Spread Of Fake News Research Articles

Empirical Studies of Russian–Ukrainian War Related Fake News (Part 2)

The Russian–Ukrainian war, which broke out on 24 February 2022, resulted in several paradigm shifts in cyberwarfare. One aspect of these changes is psychological operations. Russia and Ukraine have conducted extensive psychological operations campaigns to fulfil their war aims, which have since been intense along modified objectives. This series of studies examines the impact of war-related fake news through various empirical research. In the first part of the paper, the authors read the emergence of psychological operations and related terms in the international academic literature using network analysis methodology. In the second part of the paper, the authors use sentiment and network analysis to investigate the spread of different fake news. In the third study, the authors measure the attitudes toward the perception of the Hungarian Defence Forces from the perspective of the war in the neighbouring country.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Unveiling the hidden patterns: A novel semantic deep learning approach to fake news detection on social media

The rise of social media as a source of news consumption has led to the spread of fake news, posing serious consequences for both individuals and society. The detection and prevention of fake news are essential, and previous research has shown that incorporating news content along with its associated headlines and user comments can improve detection performance. However, the semantic relationships between these elements have not been fully explored. This paper proposes a novel approach that models the relationships between news bodies and associated headlines/user comments using deep learning techniques, such as fine-tuned Bidirectional Encoder Representations from Transformers (BERT) and cross-level cross-modality attention sub-networks. In our proposed model, we utilize two different configurations of BERT: pool-based representation, which provides a representation of the entire document, and sequence representation, which represents each token within the document (i.e., at the word and text levels). The approach also encodes user-posting behavioural features and fuses the output of these components to detect fake news using a classification layer. Our experiments on benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art (SOTA) approaches, highlighting the importance of utilizing semantic relationships for improved fake news detection (FND). These findings have significant implications for combating the spread of fake news and protecting society from its negative effects.

AGENTIC GOVERNANCE: A FRAMEWORK FOR AUTONOMOUS DECISION-MAKING SYSTEMS

The proliferation of fake news has become a significant concern in recent years, with far-reaching consequences for individuals, communities, and society. Artificial intelligence (AI) has the potential to play a crucial role in detecting and mitigating the spread of fake news. However, the use of AI in fake news detection also raises important governance considerations. In this paper, we propose a novel approach to AI governance in fake news detection, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed approach.

THE ROLE OF SOCIAL MEDIA IN INCREASING RURAL GENERATION Z PARTICIPATION IN THE 2024 ELECTIONS : A STUDY IN SUNGAI ANA VILLAGE, SINTANG DISTRICT

This research investigates the role of social media in increasing the political participation of Generation Z in rural areas, especially Sungai Ana Village, Sintang District during the 2024 General Election. Using a mixed methods approach, this research combines a literature review, survey, in-depth interviews, social media content analysis, and observation participatory to collect comprehensive data. These findings reveal that social media platforms such as Instagram, TikTok, and WhatsApp are very important for increasing political awareness among Generation Z users in Sungai Ana Village, Sintang District. About 91% of respondents rely on social media as their main source of political information, and admit that these platforms help them better understand political issues and candidates. These findings highlight the need for increased media literacy and critical thinking skills to combat the spread of fake news. The study concludes with recommendations for policymakers and stakeholders, emphasizing the importance of improving internet infrastructure in rural areas, promoting educational campaigns on media literacy, and fostering collaboration between governments, educational institutions, and social media platforms to develop effective political education initiatives. By overcoming these challenges and harnessing the potential of social media, Generation Z's political engagement in rural areas, especially in Sungai Ana Village, can be significantly increased, resulting in more active and informed participation in the democratic process

A Detecting Fake News Using Machine Learning: A Comparative Study of Techniques

Many people get their news via the Internet and social media platforms, and given the rapid growth of these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm society and the individual is known as fake news. By deliberately spreading false information, the media distort public opinion and threaten the social order by leading people to believe things that are not true. With the massive expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it poses some difficulties due to limited resources (such as datasets and published research). This paper presents diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy and can save time and effort by automatically identifying fake news, especially in light of the massive amount from materials available on the Internet.

DETECTION OF FAKE NEWS ON TWITTER USING MACHINE LEARNING: AN XGBOOST-BASED APPROACH WITH SENTIMENT AND SOURCE CHARACTERISTIC ANALYSIS

The spread of fake news on social media platforms is becoming an increasingly alarming problem with fake news becoming more deceptive and harder to detect. Twitter, in particular, poses a significant threat as fake news spreads faster than real news on the platform, enhancing misinformation and leading to serious consequences.This project presents a novel machine learning-based approach for detecting fake news tweets on Twitter using the TruthSeeker 2023 dataset from the University of New Brunswick. As the largest ground truth dataset for fake news detection on social media, it contains over 130,000 crowdsourced tweets, enabling the creation of a broader and more applicable model for real-world scenarios. The algorithm employed in this study leverages the properties of gradient-boosted decision tree models (XGBoost) to develop a novel method for classifying fake and real news tweets. The proposed model preprocesses the data by extracting additional features for each tweet, such as detailed sentiment analysis of both the tweet and the related news statement, as well as features pertaining to the author. These features are added to the tweets feature vector. The enhanced feature vectors are then fed into an XGBoost model with tuned hyperparameters determined through a grid search algorithm to perform binary classification. The additional extracted features increase the robustness of the model by highlighting key differentiating factors between real and fake tweets. The results of this study demonstrate the effectiveness of the proposed algorithm, achieving an accuracy of 0.9335 on over 13,000 unseen tweets.

The uses and gratifications (U&G) model for understanding fake news sharing behavior on social media

BackgroundSocial networking sites (SNSs) have made information sharing easier than ever, this ease has also facilitated the spread of misinformation. ObjectiveTo ascertain and validate the uses and gratifications (U&G) factors associated with fake news-sharing behaviors on SNSs. MethodsA five-factor model based on the U&G theory was measured to estimate fake news-sharing behavior among university students, using the structural equation modeling. FindingsPassing time has a strong positive influence on fake news sharing behavior (β = 0.517, CR = 4.017, p < 0.001). Information seeking has a weak and non-significant positive influence on fake news sharing behavior (β = 0.308, CR = 1.569, p = 0.117). However, socialization (β = −0.164, CR = −1.883, p = 0.377), entertainment (β = −0.070, CR = −0.878, p = 0.380), and information sharing (β = −0.044, CR = −0.295, p = 0.768) gratifications have non-significant negative influences on fake news sharing behavior. The goodness-of-fit indices indicate that the proposed model is acceptable (χ2 = 2.195 df = 237; p < 0.001; GFI = 0.920; IFI = 0.901; CFI = 0.899; SRMR: 0.077; RMSEA = 0.049). ConclusionPassing time is the key predictor of fake news sharing behavior, while information-seeking gratification also contributes positively to this behavior. The study suggests several different approaches to prevent the spread of fake news, including implementing news literacy programs, installing a firewall, supporting fact-checking organizations, and implementing regulations and legislations for responsible social media practices. ImplicationsThe study is significant because it goes beyond the “what” of fake news sharing to understand the “why”. This deeper understanding can lead to more effective solutions for promoting responsible information-sharing behavior and combating the spread of fake news in the post-truth era.

Effects of Intervention Timing on Health-Related Fake News: Simulation Study.

Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. This study investigated the appropriate timing of interventions to minimize the side effects of fake news. A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response.

Toward an Efficient Hybrid Clustering and Classification Approach for Fake News Detection in Social Network

Objectives: The spread of fake news on social media has become a pressing issue in recent times. Despite various organizations' efforts to address this problem, it continues to persist, necessitating finding more effective solutions. This study implements a machine learning-based approach for identifying fake news on social media with improved accuracy. Methods : The study's methodology utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to extract semantic features from news articles. Then, a hybrid clustering and classification approach is used, which combines K-means clustering and Artificial Neural Network (ANN) classifiers. Data used is a secondary data consisting of 23481 world news articles obtained from the Reuters.com website and 21417 unreliable news from polifact.com. During the training process, the number of units and layers on the model was tuned to optimize model performance. The model was compared to other baseline models such as KNN, SVM, Decision tree, and Boosted decision tree to establish the best performing model. Findings: The results from the two algorithms were weighted to final classifications using Bayesian probability theory. The proposed approach achieved an accuracy of 99.78%, a sensitivity of 100%, and specificity equal to 99.73%. The model's precision is 99.74%, indicating its ability to identify fake news. The F-score of the approach is 99.87%, indicating that the model strikes a good balance between correctly classifying fake news articles and reliable news articles. The approach outperformed other machine learning classifiers, including KNN, SVM, Decision Tree, and Boosted Decision Tree. Novelty : The study applies a hybrid approach with a classification and clustering algorithm to improve detection of fake news on social media, the approach is tested with varied real-world datasets to establish its robustness under different vocabularies and vocabulary sizes. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Hybrid clustering approach, Classification, CNN, LSTM, Boosted Decision Tree, Social platforms, K-means

Prevalence of Fake News in Relation to the Responsible Netizenship of University of Luzon Criminology Students

Fake News is used to deceive people. It is an act of spreading misinformation or wrong information intentionally to misguide others to gain financial returns or political gains. For them to become law enforcers in the future, the researcher was interested in how responsible criminology students are as netizens in today’s cyberspace in terms of their capacity to spot fake news and strategies to stop it from spreading. The researcher applied the Media Dependency Theory introduced by the American communication researcher Sandra Ball-Rokeach and Melvin Defleur, it is a systematic approach to the study of the effects of mass media on audiences and of the interaction between media, audiences, and social systems. For the data collection instrument, the researcher used a dichotomous formula or simply the percentage scale to interpret the results of the answers to the given questionnaire by 288 respondents who are all criminology students. The study is premised on the conjecture of the following: 1. Fake news is prevalent online; 2. The responsibility and awareness of UL criminology students in terms of netiquettes, political manipulation, and bias in story selection; 3. Internet etiquettes (netiquettes) of UL criminology students; 4. The significant relationship between responsible citizenship and the prevalence of fake news. 5. Measures to lessen the spread of fake news

Assessing the Influence of Unverified Instagram Blog Content on the Cognitive and Behavioral Pattern of Undergraduates: An Empirical Study of Obafemi Awolowo University (OAU)

The proliferation of unverified Instagram blog content in Nigeria raises concerns about the credibility and trustworthiness of information disseminated through this medium and unverified contents on Instagram blogs lack proper fact-checking, leading to the spread of misinformation, rumors, and fake news which is why this research investigated the impact of unverified Instagram blogs’ contents on Nigerian Undergraduates in Obafemi Awolowo University, Ile-Ife. The study adopted the gratifications theory, which suggests that media users play an active role in choosing and using the media and that the user has alternate choices to satisfy their need. The study also adopted a survey research method to conduct the study through the use of questionnaire. The research sample size turned out to be 543 respondents but only 403 respondents completed the questionnaire from three selected faculties, which translated to a response rate of over 74%. The statistical analysis was carried out using the statistical package for social sciences (SPSS 20). The study findings revealed high level of agreement among respondents regarding their engagement with Instagram blogs, mirrored by a weighted mean of 4.04. Additionally, the study also revealed a moderate level of agreement among with a weighted mean of 3.57 regarding respondent’s perception and attitude towards Instagram blogs’ contents. Moreover, the majority of respondent agreed that exposure to unverified information on instagram can negatively influence students’ academic performance, reflected by a weighted mean of 3.99. The research recommended that a supportive online environment should be prioritized as a fundamental intervention strategy to enhance the cognitive and behavioral pattern of undergraduate students’ such as awareness campaigns, digital programs and peer support network as well as inaugurating promotion of positive engagement is essential to counteract the prevalence of objectifying content on social media platforms. This study, therefore, concludes that unverified information on social media platform such as instagram can influence students’ engagement, perceptive, attitude and academic performance.

The use of digital platforms by citizen aid actors during the Ukraine humanitarian crisis

Following the Ukraine humanitarian crisis, which was a result of the war in Ukraine, ordinary citizens stepped up to spontaneously coordinate various humanitarian responses to support affected populations. In this article such individuals who founded or coordinated this form of spontaneous humanitarian aid are referred to as citizen aid actors (CAA). This paper explores the work of citizen aid actors in Poland, a country which received many of the people displaced by the war in Ukraine. The study applies the concept of digital affordances to illustrate and argue that citizen aid actors relied on the features available on digital platforms to attain their goals. It is an outcome of a qualitative research approach where interviews, participant observation, and analysis of relevant online material about the studied citizen aid actors were used as methods of collecting data. The findings indicate that these actors used diverse digital platforms at different stages of the humanitarian aid projects, from establishment, organization and sustenance. While this study demonstrates how specific digital platforms contributed to the accomplishment of the work done by CAAs, it also highlights how problematic it can be when the actors largely rely on digital platforms. As such, potential risks associated with overreliance on digital platforms as a way of implementing projects are factored in. Ensuing concerns include the presence of online trolls, the spreading of fake news, and internet disruptions as potential obstacles for the success of the aid projects.

The spread of fake news: Disclosure willingness role

The epidemic has had a profound negative impact on individuals worldwide, leading to pervasive anxiety, fear, and mental instability. Exploiting these fears, a significant amount of fake information proliferates and spreads rapidly on social networks. This study explores the factors that cause individuals to believe fake news under stressful and fearful conditions by applying the truth-default theory. Data was collected online in Vietnam, using Smart PLS software to analyze the research model. The findings indicated that risk perception, media trust, trust in celebrity posts, and stress were factors that urge users to believe news posted on social media, and even they actively share this news on their own channels. Disclosure willingness moderated the relationship between adoption fake news and sharing it. Both theoretical and practical implications were discussed.

Use of Social Media Platforms for Citizen Journalism on Security Challenges in Nigeria among The Polytechnic Ibadan Students

The immediacy of social media gives citizens the ability to monitor real-world events and security issues and get immediate feedback that can help shape their responses and priorities, which can also be valuable in promoting security in general. Through its constant use, social media has played a significant role in fostering both positive and negative facets of insecurity. Therefore, this study investigated use of social media platforms and engagement in citizens’ journalism regarding security challenges in Nigeria among The Polytechnic Ibadan Students. The study adopted descriptive survey research design; multi-stage sampling procedure was adopted in the sample selection of 403 respondents. A validated instrument named; Citizen Journalism Social Media Questionnaire(r=0.874) was used for data collection; data was analyzed using descriptive and inferential statistics. Findings revealed that students of The Polytechnic Ibadan made use of popular social media platforms such as Facebook, Instagram, WhatsApp (weighted x̄ 1.40) respectively and engaged in online discussion of security challenges to a large extent using Facebook, WhatsApp, Instagram, Twitter, YouTube, Telegram, and others (weighted x̄ 2.11). Also, there was no significant difference (r = 0.053, p&lt;0.05) in level of engagement in online discussion on security challenges in Nigeria. Thus, the study concluded that The Polytechnic Ibadan students’ social media use does not have any influence on security challenges in Nigeria. The study recommended that security information should be promptly disseminated virtually by security operatives to avoid spreading of fake security news through social media platforms.

What Makes Fake News Appeal to You? Empirical Evidence from the Tweets Related to COVID-19 Vaccines

Social media has become a popular means for users to accept and share the news. At the same time, however, it has also enabled the wide spread of fake news. The negative impact of fake news on society has been rapidly increased. To mitigate this problem, this study aims to find out the effect of social media users’ types of perception of information toward the acceptance and intention of spreading fake news. We conducted an online experiment with 743 of social media users and showed the following results. First, users respond differently depending on the type of message provider even if the same fake news. Second, users who relied more on experiential information processing system were more likely to accept fake news regardless of their perceived social conformity.

Unraveling the dynamics of COVID-19 fake news spread: The influence of socialization, entertainment, and altruism in instant news sharing on social media

During the Coronavirus disease of 2019 (COVID-19), the dissemination of false information on social media increased significantly. The examination of social factors influencing the proliferation of fake news related to COVID-19 necessitates exploration from various perspectives and dimensions. Our research surveyed 392 Pakistani social media consumers, employing a survey questionnaire method conducted through snowball sampling technique. We utilized SPSS 26.0 for descriptive statistics, Pearson correlation, and regression analysis. The mediation and moderation effects were tested using Andrew F. Hayes' Process program (V3.3). Our findings revealed that COVID-19-related fake news tends to spread through instant news sharing behavior, driven by socialization and entertainment motives, acting as mediating factors in the relationship between instant news sharing and the dissemination of false information. Altruism also plays a moderating role, playing a moderating role in the impact of instant news sharing on fake news sharing, and also playing a moderating role in the impact of socialization on fake news sharing. Employing a rational choice theory framework, our study identifies instant news sharing, socialization, entertainment, and altruism as key rational choices influencing the sharing of pandemic-related fake news among Pakistani social media users.

Political Buzzers’ Role on Social Media During The 2019 Indonesian Presidential Election

The 2019 presidential election involved political buzzers as one of the keys to the campaign on social media for both parties. The existence of the buzzers itself has a positive and negative impact on Indonesian society. Currently, the term “buzzers” have changed from a marketing function to a word that has a negative connotation because it is often identified with hoax news. This research uses qualitative methods, using literature studies from various documents, news, journals and previous research that are relevant to the role of buzzers in the spread of fake news on social media in the 2019 Presidential Election. After conducting searches and obtaining material that is relevant to the topic of analysis, this article analysed material and news developing in society regarding the role of buzzers in spreading hoaxes in the 2019 Presidential Election. The aim of the hoax campaign is to change the stigma from bad to good to increase electability and popularity, but it can also be the opposite, from good to bad. The 2019 presidential election noted that both parties were using them and at the same time were under attack from buzzers. One of the strategies used by buzzers during the 2019 Presidential Election is to normalise lies where both parties slander each other. The existence of a hoax campaign makes news about the Presidential Election confusing and needs to be checked carefully to ensure the facts of the news. Keywords: Election, Buzzer, social media, presidential election, fake news.

Mengintegrasikan Nilai-Nilai Buddhis dalam Penggunaan Media Sosial

This research aims to explore communication ethics in the use of social media, with a focus on Buddhist values ​​that can provide guidance for individuals in using social media. Ethics, as part of the moral values ​​implemented by religion, should be a guide for wise behavior, including when using social media. However, in reality, there is negligence in the use of social media in society, which involves practices such as smear campaigns, hate speech, online bullying (cyberbullying), and the spread of fake news (hoaxes). Therefore, this research is interesting to dig deeper through analysis of Buddhist values, so that Buddhist teachings can become a filter and guide for people to communicate wisely on social media. The research method used in this research is library research. Data analysis in this study highlights that social media use should reflect kindness, teachings of truth, joy, and consistency with facts. These four values ​​must be supported by other aspects, such as subtlety of language and expression, the basis of love in speaking, as well as consideration of the purpose and appropriate time in conveying the message. Manifestations can be found in content, writing or posts that have an educational, inspiring and useful character. In addition, when receiving information on social media, it is important to be selective and critical, but remain open to constructive criticism, while upholding healthy discussions on social media.

SOCIOPRAGMATIC ANALYSIS OF HATE SPEECH IN HILARION HEAGY’S CONVERSION NEWS ON TWITTER

This research discusses the phenomenon of hate speech in Hilarion Heagy's conversion news on Twitter. Hilarion Heagy, a former Orthodox priest who later embraced Eastern Catholicism before ultimately converting to Islam, has sparked significant controversy within religious circles, eliciting varied reactions, especially on social media. This research addresses a significant gap in the literature by examining hate speech in the context of religious conversion. Therefore, this research aims to identify types of hate speech and find out the intent behind hate speech. This research utilizes the National Police Chief's circular number SE/6/X/2015 to identify types of hate expressions and uses Searle's illocutionary speech act theory to explain the meaning behind hate expressions. In this research, data was taken from comments on Twitter related to the news of Hilarion Heagy's conversion. The results of the research found that the expressions of hatred in the news about Hilarion Heagy's conversion were related to religious issues. The outcomes found five types of hate speech, with the most performed being Blasphemy (17 times), followed by Provoking (11 times), Defamation (10 times), Insult (7 times), then the least one being Spreading fake news (5 times). As for the types of illocutionary acts, the most dominant was Assertive (40 times), followed by Directive (7 times) and Expressive (3 times). There were no Commissive and Declaration types found at all.