Framework for Participative and Collaborative Governance using Social Media Mining Techniques

Abstract

Social media and networking sites have broken the barriers in communication and brought about a revolution in information access, dissemination and communication. Given the inclusive and collaborative nature of social media, political leaders and government organizations too have attempted to harness it for building support, gauging popularity and analyzing the opinions of citizens. This paper, proposes a framework which focuses on the application of text mining techniques on social media content for achieving participative, collaborative and inclusive governance. Most of the political entities and government departments have twitter accounts. Though the tweet text is limited to 140 characters, the accompanying metadata and hashtag (#tag) embedded content make it a potent source for extracting entities, concepts and topics. Web Crawling for content pertaining to these entities and compilation of the same, results in a corpus suitable for text mining. Applying Natural Language Processing (NLP) and mining techniques like Part-of-Speech (POS) tagging, classification, clustering to this generated corpus enables categorizing and summarizing: a) sentiments b) queries and c) grievances. Summarized view of the content makes it amenable for decision making and formulation of responses. A Knowledgebase (KB) is created and perpetually updated with the concepts, entities, summaries, queries and responses. The continued process of creation and enhancement contributes to machine learning. The scope of this paper is limited to proposing a conceptual framework for social media mining facilitating informed decision making for better governance.