An Interactive Information Exchange Method for Campus Social Networks

With the widespread use of campus social network platforms, a large number of bot accounts on these platforms may engage in interactions or disseminate false information through automated means, affecting students' academic and life decisions and disrupting normal campus order. Therefore, how to promptly identify bot accounts on campus social network interaction platforms and take corresponding measures has become an urgent problem to be solved. This paper proposes a campus social network interaction information exchange platform and method. By collecting information for each campus interaction post and extracting features from comments made by the same account, the method classifies accounts using a pre-trained deep learning model. Through a hierarchical management strategy, potential bot accounts are categorized into high-risk, medium-risk, and low-risk accounts, thereby effectively intercepting false information, reducing misjudgments, and ensuring the authenticity of platform information and user experience. Experimental results show that this method significantly improves the accuracy of bot account detection and provides technical support for the healthy development of campus social networks.