Dual emotion based fake news detection: A deep attention-weight update approach

Abstract

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.