Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

FNDNet – A deep convolutional neural network for fake news detection

The explosion of social media allowed individuals to spread information without cost, with little investigation and fewer filters than before. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news, automatic detection techniques have been researched building on artificial intelligence and machine learning. The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. This work proposes a novel hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification. The model was successfully validated on two fake news datasets (ISO and FA-KES), achieving detection results that are significantly better than other non-hybrid baseline methods. Further experiments on the generalization of the proposed model across different datasets, had promising results.

Fake news detection: A hybrid CNN-RNN based deep learning approach

An ensemble machine learning approach through effective feature extraction to classify fake news

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.

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FakeBERT: Fake news detection in social media with a BERT-based deep learning approach.

Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.

Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques

*Correspondence Traore, Issa, ECE Department, University of Victoria, Victoria, BC, Canada. Email: itraore@ece.uvic.ca In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n-gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state-of-the-art methods.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/spy2.9

Detecting opinion spams and fake news using text classification

With the rapid growth of cloud computing and remote workforces, organizations are increasingly aware of the risk of data leaks and data exfiltration . Handling such risk has become more challenging because organizations today usually deal with big data. In particular, organizations must deal with massive amounts of unstructured data. As a result, modern data leak prevention (DLP) solutions must support automated methods to detect and identify confidential and sensitive information in both structured and unstructured data. In this paper, we demonstrate the benefits of using deep learning to identify unstructured context-dependent sensitive information, in contrast to traditional machine learning methods and rule-based methods.

Hadeer Ahmed

Papers

Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques

Detecting opinion spams and fake news using text classification

Automated detection of unstructured context-dependent sensitive information using deep learning