From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models

Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to use machine learning techniques to try to discover knowledge in real time. In this paper, we adapt our recently developed stream cluster mining, tracking and validation framework, Stream-Dashboard, to support detecting and tracking evolving discussion clusters in Twitter. The effectiveness of Stream-Dashboard in telling stories is illustrated by analyzing a couple of stories related to the Louisville Cardinals’ basketball championship. We further validate the detected story lines, that are automatically mined from user-generated tweets using as an alternative source, Google Trends, which are based on search queries.