Neural network-based approaches widely use Word2vec and GloVe word embedding models for context-free word embedding. Word embeddings are low-dimensional with distributed feature representations suited for natural languages. Compared to traditional ML models, models based on neural networks achieved extraordinary success on many tasks involving natural language thanks to the use of word embeddings.
These approaches inevitably produced high-dimensional interpretations of language processing, giving rise to the curse of dimensionality. Initially, Natural Language Processing (NLP) issues were handled using traditional Machine Learning (ML) methods such as Logistic Regression and Support Vector Machine (SVM) with hand-crafted features. Numerous approaches for automatically detecting the authenticity of news have been developed.
As a result, one of the rising study topics is automatic false news identification. Moreover, accuracy, precision, recall, and f1-score performance metrics are utilized to evaluate the proposed strategy and demonstrate that a balanced dataset significantly affects classification performance.įalse information spreads quickly on social media in the modern era, raising societal concerns about people’s ability to differentiate between what is phony and what is genuine while browsing and actively using social media. The proposed model outperforms the existing models with an accuracy of 92.45%. The proposed strategy is evaluated with twelve different state-of-the-art models. The proposed approach overcomes the issue of minority class and performs the classification with the AugFake-BERT model (trained with an augmented dataset). Thus, we introduce a text augmentation technique with a Bidirectional Encoder Representation of Transformers (BERT) language model to generate an augmented dataset composed of synthetic fake data.
#FAKE DATA GENERATOR MANUAL#
Additionally, manual labeling of fake news data is time-consuming, though we have enough fake news traversing the internet. However, when the dataset is biased, these models perform poorly.
The present fake news detection system performs satisfactorily on well-balanced data. Fake news detection techniques are a topic of interest due to the vast abundance of fake news data accessible via social media.
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