Dissemination Of Fake News Research Articles

Psychological factors contributing to the creation and dissemination of fake news among social media users: a systematic review

Psychological factors contributing to the creation and dissemination of fake news among social media users: a systematic review

Factors of Fake News Sharing in the Context of the Ukraine-Russia Conflict: The Effect of Suspicions About European Unions' Intentions.

This study examines the separate and joint effects of potential factors of fake news dissemination. A sample of 359 Romanian participants (61.3% female), aged between 18-79years (M = 45.01; SD = 15.69) filled questionnaires assessing their perceptions of conflict between European Union (EU) and Ukraine; fake news sharing, and various socio-demographic data. Our results revealed that the behaviour of sharing fake news is positively associated with age and more prevalent in men. Also, people who perceive a conflict between EU and Ukraine and thus suspect that behind the EU's consistent support for Ukraine lies a hidden interest are more inclined to spread fake news. In the female group, age was found to moderate the relationship between perceptions of conflict between EU and Ukraine and fake news sharing. We also discuss the relevance of our findings for public communication efforts aiming to mitigate the dissemination of fake news.

Fake News Detection in Hausa Language Using Transfer Learning Method

Fake news poses a significant threat to societies worldwide, including in Hausa-speaking regions, where misinformation is rapidly disseminated via social media. The lack of NLP resources tailored to this language exacerbated the problem of fake news in the Hausa language. While extensive research has been conducted on counterfeit news detection in languages such as English, little attention has been paid to languages like Hausa, leaving a significant portion of the global population vulnerable to misinformation. Traditional machine-learning approaches often fail to perform well in low-resource settings due to insufficient training data and linguistic resources. This study aims to develop a robust model for detecting fake news in the Hausa language by leveraging transfer learning techniques with adaptive fine-tuning. A dataset of over 6,600 news articles, including both fake and truthful articles, was collected from various sources between January 2022 and December 2023. Cross-lingual transfer Learning (XLT) was employed to adapt pre- trained models for the low-resource Hausa language. The model was fine-tuned and evaluated using performance metrics such as accuracy, precision, recall, F-score, AUC-ROC, and PR curves. Results demonstrated a high accuracy rate in identifying fake news, with significant improvements in detecting misinformation within political and world news categories. This study addresses the gap in Hausa- language natural language processing (NLP) and contributes to the fight against misinformation in Nigeria. The findings are relevant for developing AI- driven tools to curb fake news dissemination in African languages.

Understanding users’ news-sharing behaviors: roles of risk perception, believability, fake news awareness and social tie variety

PurposeThis study aims to investigate the impact of news quality on users’ risk perceptions toward online news and its subsequent influence on perceived believability and user engagement in sharing news. Additionally, we explore the moderating effects of fake news awareness and social tie variety.Design/methodology/approachDrawing upon the social amplification of risk framework, this study investigates the relationship between news quality and users’ news-sharing behaviors, along with its underlying mechanism. An online questionnaire involving 399 eligible participants was employed for hypotheses testing, and the structural equation model served as the main analytical method.FindingsThe influence of news quality on users’ news-sharing behavior is sequentially mediated by risk perception and perceived believability. Individuals with a heightened awareness of fake news or a diverse social tie are more inclined to perceive greater risks associated with news-sharing behavior and question news authenticity.Originality/valueThis study contributes to the existing literature on users’ news-sharing behaviors by examining the influence of risk perception on the relationship between news quality, perceived believability and users’ news-sharing behavior. Additionally, it explores the moderating effects of fake news awareness and social tie variety. Our findings offer valuable insights into comprehending user inclinations towards news sharing and mitigating the dissemination of fake news.

Assessing And Mitigating The Impact Of Fake News In Nigeria

\The high rate fake news - news (presentation) that has been distorted or totally false - in the Nigerian media space is at the moment of concern to communication scholars, journalists, publicists, and political leaders. Like most fake products, fake news is made to look like or to have the characteristics of the original product – magnitude, timeliness, novelty, proximity, etc., thereby having the lethal impact of propaganda. The present study examines the sources, patterns of fake news dissemination, impact of and solutions to fake news in Nigeria. Questionnaires were randomly administered online to 398 heavy users of social media (257 bloggers; 97; regular users of social media who are non-professionals; 37 journalists and 7 others based on their followership ratings. The investigation revealed that most fake news in Nigeria revolve social, entertainment, and religious issues and are reported more often in “text” and “videos” on Facebook, Instagram, and WhatsApp. The main sources of fake news in Nigeria are non-professional journalists and the high rate of fake news has largely been due to the skilful framing by their disseminators, lack of time and skill to fact-check fake news by the audience, and lack of effective enforcement of best journalism practices.

Ingenuidade e Inércia Cognitiva como Atributos para a Viralização de Fake News nas Redes Sociais Online

The dissemination of fake news on online social networks has created a scenario of disinformation in which manipulation and control are used to influence behavior and gain advantages. This phenomenon has motivated joint efforts to inhibit its spread. However, despite these initiatives, the problem persists. The assumption is that users of these channels, who are key agents in confronting this phenomenon, become contributors to disinformation when affected by naivety or cognitive inertia. The aim is to analyze the role of these users in the spread of fake news and identify factors that influence their uncritical stance. Conducted through a narrative review of the literature for theoretical purposes, this analysis adopts a reflective, exploratory and essayistic approach to the condition of co-participants of online social network users in the face of the problem of fake news, based on key themes such as post-truth and critical information competence, and illustrated by an empirical analysis of three specific cases of viral fake news. It is concluded that users of online social networks, when naive and inert, are part of the problem and, therefore, need to be more reactive, instead of, consciously or unconsciously, becoming agents of misinformation.

DEVELOPING A WEB BROWSER EXTENSION TO PREVENT THE SPREAD OF FAKE NEWS

Fake news causes huge social problems such as misdirecting and provoking the masses, creating an atmosphere of chaos by spreading fear. Detecting and stopping the dissemination of fake news has been an important and priority issue due to its rapid spread, difficulty to detect, and negative effects. In our study, a new Chrome extension that detects fake news has been developed in order to detect and prevent the spread of fake news. In the study; natural language processing, data mining methods, and various machine learning techniques for instance Passive Aggressive, Random Forest, Support Vector Machine, AdaBoost, XGBoost, and Long-Short-Term Memory (LSTM) algorithms are used. The accuracy rates of the algorithms, confusion matrices, AUC rates, and ROC curves were compared. The created machine learning model has been implemented in the online internet environment by using Flask and Rest API in the Python program. Finally, the Chrome extension interface was built using Javascript, HTML, and CSS. The LSTM algorithm gave the highest prediction result with a rate of 90.72% compared to other algorithms.

The Criminal Act of Dissemination of Fake News via Social Media in the Perspective of Individualization of Criminal

This study focuses on 2 (two) main issues. First, how is the judge's decision considered based on the perspective of individualization of punishment? The second main issue is the factors that influence the judge's decision based on the perspective of individualization of punishment for the crime of spreading false news through social media. The implementation of the perspective of individualization of punishment in the Decision of the Jayapura Class IA District Court Number 16/Pid.B/2020/PN Jap is implied in 6 (six) main points of the judge's consideration, namely (1) the indictment and criminal charges of the Public Prosecutor, (2) the defense of the Legal Counsel, (3) evidence in the form of witness statements, both charge witnesses and a de charge witnesses, expert statements, defendant statements, (4) evidence, (5) trial facts, (6) proof of elements of the crime of spreading false news through social media. In addition, 5 (five) factors influence the decision, namely (1) the condition of the perpetrator, (2) the motive of the perpetrator, (3) the impact of the crime committed, (4) the form of the perpetrator's mistake, (5) the purpose of the punishment. Thus, the judicial freedom of judges does not mean that judges abandon the perspective of individualization of punishment this perspective of individualization of punishment provides direction for judges in constructing decisions on criminal cases of spreading fake news through social media, which is fully supported by at least 2 (two) pieces of evidence and the judge's legal conviction (juridische overtuigingen).

Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach

The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support Vector Machines (SVMs), Multilayer Perceptrons (MLPs), and Convolutional Neural Networks (CNNs). Our analysis demonstrates that SVMs using TF-IDF embeddings and CNNs employing TF-IDF embeddings achieve the highest overall performance in terms of accuracy, precision, recall, and F1 score. These results suggest that TF-IDF, with its capacity to highlight discriminative features in text, enhances the performance of models like SVMs, which are adept at handling sparse data representations. Additionally, CNNs benefit from TF-IDF by effectively capturing localized features and patterns within the textual data. In contrast, while Word2Vec and FastText embeddings capture semantic and syntactic nuances, they introduce complexities that may not always benefit traditional ML models like MLPs or SVMs, which could explain their relatively lower performance in some cases. This study emphasizes the importance of selecting appropriate embedding techniques based on the model architecture to maximize fake news detection performance. Future research should consider integrating contextual embeddings and exploring hybrid model architectures to further enhance detection capabilities. These findings contribute to the ongoing development of advanced computational tools for combating misinformation.

FAKE NEWS DETECTION

The Internet is one of among the greatest inventions of the world and there are millions of individuals who utilize it. These persons employ it in various ways, as listed below: There are various social networks that are available for use among such users. Often they can be just ordinaries who decide to make a post or share the news on the internet. Such platforms offer no means of confirming any users or the content they post. Therefore, some of the users attempt to indulge in active dissemination of fake news through the platforms. This fake news can aimed at an individual, a community, a company or a political party. It becomes virtually impossible for a human being to follow all the fake news. So, currently, there exists the need for automatic classification of fake news using machine learning classifiers. My description of machine learning classifiers such as passive agressive classifiers and algorithm such as K-Nearest Neighbor, Support vector machine(SVM) is used for detecting fake news is described in this systematic literature review. KEYWORD: Fake News Dissemination, Social Networks, Automatic Classification, Machine Learning Classifiers, Passive Aggressive Classifiers, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Systematic Literature Review, Challenges in Fake News Detection.

Development of a machine learning algorithm for fake news detection

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection.

To secure an e‐commerce system using epidemic mathematical modeling with neural network

SummarySecuring an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.

Optimizing the Service Efficacy of Crowd Ratings in Curbing Fake News Dissemination on Social Media

Optimizing the Service Efficacy of Crowd Ratings in Curbing Fake News Dissemination on Social Media

“Make it difficult”: using disfluency to minimize higher motivation effects on fake (and real) news dissemination on social media

Purpose: To investigate the effect of disfluency (perceived difficulty) and prior motivation to do and disseminate electronic word-of-mouth (eWOM) on headlines/posts online, as well as the mediating role of perceived truth. Design/methodology: This study involves three online experiments emulating “X” (former Twitter) messages and Instagram/Facebook posts. Disfluency was measured in Experiment 1 and manipulated in Experiments 2 and 3 while also measuring prior motivation to disseminate eWOM. Findings: higher prior motivation increased fake and authentic news dissemination, but disfluency diminished this effect through its influence on perceived truth. Originality/value: These results demonstrate that people tend to disseminate authentic and fake news owing to a carryover effect, and this tendency is affected by prior eWOM motivation. Disfluency can, thus, not only help prevent fake news dissemination but also inhibit authentic (real) news dissemination. These effects are due to perceived truth, not attention or perceived relevance, and only affect people with higher eWOM motivation. Because the perceptual disfluency manipulations tested are like what occurs daily (i.e., “dark theme” in smartphones and Instagram’s use of font colors), we propose that similar proceedings can decrease the mass propagation of widely disseminated fake news.

Fake News Makes the News: Definitions and Framing of Fake News in Mainstream Media

ABSTRACT This paper explores how news media define and frame fake news, thus orienting public perceptions of fake news in terms of problem, causality, responsibilities, and solutions. It first maps the news discourse on fake news through semantic network analysis of 1,646 articles published in 2017–2018 (when the term reached its peak) and 2021–2022. It then analyzes the definitions and frames of fake news in a sample of 122 articles representative of the clusters identified in the previous step of our analysis. The semantic networks in both periods reveal similar dominant actors - political actors, social media platforms and professional journalists – that shape the “fake news” narrative. Fake news is either defined as disinformation and political propaganda or, more often, as false accusations that Donald Trump and his supporters make against mainstream media for their negative reporting. Political and social media actors are mostly held responsible for undermining public trust in mainstream media through fake news dissemination and/or false accusations. In this polarized conception of the public sphere, the public is positioned as a victim rather than an active contributor to the fake news problem and solution.

Motives of Sharing Fake News and Effects on Mental Health of Social Media Users: A Meta-analysis

After the outbreak of COVID-19, the world faced various economic, health, and social challenges alongside terror from fake news and posts on social media. There has been a significant impact from the spread and sharing of fake news on the mental health of social media users. Henceforth, this study examined this issue within the context of uses and gratification theory. In doing so, the study performed a meta-analysis using cross-sectional studies regarding social media usage and the association between use and gratification among social media users. Fifteen articles from 2010 to 2020 were retrieved and finalised through strict selection criteria from Google Scholar, PubMed, and Web of Science. A random effect model was deployed to estimate the uses and gratification achieved from social media usage and the motivation behind sharing specific posts. The selected articles suggested a positive and significant role of the uses and gratification perspective in motivating users to select specific social media platforms to create and share posts. It was also identified that gratification can result from the performance of both positive and negative actions on social media by social media users. This study offers new insights into social media usage from the uses and gratification perspective during health crises, shedding light on its impact on fake news dissemination and suggesting practical algorithmic control measures. Keywords: Uses and gratifications, meta-analysis, fake news, mental health, social media.

Analysis of the general election hoax news phenomenon from the perspective of Pancasila as the integrity of the Indonesian nation

A common occurrence in society that frequently causes divisions among people is the dissemination of fake news before to the general election. In addition to examining the logical outcomes that can result from the incorrect and inappropriate application of Pancasila values in the affairs of the nation and state, this study seeks to understand how attitudes toward fake news that circulates in Indonesian society are related to the application of Pancasila values, particularly prior to general elections or democratic parties. Using primary and secondary sources gathered from official websites and online news, a qualitative descriptive approach of library research is employed. A literature review was the method of data collecting employed in this investigation. Qualitative analysis was done on the gathered data. Stop hoax. Stop Money Politics. Hoax must be stopped.

Influences on COVID-19 Vaccine Adherence among Pregnant Women: The Role of Internet Access and Pre-Vaccination Emotions.

The onset of the COVID-19 pandemic brought about global uncertainties and fears, escalating the dissemination of fake news. This study aims to analyze the impact of fake news on COVID-19 vaccine adherence among pregnant women, providing crucial insights for effective communication strategies during the pandemic. A cross-sectional, exploratory study was conducted with 113 pregnant women under care at a Women's Health Reference Center. Data analysis included relative frequency and odds ratio to assess the relationship between sociodemographic and behavioral variables regarding vaccination. In the behavioral context of vaccination, internet access shows a significant association with decision-making, influencing vaccine refusal due to online information. Nuances in the odds ratios results highlight the complexity of vaccine hesitancy, emphasizing the importance of information quality. Pre-vaccination sentiments include stress (87.61%), fear (50.44%), and anxiety (40.7%), indicating the need for sensitive communication strategies. Results revealed that pregnant women with higher education tend to adhere more to vaccination. Exposure to news about vaccine inefficacy had a subtle association with hesitancy, while finding secure sources was negatively associated with hesitancy. The behavioral complexity in the relationship between online information access and vaccination decision underscores the need for effective communication strategies. In the face of this challenging scenario, proactive strategies, such as developing specific campaigns for pregnant women, are essential. These should provide clear information, debunk myths, and address doubts. A user-centered approach, understanding their needs, is crucial. Furthermore, ensuring information quality and promoting secure sources are fundamental measures to strengthen trust in vaccination and enhance long-term public health.

FATHOM OUT THE NUANCES OF SOCIAL MEDIA LITERACY AND ITS PRACTICAL ALLUSIONS TO MISINFORMATION – A REVIEW

Digital media has become an integral part of our daily lives, connecting people from all around the world in a matter of seconds. In recent years, social media has revolutionized the flow of information and helped people to connect, share, and discuss things at their fingertips. However, this rapid exchange of information has also given rise to a concerning phenomenon – the spread of misinformation. "Misinformation" has led to a wide range of negative consequences, such as political polarization, social unrest, and erosion of trust in traditional media. This review explores the importance of social media literacy, its advantages, impacts, and effects, along with allusions to misinformation. The methodology includes a systematic search of literature in the databases from Internationally double-blinded peer-reviewed journals between 2016 and 2022, in English that also includes scientific articles. A total of 64 articles were obtained. A selection process of articles took place, applying certain inclusion and exclusion criteria, resulting in a total of 10 articles being selected. The findings signify that the conception of social media literacy has just started and more research is needed to identify its practical and theoretical implications. This is linked to the content and competencies, critical thinking, content, and context of the information, and the time and literacy level and fear of missing out on the user. Socio-emotional competencies, literacy level, and time spent stand out since social media is a frequent place of interaction between people. Thus, it is crucial to develop social media literacy skills to combat the dissemination of fake news and ensure a more responsible citizen and informed society.

Developing a Stressor Strain Outcome Model That Predicts Fake News Sharing Behaviour on Social Media: The Mediating Role of Social Media Exhaustion

Despite the growing corpus of knowledge on fake news sharing, nothing much is known about the relationship between the dissemination of fake news and social media exhaustion from an empirical standpoint. There is limited knowledge that exists to show if social media exhaustion could contribute to fake news-sharing behaviour. To address the gaps in the literature, this study created and evaluated a stressor strain outcome model that forecasts social media spread of fake news. It looked at the contributing elements of social media exhaustion before connecting it to disseminating fake news online. It also looked at how social media exhaustion functions as a mediator between its antecedents and fake news-sharing activity. Data was obtained from 1650 social media users using an online survey. Smart PLS structural equation modelling was used to examine the data. Our results, which drew on the stressor strain outcome model, demonstrated that, in the context of fake news-sharing behaviour, social media usage intensity is the most important predictor of social media exhaustion. Social media exhaustion was also predicted by information irrelevance, social media overload, the fear of missing out, and information overload. Also, social media tiredness predicts how people would share misleading news. Further research indicates that the link between information relevance, social overload, fear of missing out, information overload, and the dissemination of fake news is fully mediated by social media exhaustion. Conversely, it partially mediated the link between the frequency of using social media and the dissemination of false information. Our research provides recommendations to social media businesses and developers who wish to reduce concerns about social media tiredness and the spread of fake news.