Governments worldwide are increasingly engaging with both citizens and non-citizens via online social networks. This trend reflects the fact that these platforms have become primary sources of news and crucial forums for public discussion. China is no exception to this phenomenon.Existing research into the Chinese government's use of online social networks and the public's reaction to it primarily offers qualitative insights rather than quantitative evidence. There is a noticeable absence of publicly available models and datasets that could enrich the research community's understanding of this topic. This dissertation aims to fill this void by proposing novel frameworks to examine the Chinese government's use of online social networks and the public's response.In the first section, this dissertation proposes a fresh framework for identifying persuasive techniques in textual posts. The proposed framework employs a divide-and-conquer strategy to isolate and detect each persuasive technique individually. This approach is found to be effective in extracting features specific to each technique. Furthermore, the framework leverages GPT-3.5 to generate additional training samples at a much lower cost than other methods reliant on human-annotated external data. The model derived from our framework surpasses the performance of the previous state-of-the-art model.In the second section, the dissertation investigates the similarities and differences in the Chinese government's posts across various platforms, each targeting distinct audiences. It is found that the government's focus varies from platform to platform, presumably due to the diverse audiences each network attracts.The final section of this dissertation provides a quantitative analysis of users' opinions towards the content produced by the Chinese government on online social networks. This analysis assesses whether posts by government-affiliated accounts reach their intended audiences and explores the stance characteristics of the responses.Overall, this dissertation aims to contribute to our understanding of Chinese government's engagement on social media platforms.