The Social Cocktail, Part 2 Expected vs. Unexpected Events

In the last post on social cocktails, we looked at some high-level attributes of the social media publishers, and how these attributes might help you choose the right mix of topic coverage, audience, speed and depth. In this post, I will spend a little more attention on the speed dimension by diving into a description of the social media responses to expected and unexpected events. Finally, in the third post, we’ll end with an example of the social cocktail in examining a real-world event—the JPMorgan-Chase $2+ billion loss announcement in May 2012.

When we want to quantify the social data response to an event, we often start by looking at the volume of activities around that event. Applying some filtering allows us to group activities on a topic and look at how the volume of the stream of these activities evolves with time.

Time-series volume measurements of social data generally show three distinct patterns for breaking events. These patterns can be related to the user’s expectations of the event. The rate of spread and reach of a story also depends on the level of interest of the audience as well. When looking at the time series of activity volume, we see patterns characterized in Table 1.

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Table 1.  Social data reaction event types.

Expected events often drive significant volume on social media because people want to comment, observe, banter, trash talk, and analyse.  Twitter volumes surge during the World Cup, the Super Bowl, the Video Music Awards, etc.  However, theses events show a gradual growth and decay around the event rather than abrupt changes. The bump in volume may last from a few hours to a few days.  It is often somewhat symmetric in its growth and decay. Figure 1 shows some examples of social data volume round expected events including the VMAs and Hurricane Irene.

Figure 1. Expected events show smooth and fairly symetric growth and decay of social media volume over time. Examples from the VMAs and Hurricane Irene. 

Unexpected events result in abrupt spikes in volume. On Twitter, these spikes may reach tens of thousands of related tweets per minute within 5 minutes of an event. Social data volume around unexpected events usually grows rapidly until the networks for related users are saturated with the information, then the volume decays exponentially.  These spikes have well-defined growth, peak and decay half-lives. See Social Media Pulse for discussion and analytical details.

There is a key difference between events that are witnessed by many social media users simultaneously, and news that break exclusively on social media.  Examples of the former include a spectacular goal goal in the World Cup, an Earthquake, or Beyonce’s performing pregnant at the VMAs.  Because users see these events at the same time, the social data volume instantly jumps to high levels; there is very little ramp up. See Figure 2a and b for an example of a simultaneous unexpected event response curve.

Figure 2a – Earthquake in Mexico.


Figure 2b – Steve Jobs Resigns as CEO of Apple


News the breaks partially or exclusively on social media has an observable ramp up as the news spreads through the network of related users. This spread can depend essentially on the number of followers, the credibility of the source, etc.  These events have a convex ramp up as shown in Figure 3.

Figure 3.  The sad news of Steve Jobs passing away was originally broken through traditional new sources.  But the story quickly travelled on twitter to many users that were not watching the news, so it took took a shape somewhat like a story breaking on twitter.

You are now equipped to understand the dynamics of an event at a deeper level.  Quantifying the speed of a story will help you consistently characterize and compare the impact of events and the response of the audience. With these tools in hand, you are now ready add a twist to your social cocktail with the garnish of recognizing activity patterns over time.

  • http://twitter.com/cased Alice Casey

    This is really fascinating stuff – thanks so much for sharing. What other work are you planning in this area at the moment?I’m interested in longditudinal work analysing collaborative platforms usage.

    • http://twitter.com/DrSkippy27 Scott Hendrickson

      Alice, Thanks for your comment. Part III in this series is coming out next week. In that post we will look at some specific examples across publishers. We also have some research on discussion and comments coming later this year. Gnip’s publishers currently don’t provide activity data for any project-oriented or team-coordination collaborative platforms–is that data you would be interested in? Best, SH