Strategies for Countering False Information in News Dissemination Using Deep Learning Recommendation Algorithms

As the problem of "fake news" becomes increasingly serious, there are still issues to be addressed in terms of fake news detection accuracy, system response time and recall rate. This study aims to optimize the fake news detection model, improve recognition performance and achieve accurate news recommendation. The research methods include data collection and preprocessing, deep learning model design and recommendation strategy formulation. First, false and real news samples are collected from news websites and social media platforms for labeling, cleaning, deduplication and feature extraction. Secondly, a model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is designed. Text features are extracted through TF-IDF and word embedding technology, and a multi-task learning strategy is introduced to optimize fake news detection and content recommendation. The loss function is the weighted sum of classification loss and recommendation loss. In the recommendation strategy, reinforcement learning is used in combination with user feedback to dynamically adjust the recommended content and reduce the spread of misleading information. Experimental results show that the performance of the optimized model is significantly improved: the accuracy is increased by an average of 6.26%, reaching a maximum of 89.7%; the system response time is shortened from 5-6 seconds to 3-4 seconds, an average of 1.9 seconds; the recall rate is increased from 72.8%-79.1% to 85.4%-89.1%, with the maximum increase of 11.6%. The conclusion shows that through data expansion, feature engineering and model optimization, the performance of fake news detection has been significantly improved, and the misjudgment and missed detection rates have been significantly reduced. At the same time, combined with precise recommendation strategies, the user experience has been effectively improved, providing technical support and practical solutions for false information governance.