Characteristics Analysis of Moving Conversations to Detect Events on Twitter

A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial- and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter.