Political Fake News Detection Farmwork in Social Networks Utilizing Hybrid Deep Learning Algorithms

The speed at which false news spreads across online social media presents major threats to the authenticity of information and the trustworthiness of the public. As such, accurately detecting misinformation remains an ongoing challenge mainly due to the size and high-dimensionality of the textual data; the amount of redundant information found in all forms of texts; and the need for models to take into account semantic and contextual factors. In response to these challenges, this paper introduces a hybrid approach to detect false news based upon Grey Wolf Optimization (GWO); Convolutional Neural Networks (CNNs); and Long-Short Term Memory (LSTMs). Each component performs a unique function and adds to another component's performance. For example, GWO can be used as a meta-heuristic to select relevant features from large amounts of textual data and remove irrelevant or redundant information. Therefore, it can reduce the amount of computation required to train a model and improve its ability to generalize well. Once the relevant features have been identified through GWO, they are passed through CNNs that process the textual data hierarchically and capture local patterns within the textual data. Finally, LSTMs are applied to model sequential dependencies and long-range context within the data that was captured by CNNs. Experiments were performed on three datasets, including the BuzzFeed Political News Dataset, Random Political News Dataset, and the LIAR Benchmark Dataset. The experimental results showed that the proposed model has achieved better accuracy rates than other machine learning methods and optimized methods with accuracy rates of 89.2%, 94.8%, and 96.8% respectively.

Fake News, social media, LSTM, CNN, Meta-Heuristic, GWO

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