Spread Of News Research Articles

PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection

ABSTRACT The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. This research explores quantum machine learning for fake news classification by proposing Pegasos Quantum Support Vector Machines, a novel algorithm combining Pegasos Support Vector Machines with quantum kernels, and advanced data encoding. Through experimentation on the IBM Qasm Simulator, Pegasos Quantum Support Vector Machines scored 90.67% in accuracy. This study is primarily focused on local simulation, where the proposed algorithm scored as high as 95.63%, with 95.44% precision, 99.52% recall, and 96.76% f1-score. The achieved results outperform other machine learning methods on the BUZZFEED dataset, including Quantum Neural Networks and Quantum K-Nearest Neighbors. Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). Noisy Intermediate-Scale Quantum era QPU are prone to noise effects that affect the computations negatively, and by extension, the results of quantum machine learning algorithms. Further implementation on real QPU and use of error mitigation techniques, are needed for optimal results on quantum hardware.

Психологические факторы кибербезопасности и доверие к ложным новостям в интернет-коммуникации: обзор современных зарубежных исследований

<p style="text-align: justify;">The paper presents a systematic review of contemporary foreign research on psychological factors of cybersecurity and trust in fake news. It contains an analysis of theoretical developments and empirical studies of the psychological aspects of cybersecurity within the framework of the concepts of personality psychology, behavioral psychology and social psychology. Despite the fact that general psychological patterns and theoretical models of these branches of psychology are used in the study of cybersecurity, the available results of research in this area still do not form a holistic picture of the psychological factors of cybersecurity behavior. At the same time, the factors of personal characteristics and behavior of cybercriminals seem to be better studied and more understandable than the factors of compliance and violation of the rules of cybersecurity behavior by “ordinary” users. The article then presents a review of empirical studies on the reasons why Internet users trust fake news and how to overcome it. General awareness of the problem of the spread of fake news is shown to offer little help in reducing trust in fake news, and the use of ineffective strategies for recognizing fake news often has the opposite effect. A more sophisticated strategy based on knowledge of the specific techniques by which fake news is created, on the contrary, can more effectively reduce the risk of trust in fake news. The author comes to the conclusion that measures promoting cybersecurity behavior of Internet users are advisable if aimed not at stimulating vigilance, but at increasing confidence in the picture of the world, in which the phenomena of cybersecurity threats and fake news are built in as a familiar and understandable component.</p>

Enhancing Media Convergence with Artificial Intelligence to Stabilize Financial Markets

This study explores the application of artificial intelligence (AI) technology in media convergence, focusing on how AI is driving deep integration of media and financial markets through big data analytics, AIGC (AI-generated content), and intelligent communication technologies. Ai-driven sentiment analysis and fake news detection tools effectively solve the problem of information asymmetry and the spread of false news in the financial market and promote market stability and transparency. Through personalized recommendations and intelligent communication, AI provides users with a more accurate content experience and improves user engagement and satisfaction. In addition, the rapid development of AIGC and big data ecology has promoted the intellectualization of information dissemination and public opinion analysis, providing more forward-looking support for financial market decision-making.

Cross-Cultural Perspectives on Fake News: A Comparative Study of Instagram Users in Greece and Portugal

As our society increasingly relies on digital platforms for information, the spread of fake news has become a pressing concern. This study investigates the ability of Greek and Portuguese Instagram users to identify fake news, highlighting the influence of cultural differences. The responses of 220 Instagram users were collected through questionnaires in Greece and Portugal. The data analysis investigates characteristics of Instagram posts, social endorsement, and platform usage duration. The results reveal distinct user behaviors: Greeks exhibit a unique inclination towards social connections, displaying an increased trust in friends’ content and investing more time on Instagram, reflecting the importance of personal connections in their media consumption. They also give less importance to a certain post’s characteristics, such as content opposing personal beliefs, emotional language, and poor grammar, spelling, or formatting when identifying fake news, compared to the Portuguese, suggesting a weaker emphasis on content quality in their evaluations. These findings show that cultural differences affect how people behave on Instagram. Hence, content creators, platforms, and policymakers need specific plans to make online spaces more informative. Strategies should focus on enhancing awareness of key indicators of fake news, such as linguistic quality and post structure, while addressing the role of personal and social networks in the spread of misinformation.

Innovative Approaches to Fake News Detection: A Data Mining Perspective

Fake news becomes a major concern in the era of social media, as it can spread rapidly and has significant impacts on individuals and society.Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either generated by humans or machines. In the era of social networks such as Facebook, X (twitter) and WhatsApp, the quick rotation of fake news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issues, twodata mining classification techniques were used as Extreme Gradient Boosting and Decision Tree with some python features. This study is designed to use Decision Tree and Extreme Gradient Boosting methods to develop an effective approach for detecting and classifying news as real or fake to obtain a reliable model performance. These models are trained on a labeled dataset consisting of bothreal and fake news. The performance of the models was evaluated using standard evaluation metrics such as accuracy, precision, recall, and F1-score. The proposed approach achieved 100% accuracy in distinguishing between real and fake news. It revealed andhighlighted the potential of utilizing data mining techniques to combat the spread of fake news and provide valuable insights for researchers and practitioners in the field of information confirmation/verification and media literacy. We hope to use a different dataset to test the proposed model.

Point process analysis of geographical diffusion of news in Argentina.

The diffusion of information plays a crucial role in a society, affecting its economy and the well-being of the population. Characterizing the diffusion process is challenging because it is highly non-stationary and varies with the media type. To understand the spreading of newspaper news in Argentina, we collected data from more than 27 000 articles published in six main provinces during 4 months. We classified the articles into 20 thematic axes and obtained a set of time series that capture daily newspaper attention on different topics in different provinces. To analyze the data, we use a point process approach. For each topic, n, and for all pairs of provinces, i and j, we use two measures to quantify the synchronicity of the events, Qs(i,j), which quantifies the number of events that occur almost simultaneously in i and j, and Qa(i,j), which quantifies the direction of news spreading. Our analysis unveils how fast the information diffusion process is, showing pairs of provinces with very similar and almost simultaneous temporal variations of media attention. On the other hand, we also calculate other measures computed from the raw time series, such as Granger Causality and Transfer Entropy, which do not perform well in this context because they often return opposite directions of information transfer. We interpret this as due to the characteristics of the data, which is highly non-stationary, and of the information diffusion process, which is very fast and probably acts at a sub-resolution time scale.

Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets—FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models—BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set.

The future of the audience in post-television. The case of the italian talk-show Propaganda Live!

We live in a very difficult and confusing time of using various media. To undergo this constant process of change is above all the television that seeks a way to reposition itself and adapt to the new type of audience. Our goal is to bring order and shape the future of traditional media. According to Jenkins[1] we will try to better define what is meant by cross-media, trans-media and omnimediality. Many authors give different definitions, but we are sure that we can arrive at a new means of spreading news that is more valid than the previous ones from a qualitative point of view. The objectives of the research is to contribute to forming a new type of text that is both multimedia and interactive. A story inserted in a context that demonstrates how unidirectional communication is no longer possible and where the world of media is increasingly called upon to greater involvement of the end user. The aim is to compete with the latter, which is already partly happening, in an orderly process of cross-media use. The idea that the interaction between television and social media can improve the audience for both is based on a profound change in the way people consume content. Today, watching TV is no longer a passive and solitary experience as it used to be. [1] H. Jenkins, Cultura Convergente, Apogeo, Milano 2007

How Is Fake News Spread? An Analysis of the Dissemination Process: Actors, Channels, and Motives

Even though fake news is widely recognized as one of the most serious threats in the post-truth era, there are still some gaps regarding fake news dissemination. To address these gaps, it is necessary to conduct theoretical research revising and analyzing the latest scientific developments on this topic paying attention to social, psychological, and technological contexts in which fake news is constructed and spread. Understanding this process might help to improve the media literacy of users by raising awareness and forming critical attitudes to by-products of the information environment. This article presents a literature review on fake news dissemination based on an analysis of 106 papers extracted from the Web of Science, Scopus, and Google Scholar databases. The author focuses on identifying the main actors who spread fake news. Additionally, a typology is proposed to organize knowledge about the motives behind creating fake news, based on social, psychological, and cognitive factors.

Twitter and Fake News: Implications for National Security in Nigeria

The emergence of new social media platforms such as Twitter has revolutionized the means of communication and dissemination, providing unprecedented networking avenues for immediate news spread. But this ease has also increased the spread of fake news which is a concern faced by many nations including Nigeria. Fake news simply deceives, suppresses facts relevant to political life or spreads information intended only to harm, having a direct impact on national security. For Nigeria, a country rich in ethnic, religious and political prejudice, the effects of fake news can go as far as inciting violence, destroying the social fabric or even causing instability. This study investigated the prevalence, characteristics and consequences of fake news on Twitter during critical events in Nigeria, using a qualitative methodology to understand how false information is spread and what potential threat it poses to national security. The paper uses secondary data from the extant literature and Twitter-Archive for investigating how fake news spreads, consequently shaping public opinion, damaging social solidarity and threatening national security. It lists the top drivers of misinformation in Nigeria – including poor media literacy, political divisiveness and over-dependence on social media as sources of news. It also puts forward solutions to address the risks posed by this kind of content on Twitter, including government legislation, fact-checking mechanisms, media literacy initiatives and consumer take-backs. The study contributes to a deeper understanding of the complex relationship between Twitter, fake news, and national security in Nigeria, highlighting the urgent need for proactive measures to protect the country’s stability and well-being.

The Fake News Sharing in Social Media During Covid-19 from Users and Gratification 2.0 Theory Perspective: Malaysian Users as example

Amidst the COVID-19 pandemic, the proliferation of fake news has raised significant concerns globally. This research aims to examine the influence of disseminating false news on social media channels throughout the pandemic period in Malaysia, utilising the perspective of the uses and gratification 2.0 theory. Employing a close-ended survey questionnaire for data collection and employing statistical methods, 308 respondents from Malaysia were included in the study. The findings indicated that interactivity and modality significantly influenced fake news sharing among the respondents, whereas social media usage negatively moderated the association between navigation and fake news sharing. The findings indicate that interactivity and modality increased sharing, but social media usage negatively moderates the link between navigation and sharing. The study acknowledges limitations such as the small sample size of 308 respondents from Malaysia due to time constraints, and its focus on the Uses and Gratification 2.0 theory. Nevertheless, these results highlight the significance of comprehending the mechanisms involved in the spread of fake news on social media platforms during pandemic events.

Detection of Arabic and Algerian Fake News

Abstract In an era characterised by the rapid dissemination of information through digital platforms, the proliferation of fake news has emerged as a pressing global concern. Misinformation, deliberately fabricated or misleading content presented as factual news, poses significant threats to public discourse, trust, and decision-making processes. The research highlights the significance of fake news detection in the Arabic language, with a specific focus on the Algerian dialect. The Arabic language exhibits great diversity and complexity, making the detection of false information, all the more crucial. The rapid spread of fake news through social media platforms has a significant impact on individuals and society as a whole. To address this challenge, this paper presents TruthGuardian, an innovative solution that combines machine learning and deep learning techniques with voting system for the last decision. This solution enables fast and accurate identification of fake news in the Arabic language, with emphasis on the Algerian dialect. It provides reliable and effective results in countering misinformation.

A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators

This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online personalities. We track the interaction among fake news spreaders using the User-User Interaction Graph. The study reveals a rapidly growing population of disseminators, including professional spreaders, with over 3% dominating the others. The collaboration among fake news spreaders is high, highlighting the need for further research using publicly available online data to understand the community spreading malicious misinformation about COVID-19.

O LUTO NOS TEMPOS DA COVID-19: UM ESTUDO QUALITATIVO

Objective: To analyze coping with the pandemic and grief in individuals who lost members of their socio-affective network as a result of Covid-19. Method: Cross-sectional study, with a qualitative approach, conducted with 14 individuals bereaved during the pandemic. Individual, semi-structured and recorded interviews were conducted, preceded by the application of a socioeconomic and demographic questionnaire. Results: The speeches were subjected to Bardin's content analysis, raising two thematic dimensions: fear disorders, anxiety and psychological suffering during the Covid-19 pandemic and prolonged mourning associated with the restrictions imposed by the pandemic. Final considerations: Facing the pandemic presents significant challenges in the face of the intense spread of Fake News camouflaged by the infodemic process. Health professionals and their respective population care institutions play a vital role in transmitting safe and scientific health information to communities. In addition, it is noteworthy that experiencing grief during the pandemic context imposes important restrictions on the stages ranging from hospitalization to the post-funeral process. Recognizing the distinctions of social vulnerability is fundamental to understanding the mechanisms and repercussions of this process on biopsychosocial and public health.

Operation Acolhida: Peace, health, and communication in the Venezuelan immigrants’ and refugees’ contexts in Brazil: Grassroots report

Introduction: Operation Acolhida is a pioneering humanitarian initiative in Brazil, bringing together efforts from multiple institutions to assist in the settlement process of Venezuelan refugees. The Operation is a compassionate response organized by the Brazilian government to the humanitarian crisis in Venezuela. The objective of this report is to provide a detailed and well-grounded account of the experiences of Venezuelan immigrants and refugees in Brazil, highlighting the challenges they face, and the strategies implemented for their integration. Methods: An analytical summary of 6 months, of fieldwork experiences supported by a brief review of official documents, and reports from non-governmental organizations. A contextualized analysis of the implemented strategies and resulting outcomes are highlighted to inspire researchers and professionals interested in the intersection of migration settlement actions, population public health, and health communication for individual and collective peace. Results: Operation Acolhida uncovered the synergy among many social and health-related professionals providing essential health services and promoting social and cultural integration as key elements for building lasting community peace. The Operation also tackles the challenge of combating the spread of false news that could lead to xenophobia and stigma against refugees. Effective mass communication was a cornerstone of Operation Acolhida disseminating correct and transparent information and promoting a narrative of empathy and cooperation among Brazilian host communities and Venezuelans. Conclusion: The interrelation between peace and health was undeniable. A peaceful social environment was critical for the maintenance of public health. For that, wide collaboration ensured to the host society’s health robustness contributing thus to collective stability and peace. The presented insights in this grassroots report contribute to the understanding and replication of effective practices in similar humanitarian initiatives globally.

‘Pure Prima Der Coronavirus’. Representations of the COVID-19 Era in Italian Collective Comics

ABSTRACT The response of Italian comics artists to the COVID-19 pandemic was immediate and variegated. Since the first outbreak in the Western world, these artists have been reflecting on themes related to the pandemic, such as social and individual crises, awareness of climate change, the spread of misinformation and fake news, the dissemination of conspiracy theories through social media, institutional inadequacy, and the impact of these on underrepresented communities. This meditation through comics on COVID-19 related issues confirms the socio-cultural relevance of this medium in contemporary Italian culture, and its capacity to negotiate traumatic events during times of collective crisis. This article discusses a set of case studies of a particularly successful type of comics publication, collective comics, published in Italy since February 2020. These works show how pandemic graphic narratives can serve as a privileged observatory of the historical trends and current tendencies that characterise the present Italian comics world.

REGULATING THE FAKE NEWS ON SOCIAL MEDIA PLATFORMS: Analyzing The Impact of Indian Legal System And Legal System Of Other Countries

Nowadays, there has been an increased use of social media platforms globally. Social media networks have become integral to modern communication, offering platforms where users can connect, share information, and engage with diverse content. Social media platforms have a greater reach, and a variety of people are utilizing these platforms. While social media has revolutionized information sharing and community building, it also presents challenges, particularly the spread of fake news. The spread of fake news through social media networks involves the rapid and wide dissemination of false and misleading information. This raises the question of how to detect fake news and whether it should be curbed. If fake news is not curbed, what will be the impact? If it is curbed, how does this action contradict fundamental rights? Social media networks act as intermediary platforms that host user-generated content. These intermediaries play a major role in sharing information, including fake news, on their platforms. However, the increasing impact of fake news has intensified scrutiny over whether these platforms should be more accountable and liable for the content they facilitate. This scrutiny ultimately raises the issue of intermediary liability for the spread of fake news through their platforms. The obligation of intermediaries in managing and curbing misinformation involves several critical responsibilities. This paper evaluates the technical aspects of these applications, obligations, fixation of liability, regulations, and implementation challenges, and provides a comparative analysis of intermediary liability under the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules 2021 and legal system of other countries.

Political Communication Strategies In The Digital Era For Generation Z Voters

The development of the digital era has changed political communication strategies, especially in reaching Generation Z voters. Social media such as Instagram, Facebook and TikTok have become the main platforms for delivering political messages. The purpose of this study is to analyze Generation Z's views on political communication strategies carried out by politicians in the digital era, as well as identify the effectiveness of social media platforms in attracting the attention of young voters. This research uses a qualitative method with data collection techniques through observation and interviews with six informants from Generation Z who have the right to vote. The analysis was conducted by categorizing the data into relevant themes. The results showed that political communication strategies using social media succeeded in attracting Generation Z's attention. Interesting political messaging and visual content are effective in increasing young voters' interest. The informants revealed that casual and familiar language, as well as the use of humor in political content, greatly influenced their engagement. Social media not only facilitates access to political information, but also enables direct interaction between politicians and voters. However, challenges such as the spread of fake news and the need for critical skills in assessing information are significant barriers for Generation Z. Political communication strategies that utilize social media are very effective for attracting Generation Z. However, efforts need to be made to ensure the accuracy of information and build critical skills in assessing news, so that Generation Z can participate informally in the political process.

Legal Challenges and Enforcement Strategies: Bawaslu's Role in Addressing Election Violations in Indonesia's 2024 Simultaneous Elections

Elections are a very important momentum for the community. This is because the community has the opportunity to determine the future leaders of the Indonesian nation in the next five years. Therefore, in making the election a success is not only the responsibility of Bawaslu as a supervisory institution, but also the obligation of all Indonesian people. The implementation of the 2024 election has many challenges in making the election a success, so it is necessary to have a role for Bawaslu in enforcing the law. The purpose of this study is to describe the challenges carried out by Bawaslu in enforcing violations of the law in Indonesia. The method of this research is qualitative descriptive. The results of this study show that there are four challenges for Bawaslu in the 2024 elections, including identity politics, money politics, neutralization of ASN, TNI, and Polri in election campaigns, and the spread of hoax news. In this case, it shows that Bawaslu plays an important role in law enforcement of simultaneous election violations in 2024. With the role played by Bawaslu in law enforcement, it can create a democratic and just country for the community.

FANE

In today's society, the continuous exchange of vast amounts of information, often irrelevant or misleading, highlights the need for greater awareness to distinguish between accurate and false information. Recognizing the reliability of information is critical to limiting the spread of fake news, a pervasive problem affecting various sectors, influencing public opinion, and shaping decisions in health care, politics, culture, and history. This paper proposes a methodology to assess the veracity of information, leveraging natural language processing (NLP) and probabilistic models to extract relevant features and predict the reliability of content. The features analyzed include semantic, syntactic, and social dimensions. The proposed methodology was tested using datasets that include social media news and comments captured during the lockdown due to COVID-19, providing relevant context for the analysis. Experimental validation of these different datasets yields promising results, demonstrating the effectiveness of the proposed approach.