Abstract :
As more people take part in online conversations, awareness of the varying conversational styles and social mores afforded by different software is growing. However, this awareness is largely built on personal impressions as varying styles of social interactions are hard to discover in text-based presentations. Through visualization we explore social and temporal interactions in instant messaging. CrystalChat visualizes personal chat history. Rather than showing online social networks that indicate merely who talks to who, CrystalChat reveals the patterns in an individual’s communications with those people who are part of their personal chat history. The patterns revealed come from instant messaging data that includes information about temporal clustering, conversation initiation, conversation termination, length of conversations, length of postings, patterns of repetitive or alternating postings, and emotional tone as represented by emoticons.
