model of the PHQ-4 scores was modest (χ2/df = .13/1 = .13, RMSEA = .001, SRMR = .002, CFI = 1.005, TLI = 1.000). Internal reliability was excellent (McDonald’s omega = .86; Cronbach’s alpha = .86). Indices suggested that configural, metric, and scalar invariance were supported across sex. No significant difference was found between males and females in terms of the PHQ-4 total scores, PHQ-4 anxiety scores, and PHQ-4 depression scores. The total score of the PHQ-4 and its depression and anxiety scores were significantly and moderately-to-strongly associated with lower wellbeing and higher Depression Anxiety and Stress Scale (DASS) total and subscales scores.ConclusionThe PHQ-4 proves to be a reliable, valid, and cost-effective tool for assessing symptoms related to depression and anxiety. To evaluate the practical effectiveness of the Arabic PHQ-4 and to further enhance the data on its construct validity, future studies should assess the measure in diverse contexts and among specific populations.

#CannesYouNot? Oppositional and asymmetrical versions of believability in the Depp/Heard case

Johnny Depp’s contentious return to the red carpet at the 2023 Cannes Film Festival sparked fervent debate online about domestic abuse, believability, #MeToo and the rehabilitation of men accused of abuse. In this article, we analyse two oppositional hashtags that became central to this discussion on Twitter: #CannesYouNot, created by supporters of Depp’s ex-wife Amber Heard; and #YesYouCannes, the response offered by Depp’s supporters. Drawing on a dataset of 18,000 tweets, we combine a network analysis with a qualitative analysis of the top tweets using each hashtag in order to understand how the hashtags circulated, their affective orientations and the evidence they use to support their positions. Our findings show that networked media allow the (re)construction of existing hierarchies of power; thus, we argue that although these appear to be straightforwardly oppositional hashtags, they operate asymmetrically in ways that have implications for our understandings of issue publics as well as ramifications for feminist digital activism.

Network link prediction via deep learning method: A comparative analysis with traditional methods

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

How Does Social Media Contribute to Improve Business Performance for Streaming Service?

In a rapidly evolving technological landscape, organizations are constantly seeking innovative strategies to enhance performance and connect with their target audience. One such platform gaining traction is TikTok, a social media network particularly popular among young people. Recognizing its potential, businesses are exploring how TikTok can foster brand awareness and cultivate deeper consumer engagement. This study investigates whether factors like internet consumption patterns, social influence on TikTok, perceived value of TikTok content for Netflix, and trust in Netflix as a brand contribute to its performance. Interestingly, through a survey administered to 151 participants, the research suggests that internet consumption is the primary driver, with social influence, perceived usefulness, and trust on TikTok not having a statistically significant impact on Netflix's performance. This study suggests that for Netflix, user engagement with its content is the crucial factor on TikTok, not relying on influencer marketing or specific content value perceptions. Future research should explore content formats, user interaction strategies, and audience segmentation techniques to maximize user engagement with Netflix content on the platform

„Wer um alles in der Welt ist Pom-Pom?”

AbstractThe article deals with the stories of Pom-Pom, which appeared in Hungary from the beginning of the 1980s, initially in the media network of picture books and animated films. Since the 2010s, the characters have increasingly appeared in the public space of Budapest: first in the context of thematic playgrounds, then in the form of mini-sculptures, and finally street art murals. In terms of time, these events coincide on the one hand with the so-called critical threshold, that transition between communicative and cultural memory, and on the other hand they set in at a point in time initiated by the operational end as well as the incipient building decay of the renowned Pannónia film studios. The examples chosen solely according to the criterion of visibility in public space prove to be representations planned, supported and tolerated by the public authorities.

A novel optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media

ABSTRACT Social media networks become an active communication medium for connecting people and delivering new messages. Social media can perform as the primary channel, where the globalized events or instances can be explored. Earlier models are facing the pitfall of noticing the temporal and spatial resolution for enhancing the efficacy. Therefore, in this proposed model, a new event detection approach from social media data is presented. Firstly, the essential data is collected and undergone for pre-processing stage. Further, the Bidirectional Encoder Representations from Transformers (BERT) and Term Frequency Inverse Document Frequency (TF-IDF) are employed for extracting features. Subsequently, the two resultant features are given to the multi-scale and dilated layer present in the detection network of GRU and Res-Bi-LSTM, named as Multi-scale and Dilated Adaptive Hybrid Deep Learning (MDA-HDL) for event detection. Moreover, the MDA-HDL network’s parameters are tuned by Improved Gannet Optimization Algorithm (IGOA) to enhance the performance. Finally, the execution of the system is done over the Python platform, where the system is validated and compared with baseline methodologies. The accuracy findings of model acquire as 94.96 for dataset 1 and 96.42 for dataset 2. Hence, the recommended model outperforms with the superior results while detecting the social events.

Celebrification through Media Spectacles: Emerging Forms of Performativity in Indian Politics

We live at a time when new forms of mediatisation are conditioning the political landscape of India, especially in the context of the 2024 Lok Sabha elections in the country. As heat wave conditions prevailed in many parts of the country, the quantity of time spent by politicians for on-ground campaigning was lesser in comparison with earlier elections. The hostile climatic conditions together with the intensity of digital empowerment, access to smartphones and availability of cheap data facilitated the formation of a thriving ecosystem of digital campaigning. The country witnessed political parties using digital spaces to an unprecedented level to inform, influence and even mislead the prospective voters. The focus of the paper will be on the strategic use of social media networks for the current elections in India to transform politicians into celebrities and the acts of performativity choreographed to entice the citizens with media spectacles.

Polarized social media networks: a novel approach to quantify the polarization level of individual users

ABSTRACT This paper presents a novel methodology to quantify users’ polarization within social media networks, focusing on network structure instead of content. We model polarized networks as directed graphs, collecting users’ follower/following connections data, and propose a simple algorithm to compute a Connectivity Score for each node in sub-graphs associated with identified contrary groups of the polarized network. Our method has several advantages: it relies solely on users’ follower/following connections data, which is objective, often public and easy to obtain. Also, this input data is usually memory-efficient, unlike the input data of content-based methods which may require a whole corpus. The algorithm's performance is assessed through application to Twitter's 116th Congressmembers network, comparing against a content analysis method: Sentiment score (using VADER), and two political behavior-based methods: Ideology score (based on co-sponsorship frequency) and Roll Call score (based on bill-voting similarity). The results demonstrate that: (1) users’ choice of connections on the social media can represent polarization behavior; (2) a meaningful correlation between Connectivity Scores and Ideology or Roll Call Scores shows that the political behavior of users is reflected in their social media connections; (3) Democrats’ Twitter following behavior and their bill-voting and bill co-sponsorship behavior (represented by Roll Call and Ideology) are all significantly more correlated than that of Republicans. We believe applying our algorithm in conjunction with other methods is a valuable contribution resulting in more comprehensive analysis of the social media polarization space.

Lexicon-Enhanced Long Short-Term Memory (LSTM) for Detecting of Fake News using Deep Learning

Due to its increasing popularity, low cost, and easy-to-access nature, online social media (OSM) networks have evolved as a powerful platform for people to access, consume, and share news.However, this has led to the large-scale distribution of fake news, i.e., deliberate, false, or misleading information. Spreading fake news is roughly as dangerous as spreading the virus. Fake news detection attracts many researchers' attention due to the negative impacts on the society Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news content or the social context of the news dissemination process on social media platforms. In this work, we propose a lexicon-enhanced LSTM an automated model that is able to take into account both the news content and the social context for the identification of fake news. The model first uses sentiment lexicon as an extra information pre-training a word sentiment classifier and then get the sentiment embeddings of words including the words not in the lexicon. Combining the sentiment embedding and its word embedding can make word representation more accurate and to detect fake news and better predict fake user accounts and posts.We used five performance metrics to evaluate the proposed framework: accuracy, the area under the curve, precision, recall, and f1-score.The model achieves an accuracy of 99.55% compared to 93.62% against discourse structure analysis. Also, it shows an average improvement of 18.76% against other approaches, which indicates its viability against fake-classifier-based models

Organizational Technology Policies and the Growth of Micro and Small Enterprises in Nairobi City County, Kenya

This study sought to establish the influence of establish the influence of organizational technology policies on the Growth of Micro and Small Enterprises in Nairobi, Kenya and; To determine the moderating effect of Entrepreneurial Orientation on organizational technology policies and the Growth of Micro and Small Enterprises in Nairobi, Kenya. The study was grounded on the Resource Based theory. The study adopted a descriptive research design since the design enabled the researcher to generalize the findings to a larger population of MSEs. In addition, this design enabled the researcher describe the characteristics of the population being studied as they exist at present hence minimizing biasness and maximizing the reliability of the evidence collected. This study was guided by positivism philosophy. The target population for this study comprised of all the 1835 Owners or managers of the MSEs registered with Nairobi City County and have operated for more than five years. Stratified and Simple Random sampling techniques was used to select a total of 184 respondents for the study. The study found out that organizational technology policies and organizational technology policies were the key variable that significantly influencing the Growth of Micro and Small Enterprises in Nairobi County, Kenya. Specifically, MSEs Staff were largely found to be un-trained especially on Emerging Technologies and lacked motivation to adopt and use technologies. In addition, study found out that there was low adoption on use of social media platform for business operation. The study recommends that the Management of MSEs needs to make deliberate efforts and invest on training especially on Emerging Technologies such as e-commerce, communication, e-production, e-procurement among others. This will ensure that the MSEs staffs are well equipped with the necessary skills and knowledge needed to effectively utilize the emerging technologies in the market. Further, the management of the MSEs needs to be motivated to adopt and use technologies and innovation. Motivation many be inform of incentives and rewards that motivates users to adopt and use technology in their operations. The is also need to invest in a strong and robust social media platform that are easy to use for most users. Social media networks can be harnessed to boast in product and services marketing and sourcing of market information among other.

Social Media as a Tool for Informal Spanish Learning: A Phenomenological Study of Chinese Students' Behaviour in Spain.

This study explores the perceptions of Chinese learners in Spain regarding the use of social networks for informal Spanish language learning. The objective is to identify the challenges and benefits of using social networks to address the real needs of students in learning Spanish. A qualitative phenomenological approach was adopted, focusing on participants' perceptions before and after using social networks. Eight Chinese students were selected for the study. The study was conducted in Valencia and Barcelona, Spain, from 1 September 2023 to 20 March 2024, and three commonly used social media networks were compared. The results indicate positive perceptions towards social media as a tool for learning Spanish, highlighting its usefulness in improving language skills and enhancing cultural awareness. Additionally, Xiaohongshu and Bilibili emerged as the most popular platforms for Spanish language learning among Chinese students. This study concludes that social media effectively meets the authentic needs of Chinese students learning Spanish in Spain, enhancing both language skills and cultural adaptation. This multifaceted approach reflects the complexity of learning Spanish in the digital age, combining personal passion, professional aspirations, and cultural adaptation needs.

The online destination image as portrayed by the user-generated content on social media and its impact on tourists' engagement

Tourism Destination Image (TDI) is complex, dynamic and multidimensional by nature and shaped by a multitude of events, interactions and sensory impressions, especially once the tourist is at the destination, generating tourists' engagement on social media networks. This study aims to measure the online destination image (ODI) portrayed in user-generated content (UGC) on social media by employing the sentiment analysis method. Data were extracted from Facebook and Instagram on the Visit Algarve social media profile. The Algarve region is situated in the south of Portugal and is one of Europe's most popular tourist destinations. The main results shed light on the relevance of intangible heritage experiences as triggers of positive feelings about the destination. Content shared about tourists' activities in the region generates more engagement in the tourism destination's social media. Tangible heritage received the lowest rated sentiment, and destination agents should strategically look into it as there are opportunities for destination management. Tourism infrastructure has the lowest engagement value; therefore, in terms of promotion, it does not generate interest.

The Effect of Knowledge Orchestration On Dynamic Capabilities: An Analytical Research in the Iraqi Media Network

This research aims to know the effect of the knowledge orchestra with its dimensions (knowledge mobilization, knowledge acquisition, knowledge coordination, knowledge sharing, and knowledge integration) on the dynamic capabilities with its dimensions (environmental sensing capabilities, opportunity investment capabilities, resource rearrangement capabilities, marketing capabilities, and technological capabilities). The main problem of this research was represented by the current question (Was the Iraqi Media Network able to invest the knowledge orchestra in achieving distinguished performance across having dynamic capabilities?). The Iraqi Media Network took a field of application for these variables, as the research sample included the senior leaders of the Iraqi Media Network, their numbers were 134 being between a director of a directorate, an assistant director, and a head of a department. The research used the quantitative method and the questionnaire was the main tool for collecting data and information, and the research used the SPSS program (v. 25) for data analysis. The results of the study concluded that there is a significant and positive influence and correlation between each dimension of the orchestra's knowledge operations and its dynamic capabilities. Also, results of the research reported that the Media Network showed its modules to mobilize knowledge through adopting communication patterns that are harmonious with its orientation towards having intellectual capital that distinguishes it from others, particularly it works to expand the knowledge base, such as diversity in knowledge fields within its current and future staff.