Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using ELECTRA model, which achieved 85.11% accuracy score, is thus reported in this manuscript. In addition to that, a new novel dataset called COVAX-Reality containing COVID-19 vaccinerelated news has been contributed. Using the COVAX-Reality dataset, the performance of FNEC is compared to several traditional learning models i.e., Support Vector Machine (SVM), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) and Bi-directional Encoder Representations from Transformers (BERT). For the evaluation of FNEC, standard metrics (Precision, Recall, Accuracy, and F1-Score) were utilized.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

The role of personality traits and the ability to detect fake news in predicting information avoidance during the COVID-19 pandemic

PurposeThe objective of this study was to investigate the impacts of personality traits and the ability to detect fake news on information avoidance behavior. It also examined the effect of personality traits on the ability to detect fake news.Design/methodology/approachThe sample population included Shiraz University students who were studying in the second semester of academic year 2021 in different academic levels. It consisted of 242 students of Shiraz University. The Big Five theory was used as the theoretical background of the study. Moreover, the research instrument was an electronic questionnaire consisting of the three questionnaires of the ability to detect fake news (Esmaeili et al., 2019, inspired by IFLA, 2017), the Big Five personality traits (Goldberg, 1999) and information avoidance (Howell and Shepperd, 2016). The statistical methods used to analyze the data were Pearson correlation and stepwise regression, which were performed through SPSS software (version 26).FindingsThe results showed that from among the five main personality factors, only neuroticism had a positive and significant effect on information avoidance. In addition, the ability to detect fake news had a significant negative effect on information avoidance behavior. Further analyses also showed positive and significant effects of openness to experience and extraversion on the ability to detect fake news. In fact, the former had more predictive power.Practical implicationsFollowing the Big Five theory considering COVID-19 information avoidance and the ability to detect COVID-19 fake news, this study shifted the focus from environmental factors to personality factors and personality traits. Furthermore, this study introduced the ability to detect fake news as an influential factor in health information avoidance behaviors, which can be a prelude for new research studies.Originality/valueThe present study applied the five main personality factors theory in the context of information avoidance behavior and the ability to detect fake news, and supported the effect of personality traits on these variables.

A novel hybrid multi-thread metaheuristic approach for fake news detection in social media.

In fake news detection, intelligent optimization seems to be a more effective and explainable solution methodology than the black-box methods that have been extensively used in the literature. This study takes the optimization-based method one step further and proposes a novel, multi-thread hybrid metaheuristic approach for fake news detection in social media. The most innovative feature of the proposed method is that it uses a supervisor thread mechanism, which simultaneously monitors and improves the performance and search patterns of metaheuristic algorithms running parallel. With the supervisor thread mechanism, it is possible to analyse different key attribute combinations in the search space. In addition, this study develops a software framework that allows this model to be implemented easily. It tests the performance of the proposed model on three different data sets, respectively containing news about Covid-19, the Syrian War, and daily politics. The proposed method is evaluated in comparison to the results of fifteen different well-known deep models and classification algorithms. Experimental results prove the success of the proposed model and that it can produce competitive results.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively. © 2023 Tech Science Press. All rights reserved.

Fake news detection using social media data for Khasi language

With the advancement in technology, web technol-ogy in the form of social media is one of the main origins of information worldwide. Web technology has helped people to enhance their ability to know, learn, and gain knowledge about things around them. The benefits that technological advancement offers are boundless. However, apart from these, social media also has major issues related to problems and challenges concerning filtering out the right information from the wrong ones. The sources of information become highly unreliable at times, and it is difficult to differentiate and decipher real news or real information from fake ones. Cybercrime, through fraud mechanisms, is a pervasive menace permeating media technology every single day. Hence, this article reports an attempt to fake news detection in Khasi social media data. To execute this work, the data analyzed are extracted from different Internet platforms mainly from social media articles and posts. The dataset consists of fake news and also real news based on COVID-19, and also other forms of wrong information disseminated throughout the pandemic period. We have manually annotated the assembled Khasi news and the data set consists of 116 news data. We have used three machine learning techniques in our experiment, the Decision Tree, the Logistic Regression, and the Random Forest approach. We have observed in the experimental results that the Decision Tree-based approach yielded accurate results with an accuracy of 87%, whereas the Logistic Regression approach yielded an accuracy of 82% and the Random Forest approach yielded an accuracy of 75%. © 2023 IEEE.

COVID-19 Fake News Detector

COVID-19 pandemic has been impacting people's everyday life for more than two years. With the fast spreading of online communication and social media platforms, the number of fake news related to COVID-19 is in a rapid growth and propagates misleading information to the public. To tackle this challenge and stop the spreading of fake news, this project proposes to build an online software detector specifically for COVID-19 news to classify whether the news is trustworthy. Specifically, as it is difficult to train a generic model for all domains, a base model is developed and fine-tuned to adapt the specific domain context. In addition, a data collection mechanism is developed to get latest COVID-19 news data and to keep the model fresh. We then conducted performance comparisons among different models using traditional machine learning techniques, ensemble machine learning, and the state-of-the-art deep learning mechanism. The most effective model is deployed to our online website for COVID-19 related fake news detection. © 2023 IEEE.

Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

Multiverse: Multilingual Evidence for Fake News Detection.

The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. In this work, we propose Multiverse-a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. Our hypothesis that cross-lingual evidence can be used as a feature for fake news detection is supported by manual experiments based on a set of true (legit) and fake news. Furthermore, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset, showing that (in combination with linguistic features) it yields significant improvements over the baseline models, bringing additional useful signals to the classifier.

COVID-19 Fake News and Misinformation Detection using Transformer Learning

As COVID-19 spreads globally and generates an unprecedented pandemic, COVID-19 fake news is born and quickly disseminated on the internet. Misinformation and disinformation of COVID-19 can distort public perception of the virus and have a serious negative influence on society. To increase vaccine coverage rates and achieve herd immunity, eliminating fake news becomes an urgent need worldwide. Our research aims at using the Transformer model to implement COVID-19 fake news detection. We use the dataset of COVID-19 fake news, extract features through the embedding method of one hot representation, and construct the transformer model to implement text classification on the binary problem. Then we analyze results through loss curve and confusion matrix and show performance parameters, including accuracy, AUC score, and F1 score. We conclude that the model can achieve an accuracy of 72% for COVID-19 fake news detection. This research provides insight for transformer learning dealing with fake news detection of COVID-19. © 2022 IEEE.

Detecting Fake News about COVID-19 Infodemic Using Deep Learning and Content Analysis

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information―both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study. © 2022,Asia Pacific Journal of Information Systems.All Rights Reserved.

CoviFake: A Framework to Detect and Analyze Fake COVID19 Tweets

Along with the unprecedented impact of the COVID-19 pandemic on human lives, a new crisis of fake and false information related to disease has also emerged. Primarily, social media platforms such as Twitter are used to disseminate fake information due to ease of access and their large audience. However, automatic detection and classification of fake tweets is challenging task due to the complexity and lack of contextual features of short text. This paper proposes a novel CoviFake framework to classify and analyze fake tweets related to COVID-19 using vocabulary and non-vocabulary features. For this purpose, first, we combine and enhance 'CTF' and 'COVID19 Rumor' datasets to build our COVID19-sham dataset containing 25,388 labelled tweets. Next, we extract the vocabulary and 12 non-vocabulary features to compare the performance of six state-of-the-art machine learning classifiers. Our results highlight that the Random Forest (RF) classifier achieves the highest accuracy of 94.53% with the combination of top 2,000 vocabulary and 12 non-vocabulary features. In addition, we developed a large-scale dataset of CoviTweets containing 7.88 million English tweets posted by 3.8 million users during two months (March-April, 2020). The analysis of CoviTweets leveraging our framework reveals that the dataset contains 1.64 million (20.87%) fake tweets. Furthermore, we perform an in-depth examination by assigning a 'fakeness score' to hashtags and users in CoviTweets. © 2022 IEEE.

Fake news detection: deep semantic representation with enhanced feature engineering.

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).

Fake news detection: A survey of graph neural network methods.

The emergence of various social networks has generated vast volumes of data. Efficient methods for capturing, distinguishing, and filtering real and fake news are becoming increasingly important, especially after the outbreak of the COVID-19 pandemic. This study conducts a multiaspect and systematic review of the current state and challenges of graph neural networks (GNNs) for fake news detection systems and outlines a comprehensive approach to implementing fake news detection systems using GNNs. Furthermore, advanced GNN-based techniques for implementing pragmatic fake news detection systems are discussed from multiple perspectives. First, we introduce the background and overview related to fake news, fake news detection, and GNNs. Second, we provide a GNN taxonomy-based fake news detection taxonomy and review and highlight models in categories. Subsequently, we compare critical ideas, advantages, and disadvantages of the methods in categories. Next, we discuss the possible challenges of fake news detection and GNNs. Finally, we present several open issues in this area and discuss potential directions for future research. We believe that this review can be utilized by systems practitioners and newcomers in surmounting current impediments and navigating future situations by deploying a fake news detection system using GNNs.

Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.

Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.

Search queries related to COVID-19 based on keyword extraction.

Background: : Pandemic COVID-19 caused an infodemic - massive spread of true and fake information about novel coronavirus. This study aims to present the possibility of using Keyword Extraction as a tool to obtain the most trending search queries related to COVID-19 and analyze the possibility of including their search volume in models for the prediction of fake news. Methods: : The study used Python implementation of the machine learning-based technique KeyBERT to extract keywords from true and fake news. These keywords were used in the next step to obtain related search queries with Google Trends API. Results: : Non-parametric Spearman Rank Order Correlation was identified as a statistically positive correlation (p < 0.001) between the occurrence of false news and top query / rising query metrics provided by Google Trends of queries related to extracted keywords pandemic, HIV, lockdown, plague, Michigan, and protest, which proves that search volume can identify fake news. Conclusions: : Experiments done in this research proved that Keyword Extraction from false news is useful for obtaining related search queries and the top query and rising query metrics can be used to increase the accuracy of fake news prediction models.

Deep transfer learning for COVID-19 fake news detection in Persian.

The spread of fake news on social media has increased dramatically in recent years. Hence, fake news detection systems have received researchers' attention globally. During the COVID-19 outbreak in 2019 and the worldwide epidemic, the importance of this issue becomes more apparent. Due to the importance of the issue, a large number of researchers have begun to collect English datasets and to study COVID-19 fake news detection. However, there are a large number of low-resource languages, including Persian, that cannot develop accurate tools for automatic COVID-19 fake news detection due to the lack of annotated data for the task. In this article, we aim to develop a corpus for Persian in the domain of COVID-19 where the fake news is annotated and to provide a model for detecting Persian COVID-19 fake news. With the impressive advancement of multilingual pre-trained language models, the idea of cross-lingual transfer learning can be proposed to improve the generalization of models trained with low-resource language datasets. Accordingly, we use the state-of-the-art deep cross-lingual contextualized language model, XLM-RoBERTa, and the parallel convolutional neural networks to detect Persian COVID-19 fake news. Moreover, we use the idea of knowledge transferring across-domains to improve the results by using both the English COVID-19 dataset and the general domain Persian fake news dataset. The combination of both cross-lingual and cross-domain transfer learning has outperformed the models and it has beaten the baseline by 2.39% significantly.

Fake News Detection on COVID 19 tweets via Supervised Learning Approach

In December 2019, the first cases of an infection caused by the virus called Covid19 were recorded in the Chinese city of Wuhan. As the months passed, this virus gave rise to a global pandemic that has not yet been eradicated. The COVID19 information disseminated on digital platforms has very different contents, which makes it difficult to recognize whether the published news is true or false, as well as the sentiments associated with it. Therefore, the hypothesis that feelings about COVID19 may differ between Fake news and Real news is considered. The aim of the present study is to support the identification of real tweets from fake ones and to compare the sentiments that users express in them. To achieve this goal, two different datasets obtained from the English version of the social network Twitter were used: the first dataset was downloaded from 'Kaggle' and relates to the year 2021, while the second dataset is more recent and was obtained via 'Python'. Supervised Learning techniques were applied to the dataset downloaded from 'Kaggle', also highlighting variables not recognizable at first glance (metadata) and associating the sensations manifested in the publications. From the analysis of the first d ataset, we derived an algorithm, which we subsequently applied to the second dataset for news recognition. The performances obtained are of interest and show the change and trend of emotions and feelings conveyed by the tweets. © 2022 IEEE.

Towards a soft three-level voting model (Soft T-LVM) for fake news detection.

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.