Twitter at Events: Find Out What Attendees Really Think

The immediacy of Twitter has provided an unprecedented window into the collective mind of conference and trade show attendees as they share information on what they are doing and thinking right now. Just ask Evan Williams, the co-founder of Twitter. At his keynote at SXSW earlier this year, when he was interviewed by Umair Haque, Director of the Havas Media Lab, the negative comments on Twitter about the session came fast and furious while it was happening. “The guy behind us is snoring” tweeted one attendee, while another tweeted “walked out of the keynote…not very compelling”. This is not an isolated incident by any means. Recently, there was one call for banning Twitter at conferences, by a speaker who was dismayed that the audience was more engaged with tweeting than they were listening to the presentation.

Twitter has now made conference evaluation sheets and post-show surveys seemingly obsolete. If you really want to know what’s on the mind of your attendees, analyze the Twitter stream that flows from the attendees during a conference or trade show. There you will find the unfiltered and unvarnished truth about what attendees really think from the most vocal and most influential attendees at the event.

There is a wealth of information that can be gained from a Twitter stream during an event, well beyond the occasional negative comments that emanate from a keynote that goes flat. As an example, I archived the Twitter stream at a recent technology conference. In order to protect confidentiality, I’ll call it the Open Source Technology Conference. Let’s take a look at some of the information that can be gleaned from it.

The Most Influential Attendees

I collected a total of 1462 tweets that took place during the course of the event. 74% of the tweets were original tweets, 31% contain a @user reference, 39% contain hashtags, 33% contain a URL, and 26% were retweets. There were 312 distinct users that tweeted in the course of the event. Not surprisingly, the distribution of their tweets follows a power law (long tail) distribution.

The top ten most active users tweeting included the following:

What’s even more interesting are the conversational habits of the users, which can be illuminated by building a graph of their conversational patterns. The figure below shows a directed graph in which the users are the nodes and the edges represent mentions or replies between them.  In order to make the graph more visually intelligible, it shows only users shows users who have ten directed messages or more.

Each node in the graph represents a user. The size of the node is scaled to show the relative number of mentions and replies each user had. The color of each node ranges from red to blue. The more red a node is, the higher the authority value of the user – meaning that they are the users that receive the most mentions from others. The more blue the node is, the more the user tends to send @reply messages, and is thus more of a hub for conveying information to other users. The graph makes it clear that some users are more influential than others. The most important authority at the event was user “AmilCarta”, as is evidenced by the large red node representing that user’s interactions. This individual is an important person for the event organizers to recognize and interact with. The large size of user “theexhibitsgroup”, and its purple color, show that it is the second most important authority figure, but is also a hub that conveys important information to other attendees. All of the individuals on the graph, given their high level of interaction, are important for the event organizers to develop a close relationship with in order to ensure the success of their event.

Note that the volume of tweets generated by a user doesn’t necessarily mean that they interact with other users via mentions or replies. TSUS, the event organizer, was the seventh most active tweeter, but didn’t interact with attendees. TSUS used tweets to primarily make announcements about upcoming sessions, speeches, and awards programs. You can chalk this up as a major missed opportunity by the events organizer – by not interacting with attendees, it forfeited the opportunity to participate in the flow of the conversation.

What Attendees Were Tweeting About

The Twitter stream also sheds light on what topics were foremost on the mind of attendees. One way to get a handle on this is to look at the most frequently used hashtags by attendees in their tweets. By studying hashtags, we can determine what the key messages were that attendees want to spread via Twitter. The dataset for this event, in which 39% of the tweets contain hashtags, versus an average of 5% on Twitter as a whole, show a strong desire on the part of attendees to emphasize particular messages that will be found not only by other attendees, but by anyone interested in the particular topic represented by the hashtag. The top ten hashtags used at the event were as follows:

It’s not necessary to stop the analysis at this level. For instance, it’s possible to drill down into each of these topics and create a word cloud to get a better sense of the buzz around the topic. Below is the word cloud for the tweets containing the hashtag #ibm:

The word cloud gives an instant impression as to the content of the 94 tweets for anyone familiar with the event. It isn’t necessary to be limited by hashtags in trying to distill the content of the 1462 tweets. One can also use the text mining and data clustering techniques I described in my post  A New Way to Segment Your Twitter Followers With Analytics to discover the major themes of conversations at the event.

Even More Information…

I’ve really just scratched the surface as far as what you can learn about an event from analyzing its Twitter stream. There is much more that you can learn and implement:

• Find out more about the interests, sentiment, and affiliations of your attendees by analyzing the content of linked URLs within tweets.
• Get extra insight as to what activities generate buzz during your event by examining the timing of heavy periods of tweet activity.
• Identify unique communities within your Twitter network. Based on the graph of interactions displayed above, algorithms can be applied to the network structure to identify groups of attendees who tend to communicate with each other more frequently than with the rest of the group. These communities may have different interests than the rest of the network, which can be used to custom tailor your communications with that community.
• Cross reference and apply everything that you learn about the topics, conversational patterns, and communities of attendees that tweet during the event to your entire group of Twitter followers, and the friends, fans, and subscribers in all of your various social networks.
• Determine the network value of an attendee. The valuable information that you can learn from analyzing the Twitter stream at your event underscores the importance of capturing in your CRM system the social media user name and/or identity of your prospects and attendees. Once captured, you can begin to determine the network value of an attendee – how much that indivdual may influence others to attend within your network of prospects. These attendees can be targeted with custom tailored communications, referral program incentives, and rewards programs.

If you have more ideas about the information that can be learned by analyzing the tweet stream at an event, or have questions, please leave a comment or email me at rhodgson@predictive-marketing.com.