Search Results (140)

In recent years, the number of fake news stories has significantly increased in the world of media, especially with the widespread use of social media. It has impacted several industries, including tourism. From a tourism point of view, the spread of fake news can contribute to the reduction of the popularity of a destination. It may influence travel decisions by discouraging tourists from visiting certain places and thus damage the reputation of the destination, contributing to economic loss. After a literature review on the communication aspect of fake news and a general introduction of fake news in the tourism and hospitality industry, we conducted a systematic literature review (SLR), a research methodology to collect, identify, and analyse available research studies through a systematic procedure. The current SLR is based on the Scopus, Web of Science, and Google Scholar databases of existing literature on the topic of fake news in the tourism and hospitality industry. The study identifies, lists, and examines existing papers and conference proceedings from a vast array of disciplines, in order to give a well-rounded view on the issue of fake news in the tourism and hospitality industry. After selecting a total of 54 previous studies from more than 20 thousand results for the keywords ‘fake news’ and ‘tourism,’ we have analysed 39 papers in total. The SLR aimed to highlight existing gaps in the literature and areas that may require further exploration in future primary research. We have found that there is relatively limited academic literature available on the subject of fake news affecting tourism destinations, compared to studies focused on hospitality services. Full article

Fake news is false or misleading information that looks like real news and spreads through traditional and social media. It has a big impact on our social lives, especially in politics. In Pakistan, where Urdu is the main language, finding fake news in Urdu is difficult because there are not many effective systems for this. This study aims to solve this problem by creating a detailed process and training models using machine learning, deep learning, and large language models (LLMs). The research uses methods that look at the features of documents and classes to detect fake news in Urdu. Different models were tested, including machine learning models like Naïve Bayes and Support Vector Machine (SVM), as well as deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), which used embedding techniques. The study also used advanced models like BERT and GPT to improve the detection process. These models were first evaluated on the Bend-the-Truth dataset, where CNN achieved an F1 score of 72%, Naïve Bayes scored 78%, and the BERT Transformer achieved the highest F1 score of 79% on Bend the Truth dataset. To further validate the approach, the models were tested on a more diverse dataset, Ax-to-Grind, where both SVM and LSTM achieved an F1 score of 89%, while BERT outperformed them with an F1 score of 93%. Full article

The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inputs for retrieving relevant text from images and determining a confidence score that suggests the probability of the news being authentic. We report extensive experimental results on the ISOT, WELFAKE, TRUTHSEEKER, and ISOT_WELFAKE_TRUTHSEEKER datasets. Our proposed model demonstrates better generalization on the TRUTHSEEKER dataset with an accuracy of 99.97%, achieving substantial improvements over existing methods with an F1-score of 0.98. Experimental results indicate a potential accuracy increment of +3.35% compared to the latest baselines. These results highlight the potential of our approach to serve as a strong resource for automatic fake news detection by effectively integrating both text and visual data streams. Findings suggest that using diverse datasets enhances the resilience of detection systems against misinformation strategies. Full article

In an age of AI-generated content and deepfakes, fake news and disinformation are increasingly spread using manipulated or fabricated images. To address this challenge, we introduce Post-Quantum VerITAS, a cryptographic framework for verifying the authenticity and history of digital images—even in a future where quantum computers threaten classical encryption. Our system supports common image edits, like cropping or resizing, while proving that the image is derived from a legitimate, signed source. Using quantum-resistant tools, like lattice-based hashing, modified Poseidon functions, and zk-SNARK proofs, we ensure fast, privacy-preserving verification without relying on trusted third parties. Post-Quantum VerITAS offers a scalable, post-quantum-ready solution for image integrity, with direct applications in journalism, social media, and secure digital communication. Full article

The spread of fake news threatens trust in both traditional and digital media. Early detection methods, based on linguistic patterns and handcrafted features, struggle to identify more sophisticated misinformation. Large language models (LLMs) offer promising solutions by capturing complex text patterns, but challenges remain in ensuring their accuracy and generalizability. This study evaluates LLM-based feature extraction for fake news detection across multiple datasets. We compare BERT-based text representations, introduce a method for extracting factual segments from news articles, and create two new datasets with fact-based features. Additionally, we explore graph-based text representations using LLMs to capture relationships within news content. By integrating these approaches, we improve fake news detection, making it more accurate and interpretable. Our findings provide insights into how LLMs and graph-based techniques can enhance misinformation detection. Full article

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Research on misinformation spans multiple disciplines, but technical surveys rarely focus on the medical domain. Existing medical misinformation surveys provide broad insights for various stakeholders but lack a deep dive into computational methods. This survey fills that gap by examining how fact-checking and fake news detection techniques are adapted to the medical field from a computer engineering perspective. Specifically, we first present manual and automatic approaches for fact-checking, along with publicly available fact-checking tools. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation. While this survey primarily serves researchers and technology experts, it can also provide valuable insights for policymakers working to combat health misinformation. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation. Full article

The open society is under threat from populism and fake news. But what do we mean by the ‘open society’? The notion was made popular by Bergson and Popper. Under the impact of totalitarianism, Popper distinguished open from closed societies. They differ by their degree of institutionalized critical scrutiny of political and societal practices. Modern sociological theory uses the notions of differentiation (or complexity) and reflexivity to distinguish these types of society (Habermas, Giddens). Reflexivity goes beyond critical scrutiny; it describes the constant examination and revision of social practices in the light of incoming information. An evaluation of these criteria shows that a necessary and sufficient condition for the distinction between open and closed societies is the degree of institutionalized critical scrutiny (contestability) and, even more, reflexivity. Openness is not a function of the complexity of societal development. It is a function of appropriate political structures. Therein lies its deeper connection with democracy: drawing upon several historical and contemporary examples thisarticle suggests that open societies can be characterized by critical scrutiny and even more reflexivity. In the final section, this article analyses the malaise of modern democracies with respect to the risks posed by populism and disinformation through social media. But rather than focusing on immigration or the economy, it considers the risks in terms of the erosion of institutional trust. Institutional trust is one of the civic virtues which the Enlightenment regarded as an essential feature of a democratic society. I conclude that populism and the deliberate spread of false information undermine civic virtues; a return to civic virtues is an important feature of the survival of democracy as an open society. Full article

Fake news has become a significant challenge on online social platforms, increasing uncertainty and unwanted tension in society. The negative impact of fake news on political processes, public health, and social harmony underscores the urgency of developing more effective detection systems. Existing methods for fake news detection often focus solely on one platform, potentially missing important clues that arise from multiple platforms. Another important consideration is that the domain of fake news changes rapidly, making cross-domain analysis more difficult than in-domain analysis. To address both of these limitations, our method takes evidence from multiple social media platforms, enhances our cross-domain analysis, and improves overall detection accuracy. Our method employs the Dempster–Shafer combination rule for aggregating probabilities for comments being fake from two different social media platforms. Instead of directly using the comments as features, our approach improves fake news detection by examining the relationships and calculating correlations among comments from different platforms. This provides a more comprehensive view of how fake news spreads and how users respond to it. Most importantly, our study reveals that true news is typically rich in content, while fake news tends to generate a vast thread of comments. Therefore, we propose a combined method that merges content- and comment-based approaches, allowing our model to identify fake news with greater accuracy and showing an overall improvement of 7% over previous methods. Full article

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. Full article

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. Full article

In a hyperproductive interactive environment, where speed and cost-effectiveness often overshadow accuracy, the media’s role is increasingly shifting towards an educational function, beyond its traditional informative and entertaining roles. This shift, particularly through the promotion of science and education, aims to bridge the gap between educational institutions and the labor market. In this context, the importance of 21st-century competencies—encompassing a broad range of knowledge and skills—becomes increasingly clear. Educational institutions are now expected to equip students with relevant, universally applicable, and market-competitive competencies. This paper proposes using a combination of principal component analysis (PCA) and fuzzy analytic hierarchy process (FAHP) to rank 21st-century competencies developed throughout the educational process to improve the system. The highest-ranked competency identified is the ability to manage information—specifically, gathering and analyzing information from diverse sources. It has been shown that respondents who developed “soft skills” and media literacy during their studies are better able to critically assess content on social networks and distinguish between credible and false information. The significance of this work lies in its focus on the damaged credibility of online media caused by user-generated content and the rapid spread of unverified and fake news. Denying such discourse or erasing digital traces is therefore futile. Developing a critical approach to information is essential for consistently identifying fake news, doctored images, and recordings taken out of context, as well as preventing their spread. Full article

This study investigates the impact of candidates disseminating fake news on voter behavior and electoral outcomes in highly competitive, partisan races. While the effects of fake news on electoral outcomes have been studied, research has yet to examine the impact of candidates’ strategic use of fake news in elections where it may have the greatest impact—close races. This research explores whether the use of fake news influences voter support, particularly among independent voters, in tightly contested elections. Through a conjoint survey experiment involving participants from Amazon MTurk, this study analyzes how variables such as race competitiveness, perceived risk of alienating independents, and the presence of partisan labels affect voter responses to candidates who spread misinformation. The findings indicate that while the competitiveness of a race does not significantly enhance support for candidates sharing fake news, the presence of partisan labels does. These results suggest that voter behavior in response to fake news is more closely tied to partisan identity than to strategic electoral considerations. This study highlights the complex dynamics of misinformation in electoral contexts and its implications for democratic processes. Full article

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score. Full article

Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models. Full article

The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text. Through a series of experiments on diverse datasets, applying transfer and incremental learning techniques, we demonstrate the effectiveness of our hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Furthermore, our findings emphasize the importance of dataset alignment and balance in transfer learning, as well as the utility of incremental learning in maintaining high detection performance while reducing runtime. This research offers promising avenues for further advancements in fake news detection methodologies, with implications for future research and development in this critical domain. Full article