Optimization Strategies in Deep Learning Method ADO for Fake News Detection on Social Media

The emergence of social media as a primary platform for news consumption has created a complex landscape, marked by accessibility and rapid dissemination alongside the proliferation of misinformation. This dual nature underscores the pivotal necessity for effective strategies in detecting false information to mitigate its detrimental impacts on society. In response to this critical need, this paper introduces a novel methodology, CNN-LSTM-ADO, which amalgamates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Dingo Optimization (DOX) and Adam Optimization. Here, we introduce the CNN-LSTM-ADO framework tailored for identifying fake news. This framework utilizes the Dingo optimizer (DOX) and Adam optimizer to train the CNN-LSTM model. By consolidating complementary strengths of these techniques, CNN-LSTM-ADO endeavours to augment the precision and efficiency of fake news detection across social media platforms