I had the honor to participate in Harvard Law School’s behavioral economics and social media conference, organized by Cass Sunstein last week. Scholars from across Harvard along with folks from Facebook, Twitter, Microsoft Research and SocialFlow discussed important trends around social media, theory and practice and its potential to help us assess behavioral change. As part of the ‘theory and practice’ session led by Yochai Benkler, I presented alongside Facebook’s Eytan Bakshy and Sharad Goel of MSR.
Nate Matias, research assistant at the MIT Media Lab’s Center for Civic Media put together a comprehensive writeup of the session. Sean Laurence of Boston Startup School put together full audio of the event here. Following is a crib of my presentation on the promise of realtime data from social networks.
I’ll start with a short story.
I just moved into a larger apartment in New York City and finally have enough space for a piano. So I did what many do, and start obsessively researching the web for used upright pianos. From Craigslist to Google to rental stores, the task is actually quite difficult given the variety in types, sizes and prices.
It didn’t take long before piano ads started following me around the internet. As I consumed the news, I saw ads for Yamaha pianos. When I went to YouTube, ads for Steinway. Even when reading my daily Mashable quota…, more piano ads. Following me around as I browse the web, regardless what I was doing, making me feel terrible for being that indecisive procrastinator who can’t seem to make up his mind.
My anger at the ads quickly turned into pity. Faust Harrison Pianos were clearly users of the latest in digital marketing strategy wonders, buying against user behavior stored in cookies within people’s browsers. I must’ve clicked on their website at some point in time, and since then, my browser has a cookie that signals my interest in acquiring a piano. True the intent is there. But believe it or not, it is not the only thing I think about throughout the day. The last thing I’d want is to be reminded every minute of every day that I still have to make this decision.
As ads attempt to become more “relevant” either by matching to our browsing history or to friend association, they are doing more harm than good if they do not understand the user’s context, and more importantly what someone is willing to be attentive to. Intent used to be the biggest buzzword around search engine conversations. Back in the day, the thought was that If we could identify someone’s intent we could present them with relevant information. They got that right with my search. But where these ads completely failed at was understanding my context as well as my personal psychology around purchasing. Its been over 10 years since Google innovated and changed the world of advertising. Is cookie-based ad targeting *really* the best we can do?
It felt good to see that I’m not alone here. Digg’s @tolar claims that he visited Urban Airship once and now can’t escape their ads:
Tom Fowler tweets:
And Jared Kim adds:
When I search google for ‘ads stop foll…‘ I clearly see that other users experience the exact same thing.
Various ad-blocking services have sprung up, FixTracking.com has some information. Otherwise many informed users make sure to clear their browser cookies on a daily basis. Is this really the type of ecosystem we want to support? Where the technically informed are able to block ads from chasing them around the web, while the majority deal with the consequences?
Enter Social Media.
So much has been written, discussed and examined about the shift that we’re seeing with the popularity of social networked spaces. The networked nature of these spaces mean that our old ways of dealing with audiences has got to change. Power has to be renegotiated, and in many cases doesn’t come top-down, but rather from loosely connected points in the network.
In order for information to spread, people along the way must be attentive and choose to pass the tweet or status update onwards. As the threshold to publishing content nears zero, getting people to be attentive has become a scarce commodity. One cannot demand or even expect someone’s attention at any given point in time. As James Gleik puts it in his seminal book, The Information, “When information is cheap, attention becomes expensive“. You don’t need to take Intro to Macro-economics to get this.
The following plot does a great job at expressing how attention shifts within social networked spaces. The green line represents the number of tweets over time that had the word ‘Superbowl’ in them while the blue, the word ‘power’. This is measured over time across all publicly posted Tweets between February 3rd and 4th. Note the clear switch that happens when the Superdome goes dark. Attention shifts from the game which abruptly stops, to focus on the fact that half of the stadium loses power.
What evolves online, is the poster child example of how realtime information can be used to inform marketing campaigns. It took minutes for Oreo to come up with an innovative advertisement in response to the blackout, which got them a significant level of visibility (16k retweets and 6k favorites so far only on Twitter). Twitter reported that it took just 4 minutes for someone to buy promoted tweets against searches for the phrase “power outage”. Other brands quickly responded as well, catering to the millions of sports fans who were following the chain of events happening in the stadium. Having flexibility and changing the frame to what people were attentive to, the power outage, clearly paid off.
We see these kinds of attention shifts happening all the time, whether affecting a wider region of the network, or a localized audience.
Using information from social networks can help us understand the context switches happening amongst audiences and generally within populations in realtime and over time. What people are attentive to and how that changes over time. In a study that Suman Deb Roy, our summer intern from last year defined and measured what he called audience volatility – the frequency of change in topics at the focus of an observed group of users. The higher the volatility of an audience, the less focused it is, as there’s a wide array of topics at play. The lower the volatility score, the more focused an audience.
For example, when we measured the volatility in Twitter’s trending topics across different cities we could see clear peaks and troughs in volatility. Remember, the higher the graph, the more volatile the trends within that city. Whats fascinating about this plot is the lowest point marked with an arrow. This happens around the second week of March, 2012, and represents a point of heightened focus across all major cities in the United States.
The lowest point on this graph is the day that Invisible Children launched their #Kony2012 campaign. This is the point of lowest volatility / maximum focus, showing just how good that campaign was at capturing people’s attention in all major cities across the United States
Why is all this important?
This is the first time that we can clearly identify spikes in user attention, what groups of people are focused on, in realtime, and over time. We don’t have to wait for market research and poll results, but rather we can plug into this information. Additionally, we have a way to quantify these shifts, seeing just how much effect real-world events have on groups of people online, how much focus they choose to devote to said event.
As we get better at understanding of user interaction within social networks, we’ll get a more holistic view of whats going on. While there is still benefit in planning campaigns and taking the time to think through their design, social networked spaces bring with them the hope for a more nuanced understanding of user behavior, intent as well as context.
Maybe soon advertisements will stop following us around the web, and pop up in the right context, at the right time.
Am I too optimistic? Maybe. But I still want to get that piano!