在线社交网络控制实验的现状与展望

在线社交网络已发展成为一个独特的电子生态系统，其应用深刻影响着人们生活的方方面面。由于在线社交网络特性复杂，分析在线社交网络形成和变化中的规律成为当前计算机科学、社会学和物理学的一项挑战。传统上，在线社交网络实证研究主要采用计算机辅助的被动数据获取和分析方式。近年来，在真实大规模在线社交网络上直接进行控制实验从而主动获取数据并开展分析研究的方式广受关注。评述了这一领域的研究进展，包括：社交网络控制实验的主要研究模式；控制实验方法在社交网络结构、信息传播、行为和心理学等领域取得的主要成果以及主要实验工具的适用条件和局限性。最后，展望了人工智能技术在社交网络控制实验中的应用潜力，分析了智能算法对降低实验成本和提高实验效率的作用。

Review of control experiments on online social networks

Online social networks have evolved into a unique electronic ecosystem whose applications have profoundly affected all aspects of people‘s lives. Due to the high level of complexity, the formation and changing laws of online social networks have become a challenge in current computer science, sociology, and physics. Traditionally, empirical research on online social networks has mainly adopted passive data acquisition and analysis. In recent years, a research that directly conducts control experiments on large-scale online social networks to actively acquire data has received extensive attention. This paper reviews the research progress in this field, including the main research modes of social network control experiments; main results of control experiment methods in some fields, including network structure, information diffusion, human behavior and psychology; and the conditions and limitations of current experimental methods. Finally, we prospect to the potential application of artificial intelligence techniques in control experiments, as well as the role of intelligent algorithms in reducing the cost and enhancing the effect of control experiments. This work provides a theoretical basis and enlightenment for expanding the application of control experiments in social network analysis.