Analysis of the Influence of Algorithm Recommendation on the Dissemination Effect of Anti-Fraud Information in Social Networks
Yinan Deng, Zhao Mengqi · Journal of big data and computing. · 2025
Telecommunication network fraud is a pressing societal issue, and social network platforms play a vital role in disseminating anti-fraud information. This study examines the impact of algorithmic recommendation systems on anti-fraud information dissemination, focusing on their potential to improve information coverage, user engagement, and educational effectiveness, while addressing their limitations. By combining theoretical analysis with empirical data, the research evaluates how algorithmic recommendations enhance information visibility and relevance through personalized data-push, while also identifying risks such as algorithmic opacity and misinformation diffusion. The findings indicate that algorithmic recommendations significantly improve the reach and accuracy of anti-fraud information but may inadvertently spread false information due to inherent biases. The study concludes that, despite these challenges, optimizing algorithms can effectively enhance anti-fraud information dissemination, thereby raising public awareness and personal protection capabilities. This research provides insights into the role of algorithms in social networks and suggests the need for improved regulatory and technical standards to ensure the ethical use of technology in combating fraud.