Monitoring Dynamic Patterns of Information Diffusion in Social Media: A Multiresolution Approach with Twitter Data

• Activity Number: 568
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Hechao Sun*+
• Keywords: information diffusion ; Twitter ; visualization ; network analysis ; viral marketing

Abstract

With advances the increasing popularity of social media, understanding the diffusion of information on this new media is becoming increasingly important. Social media has significantly changed marketing, affected the exchange of ideas, and even assisted in social revolution. Thus, it is important to understand dynamics and mechanisms of information flow through popular social media, such as Twitter, Facebook, etc. However, most studies of social media data focus on static relationships, such as following-follower network in Twitter, which is not necessarily well-correlated with actual patterns of conversation. Therefore, it is necessary to move beyond static network representations; revealing dynamic patterns of the network is necessary for accurately understanding information diffusion in this media. In our work, we perform a multiresolution analysis with Twitter data, where we examine, using a variety of different time windows, the dynamics and properties of mention and retweet networks around several major news events. We find that network properties stabilize at larger sampling resolutions, from which we draw new insights for viral marketing and information diffusion.