If you find yourself at ICWSM this week, say hi to us. I know I’ve been introduced to Naaman at least twice so far; I believe he still writes here. So far it’s been a nice mix of the standard social network analysis to S. Craig Watkins’s talk on Investigating What’s Social about Social Media (he’s from UT Austin’s Radio TV and Film department and gives a great perspective on personal motivations and behaviors). Yahoo!’s Jake Hofman gave a great tutorial on Large-scale social media analysis with Hadoop.
Tonight, I’ll be presenting my work on Conversational Shadows. In this work we look at how people tweeted during the inauguration and show some analytical methods for discovering what was important in the event, all based off of the shadow their Twitter activity casts upon the referent event. Let me give a clear example.
Ever go to a movie? Did you notice people chat with their friends through the previews. Once the lights go down and the movie starts, they stop chatting. Sure they might say “this will be good” or “yay” but the conversation stops. I began to wonder, shouldn’t this occur on Twitter while people are watching something on TV. Does the conversation slow down at that moment of onset or when the show starts?
During Obama’s Inauguration, we sampled about 600 tweets per minute from a Twitter push stream. The volume by minute varied insignificantly. However, “a conversation” on Twitter is exhibited via the @mention convention. The mention is highlighted. It calls for attention from the recipient. Our dataset averaged about 160 tweets per minute with an @ symbol. Curiously, there were 3 consecutive minutes where the number of @ symbols dropped significantly to about 35 @s per minute. We still sampled about 600 tweets, just there was a general loss of @s. People hushed their conversation. Perhaps even gasped. Here’s a graph to give you a better feel:
During those minutes where the @ symbols dropped, Obama’s hand hit the Lincoln bible and the swearing in took place. People were still shouting “Hurray!” but they weren’t calling to other’s via the @ symbol. Following the human centered insight (as we found by studying video watching behaviors), we can examine the @ symbols to find the moment of event onset. We call this a conversational shadow: the event has a clear interaction with the social behaviors to be found on the Twitter stream. We’ve found other shadows too, come by the poster session tonight to see them or, if you can’t attend, check out my paper.