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September 10, 2025

Prediction and optimization of the impact of digital contracts on Internet news communication based on deep reinforcement learning

QCQianwen CaoChina University of Petroleum, Beijing

Key Points

The proposed model increases detection accuracy on two datasets, showing an improvement of 11.09% on Pheme Twitter.
Incorporating an attention mechanism within the Deep Q-Network helps the model identify key text features effectively.
The DQN + Attention model outperforms complex models, indicating strong potential for simple yet effective rumor detection.
Findings suggest this approach can help social media platforms prevent rumor spread and maintain public stability.

Abstract

With the rapid development of Internet technology, the digital contract is a vital medium of information dissemination. The rapid spread of rumors in Internet news communication misleads public cognition and may cause social panic and instability. This study aims to explore a deep reinforcement learning (DRL) model for rumor detection in news communication that integrates an attention mechanism. By enriching and advancing the theoretical framework and technical methods in the field of rumor detection, it seeks to provide more accurate and reliable information support for Internet news communication. This study first analyzes the public opinion transmission cycle of Internet news. It makes it clear that the word vector model is the key link in the natural language processing task of news rumor detection. In the task of rumor detection, a Deep Q-Network (DQN) detection model is further proposed, which takes text information as input, extracts features, and learns optimal strategies through the deep neural network, to achieve accurate recognition of rumors. The attention mechanism is integrated into the model to help it better identify the key information in the text, such as keywords, key sentences, etc., thereby improving the accuracy of detection. On the Pheme Twitter and Weibo datasets, the DQN + Attention model proposed in this study has improved accuracy by 11.09% and 10.1% compared to the Long Short-Term Memory model. Compared with the Relay Diffusion Model, the proposed model’s accuracy on the two datasets is increased by 2.5% and 0.2%, respectively. This finding demonstrates that the model can still achieve or even surpass the effectiveness of complex models that combine multiple types of information even when using only textual information. The research results have vital practical value for the field of rumor detection. They not only assist in accurately and timely identifying rumors but also provide effective rumor prevention and control measures for social media platforms.

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