Accountable Proxy Re-Encryption for Secure Data Sharing

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.

Staying up-to-date on news through social media has both advantages and disadvantages. Social media platforms are excellent sources of information, but they also contribute significantly to the spread of fake news (incorrect or poor-quality content). The widespread circulation of such information can profoundly impact society by shaping public opinion, influencing policy debates, and affecting decision-making processes. As a result, fake news detection has become a critical area of research, particularly in multilingual and multicultural contexts. Detecting fake news in Arabic presents unique challenges due to the language’s complexity, diverse dialects, and the limited availability of comprehensive annotated datasets. Many existing detection models are vulnerable to misleading or context-heavy information because they rely primarily on basic content features. This study evaluates the effectiveness of various machine learning and deep learning techniques—including random forest (RF), naive Bayes (NB), feedforward neural networks (FNN), long short-term memory (LSTM), Arabic bidirectional encoder representations from Transformers (BERT), and a hybrid AraBERT + bidirectional long short-term memory (BiLSTM) model—in identifying fake news in Arabic media. Experiments were conducted using the Arabic Fake News Dataset (AFND), which contains over 606,000 news items labeled according to credibility. The hybrid AraBERT + Bi-LSTM model outperformed all others, achieving an average F1-score of 98.0%. This model integrates transformer-based contextual embeddings with sequential modeling.

With the exponential growth of user-generated content on social media platforms, the spread of fake news has become a critical challenge, undermining public trust and influencing societal behavior. This research focuses on fake news detection and knowledge verification using Deep Attention Networks, which can capture fine-grained semantic relationships and contextual dependencies in textual data. To achieve realistic and comprehensive evaluation, datasets are collected from diverse social media platforms including Twitter, Facebook, and Reddit, encompassing both verified and misleading content. The proposed methodology involves the implementation of a Deep Attentionbased model and its hierarchical comparison with existing state-of-the-art models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Transformerbased architectures (e.g., BERT). Each model is evaluated using multiple performance metrics -accuracy, precision, recall, F1-score, and AUC - under various conditions tailored to different social media dynamics. A detailed analysis is presented through graphical representations such as bar charts, ROC curves, and confusion matrices, offering a visual and quantitative understanding of model effectiveness. The results consistently show that the Deep Attention Network provides superior performance in identifying and verifying fake news, especially in linguistically complex and context-rich scenarios. This research underscores the significance of attention mechanisms in enhancing the reliability and interpretability of fake news detection systems and lays the foundation for more robust content verification strategies on digital platforms.

The viral spread of fake news on social media platforms has become a significant concern. Detecting and mitigating the spread of fake news pose a major challenge, requiring extensive effort from fact-checkers, governments, and organizations. However, the speed and volume of disinformation on social media reach far beyond the capacity of manual efforts to prebunk it. In recent years, various automatic fake news detection mechanisms have been proposed, ranging from models focusing on linguistic cues to approaches analyzing network characteristics. While these methods produce successful results, ever-evolving techniques of deception often outpace them. AI-based approaches offer a new opportunity in the subject matter. Especially deep learning-based detection systems provide substantial new ways to understand the context while adapting well to the evolving patterns of fake news. In this study, we explore the evolution of fake news detection. We discuss conventional and automatic fake news detection methods and the state-of-the-art deep learning approaches.

'Cure or Poison?' Identity Verification and the Spread of Fake News on Social Media

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Fake news is the kind of news that is made deliberately to deceive the readers. It is a sort of purposeful publicity which is distributed as veritable information. Fake news is spread out through traditional news media and social media. Fake news has been an issue for quite a while. With the introduction of social media, the spread of fake news is increased, and it got hard to separate between true news and fake news. The spread of fake news involves worry as it manipulates public opinions. The widespread of fake news can adversely affect people and society in general. The problem of fake news has increased significantly in recent years. The scope and impact of social media have shifted dramatically. On the one hand, it is low cost and simple accessibility, as well as the ability to quickly exchange information attract people to read news from it. On the other hand, it empowers widespread fake news, which is only false data to deceive people. A stand-alone bidirectional encoder representations from transformers model is used in this paper for fake news detection. We try to pose the problem as a text classification problem and build a deep learning model for achieving the objective. We used the BERT model to develop fake news or real news classification solution for our solution in this model. We achieved more than 95% accuracy on the test set and a remarkable by the area under the curve (AUC) by a stand-alone BERT model.KeywordsNews detectionBERTDeep learningMachine learningSocial mediaTransformerNLP

The blistering development of digital journalism and social media has contributed to the proliferation of misinformation and fake news [1], which affects the overall discourse, ruins the reputations of individuals and even can affect the election results [3]. Manual methods of fact checking are not scalable and are also time consuming and traditional [4]. To solve this, we suggest INFACT (Intelligent Fake News and Sentiment Analysis Framework), a modular system where machine learning is applied [6], and natural language processing,sentiment analysis [5], and credibility scoring to detect, classify, and analyze fake news in real time. The framework integrates six interconnected modules: web scraping, fake news detection, multimodal analysis, sentiment analysis, credibility scoring, and trend tracking. Implemented using Python, Django, and scikit learn, the system aims to provide users with reliable classification, interpretability through sentiment and credibility scores, and visual insights into misinformation trends.

Fake news detection within online social media using supervised artificial intelligence algorithms

Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).

The rise of social media has provided a means for the spread of fake news, factual false information that is used to further a financial, political or societal agenda. The response to fake news on social media can be from individual users, social media platform companies, or respective national governments. This systematic literature review was designed to determine whether there is commonality across cultures and nations as to fake news exposure, response to encountering fake news on social media, and attitudes towards mediating efforts. A total of five published journal articles were selected, each using quantitative methods and representing five distinct geographical areas. These articles formed the bases for a systematic literature review that answered the research questions concerned with attitudes towards the threat of fake news, responses to fake news encountered on social media platforms, and attitudes towards efforts to curb fake news, whether by individuals, social media platform companies, or governments. Results showed universal recognition of fake news as a potential or real threat, mixed results as to whether fake news is read or shared by respondents, and mixed results regarding which actor (people, companies or governments) should lead mitigation efforts. These results emphasize the cultural and societal differences that must be considered when investigating fake news, particularly with the intention to develop recommendations or guidelines for its reduction.

Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility

This paper is aimed to use a quantitative and visual method to evaluate the history, current and future of publications regarding fake news and rumors on social media. VOSviewer was used to identify and summarize the publications from Scopus and WoS from 2010 to 2020. Publication source, publication organization, authors, country, citation of articles, citation of country and organization were recorded and analyzed. Bibliometric maps of authorship, citation, co-citation and network of co-occurrence of keywords were drawn. 6354 articles and 33503 cited references were analyzed. The United States dominates the publications (1615, 25.4%) and citations (5867). University of Chinese Academic of Sciences (85) is the most productive organization. Loftus (26) is the most productive author. Memory & Cognition published the most papers about fake news and rumors. The latest keyword “machine learning” appeared in 2018 in 182 papers. Other relatively new keywords include “deep learning” “natural language processing” “social media platform” “fake news detection” “rumors detection” which appeared in 2018 in 98, 66, 63, 58, and 52 papers respectively. As for researchers and practitioners, this paper suggests an analysis of integrated visualization in terms of knowledge and innovation based on the area of fake news and rumors.

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

In this manuscript, the rise of misinformation and harmful behavior on social media platforms has made it crucial to develop systems that ensure the authenticity of shared content and user accounts is presented. Parsing of social media feeds for forensic investigation aims to identify fake news and detect fake social media accounts (focusing on platforms like Instagram). The main goal of parsing of social media feeds for forensic investigation is to protect users from misleading information spread by fake accounts. To achieve this, various modules are integrated into the system, including Data Collection and Pre-Processing, Fake News Detection and Account Authenticity Verification. Web scraping tools such as BeautifulSoup are used to gather real-time data, while machine learning algorithms, including Random Forest and Large Language Model (LLM) are employed to classify fake news and detect suspicious account behaviors. The system also uses NLP models to identify fake news patterns and generate comprehensive reports summarizing the findings of each module. By combining Natural Language Processing (NLP) and machine learning techniques, parsing of social media feeds for forensic investigation provides an essential platform for users to protect themselves from misleading information on social networks.