Counterfactual Reasoning over Community Detection: A Case Study of the Public Science Day Community

Abstract

With the rapid rise of new media platforms such as Weibo and Tiktok, communities with science communication characteristics have progressively grown on social networks. These communities pursue essential objectives such as increased visibility and influence. For the success of the public understanding of science in China, case studies of science communication communities on social media are becoming increasingly valuable as a point of reference. The authenticity of user influence plays an important role in the analysis of the final outcome during the process of community detection. By integrating counterfactual reasoning theory into a community detection algorithm, we present a novel paradigm for eliminating influence bias in online communities. We consider the community of Public Science Day of the Chinese Academy of Sciences as a case study to demonstrate the validity of the proposed paradigm. In addition, we examine data on science communication activities, analyze the key elements of activity communication, and provide references for not only augmenting the communication impact of similar types of popular science activities but also advancing science communication in China. Our main finding is that the propagation channel for the science communication experiment exhibits multi-point scattered propagation and lacks a continuous chain in the process of propagation.

Keywords

• causal inference
• counterfactual reasoning
• community detection
• science communication
• social networks