A Social Media Text Analytics Framwork For Double-loop Learning For Ctizen-centric Services

In recent years,due to the rapid development of new media,micro-blog,WeChat and NetEase have become well-known social media platforms.People can share data on social media platforms,and micro-blog has become an important channel and carrier for people to communicate due to its openness and strong interaction.Micro-blog is an official interactive platform for listening to public opinion and serving the public.The micro-blog set up by government agencies and officials aim to provide public affairs.On the one hand,the establishment of government micro-blog has changed the ecological environment of the media and changed the way the citizens communication.For there are no restrictions on relevant laws and regulations,citizens can speak freely through micro-blog.The government can see it as an important channel for understanding the civil situation.On the other hand,government micro-blog can integrate scattered information.The government can mobilize the public to set up issues,arouse people's discussion,and can also gather the wisdom of the people to help the government handle cases,and help the government to deal with some emergencies.However,because micro-blog can send a large amount of information in the short term,it can easily lead to a large amount of data accumulation in a short time,and these data are uneven.In order to accurately extract effective information,this paper introduces the LDA theme model.The use of the theme model can quickly find the relevant information in large quantities of data.This paper develops a framework for facilitating organizational learning through social media text analytics to enhance citizen-centric public service quality.Theoretically,the framework integrates double-loop theory with extant models of e-participation in government.Empirically,the framework is applied to a case study of citizen-to-government online interactions on microblog.Our findings indicate that the missed double-loop learning opportunity resulted from two factors.To begin with,microbiog were primarily used to advocate the government's agenda or give some tips to citizens.Secondly,this single-loop learning orientation were used to maintain the nature of the government's posts,and precluding the meaningful changes to meet citizen's needs,New insights generated from the case study suggest government's double loop learning does enhance the services of the government.