The Fake News and Polarization Landscape: Scoping Review of Fake News Research and its Link with Attitude Polarization.

Social media platforms need to respond effectively to the accusation of distributing fake news. Thus, this study investigates the extent to which different types of fake news flags (Pinocchio symbol, stop symbol, caution symbol) and the type of device (smartphone vs. PC) used to engage with (fake) news posts influence believability, user engagement, and cognitive elaboration. We test for such differences in an experimental design using data from 256 participants exposed to artificially generated Facebook posts on political (fake) news. Results confirm that fake news flags provide an effective countermeasure against believing in fake news. However, the semantic priming effect of different warning symbols used, which is inextricably intertwined with the device on which fake news is consumed, makes the difference between effective and less effective fake news flags. Smartphones do not per se increase (fake) news believability compared to PCs, but our findings suggest that stronger warnings may be required on smartphones than on PCs. Overall, this study provides a rich and contextualized understanding of fake news flags and user interaction with fake news, pointing to some important practical implications for social media platform designers.

The spread of digital disinformation (aka "fake news") is arguably one of the most significant threats on the Internet today which can cause individual and societal harm of large scales. The susceptibility to fake news attacks hinges on whether or not Internet users perceive a fake news article/snippet to be legitimate (real) after reading it. In this paper, we attempt to garner an in-depth understanding of users’ susceptibility to text-centric fake news attacks via a neuro-cognitive methodology (thus corroborating as well as extending the traditional behavioral-only approach in significant ways). In particular, we investigate the neural underpinnings relevant to fake vs. real news through EEG, a well-established brainimaging technique. We design and run an EEG experiment with human users to pursue a thorough investigation of users’ perception and cognitive processing of fake vs. real news. We analyze the neural activity associated with the fake vs. real news detection task for different categories of news articles.Our results show that there may be no statistically significant or automatically inferable differences in the way the human brain processes the fake vs. real news, while marked differences are observed when people are subject to (real or fake) news vs. resting state and even between some different categories of fake news. This neurocognitive finding may help to justify users’ susceptibility to fake news attacks, as also confirmed from the behavioral analysis. In other words, the fake news articles may seem almost indistinguishable from the real news articles in both behavioral and neural domains. Our work serves to dissect the fundamental neural phenomena underlying fake news attacks and explains users’ susceptibility to these attacks through the limits of human biology. We believe that this could be a notable insight for the researchers and practitioners suggesting that the human detection of fake news might be ineffective, which may also have an adverse impact on the design of automated detection approaches that crucially rely upon human labeling of text articles for building training models.

The objectives of the study were to identify the relationship between big data analytics with context-based news detection on digital media in the data age, to find out the trending approaches to detect fake news on digital media, and to explore the challenges for constructing quality big data to detect misinformation on social media. Scoping review methodology was applied to carry out a content analysis of 42 peer-reviewed research papers published in 10 world-leading digital databases. Findings revealed a strong positive correlation between quality big data analytics and fake news detection on digital media. Additionally, it was found that artificial intelligence, fact-checking sites, neural networks, and new media literacy are trending techniques to identify correct information in the age of misinformation. Moreover, results manifested that hidden agenda, the volume of fake information on digital media, massive unstructured data, the fast spread of fake news on digital media, and fake user accounts are prevalent challenges to construct authentic big data for detecting false online information on digital media platforms. Theoretically, the study has added valuable literature to the existing body of knowledge by exploring the relationship between big data analytics and context-based fake news on digital media in the data age. This intellectual piece also contributes socially by offering practical recommendations to control the cancer of fake news in society for stopping horrific perils; hence, it has a societal impact. Current research has practical applications for generators of digital media applications, policy-makers, decision-takers, government representatives, civil societies, higher education bodies, media workforce, educationists, and all other stakeholders. Recommendations offered in the paper are a roadmap for framing impactful policies to stay away from the harms of fake digital news.

AimPrevious research has focused on accuracy associated with real and fake news presented in the form of news headlines only, which does not capture the rich context news is frequently encountered in real life. Additionally, while previous studies on evaluation of real and fake news have mostly focused on characteristics of the evaluator (i.e., analytical reasoning), characteristics of the news stimuli (i.e., news source credibility) and the interplay between the two have been largely ignored. To address these research gaps, this project examined the role of analytical reasoning and news source credibility on evaluation of real and fake full-length news story articles. The project considered both accuracy and perceived credibility ratings as outcome variables, thus qualifying previous work focused solely on news detection accuracy.MethodWe conducted two independent but parallel studies, with Study 2 as a direct replication of Study 1, employing the same design but in a larger sample (Study 1: N = 292 vs. Study 2: N = 357). In both studies, participants viewed 12 full-length news articles (6 real, 6 fake), followed by prompts to evaluate each article’s veracity and credibility. Participants were randomly assigned to view articles with a credible or non-credible source and completed the Cognitive Reflection Test as well as short demographic questions.FindingsConsistent across both studies, higher analytical reasoning was associated with greater fake news accuracy, while analytical reasoning was not associated with real news accuracy. In addition, in both studies, higher analytical reasoning was associated with lower perceived credibility for fake news, while analytical reasoning was not associated with perceived credibility for real news. Furthermore, lower analytical reasoning was associated with greater accuracy for real (but not fake) news from credible compared to non-credible sources, with this effect only detected in Study 2.ConclusionsThe novel results generated in this research are discussed in light of classical vs. naturalistic accounts of decision-making as well as cognitive processes underlying news articles evaluation. The results extend previous findings that analytical reasoning contributes to fake news detection to full-length news articles. Furthermore, news-related cues such as the credibility of the news source systematically affected discrimination ability between real and fake news.

Consuming news from social media is becoming increasingly popular. However, social media also enables the wide dissemination of fake news. Because of the detrimental effects of fake news, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.In an attempt to understand the correlations between news propagation networks and fake news, first, we build hierarchical propagation networks for fake news and true news pieces; second, we perform a comparative analysis of the propagation network features from structural, temporal, and linguistic perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature importance analysis. We conduct extensive experiments on real-world datasets and demonstrate the proposed features can significantly outperform state-of-the-art fake news detection methods by at least 1.7% with an average F1>0.84. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.

Fake news of social media is growing rapidly. The exponential growth and clean get right of entry to of the facts available on social media networks has made it elaborate to distinguish among fake and real news. Detecting fake news is very important. To identify the fake news detection techniques are proposed in Machine Learning and Deep Learning. In this Recurrent Neural Network method is used to determine whether or not the information is actual or fake information. Fake news will mislead and create wrong perceptions among the people. This paper explores different textual properties which are used to distinguish between real and fake news. In this, datasets of fake and true news are used to train the model using proposed algorithm. The accuracy of the model will show the efficiency of the system

False information that appears similar to trustworthy media content, or what is commonly referred to as ‘fake news’, is pervasive in both traditional and digital strategic communication channels. This paper presents a comprehensive bibliographic analysis of published academic articles related to ‘fake news’ and the related concepts of truthiness, post-factuality, and deepfakes. Using the Web of Science database and VOSViewer software, papers published on these topics were extracted and analysed to identify and visualise key trends, influential authors, and journals focusing on these topics. Articles in our dataset tend to cite authors, papers, and journals that are also within the dataset, suggesting that the conversation surrounding ‘fake news’ is still relatively centralised. Based on our findings, this paper develops a conceptual ‘fake news’ framework—derived from variations of the intention to deceive and/or harm—classifying ‘fake news’ into four subtypes: mis-information, dis-information, mal-information, and non-information. We conclude that most existing studies of ‘fake news’ investigate mis-information and dis-information, thus we suggest further study of mal-information and non-information. This paper helps scholars, practitioners, and global policy makers who wish to understand the current state of the academic conversation related to ‘fake news’, and to determine important areas for further research.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

Social media is one of the major platforms to get news and information. However, it also provides convenience for widespread of fake news. The reason at the back of fake news is to create hype in order to get the audience's attention and build negative impact on society. The fake news detection is necessary to purify the Internet environment. Various machine learning based detection algorithms are designed to detect fake news. We use attention-based transformer model on publically available dataset for detection of fake and real news. This research aims to test and compare state-of-the-art algorithm and our proposed technique in detection of fake and real news. Our result shows that 15% of the accuracy in fake news detection is improved by transformer model as compare to Hybrid CNN.

Despite increased interests in the study of fake news, how to aid users' decision in handling suspicious or false information has not been well understood. To obtain a better understanding on the impact of warnings on individuals' fake news decisions, we conducted two online experiments, evaluating the effect of three warnings (i.e., one Fact-Checking and two Machine-Learning based) against a control condition, respectively. Each experiment consisted of three phases examining participants' recognition, detection, and sharing of fake news, respectively. In Experiment 1, relative to the control condition, participants' detection of both fake and real news was better when the Fact-Checking warning but not the two Machine-Learning warnings were presented with fake news. Post-session questionnaire results revealed that participants showed more trust for the Fact-Checking warning. In Experiment 2, we proposed a Machine-Learning-Graph warning that contains the detailed results of machine-learning based detection and removed the source within each news headline to test its impact on individuals' fake news detection with warnings. We did not replicate the effect of the Fact-Checking warning obtained in Experiment 1, but the Machine-Learning-Graph warning increased participants' sensitivity in differentiating fake news from real ones. Although the best performance was obtained with the Machine-Learning- Graph warning, participants trusted it less than the Fact-Checking warning. Therefore, our study results indicate that a transparent machine learning warning is critical to improving individuals' fake news detection but not necessarily increase their trust on the model.

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

The process of obtaining news from social media is like double edged weapon. On one hand, it is easy to access, less time consuming, user friendly, easily conveyable socially relevant news, possibility for obtaining various perspective of a single news and is being updated in every minute. On other hand, news is being manipulated by various networking sites based on private opinions or interest. Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organization. Due to the dissemination of fake news, there is need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. We can decide whether the news is solid or forged based on formerly witnessed fake or real news. We can use various models to access deceptive news in social media. Our contribution is bifold. First, we must introduce the datasets which contain both fake and real news and conduct various experiments to organize fake news detector. We use Natural Language Processing, Machine learning and deep learning techniques to classify the datasets. We yield a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques.

Recent studies have indicated that fake news is always produced to manipulate readers and that it spreads very fast and brings great damage to human society through social media. From the available literature, most studies focused on fake news detection and identification and fake news sentiment analysis using machine learning or deep learning techniques. However, relatively few researchers have paid attention to fake news analysis. This is especially true for fake political news. Unlike other published works which built fake news detection models from computer scientists’ viewpoints, this study aims to develop an effective method that combines natural language processing (NLP) and latent semantic analysis (LSA) using singular value decomposition (SVD) techniques to help social scientists to analyze fake news for discovering the exact elements. In addition, the authors analyze the characteristics of true news and fake news. A real case from the USA election campaign in 2016 is employed to demonstrate the effectiveness of our methods. The experimental results could give useful suggestions to future researchers to distinguish fake news. This study finds the five concepts extracted from LSA and that they are representative of political fake news during the election.

Not all fake news is semantically similar: Contextual semantic representation learning for multimodal fake news detection

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyse a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our main findings reveal that, compared with fake news, where users generate more negative sentiment, real news tends to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.