Eric Gundersen, Javier de la Torre, Sean Gorman, & Francesco D’Orazio discuss social data and geo.
We are at the point in social where there is a shift happening in the way we look at data. Literally. Data visualization is becoming more necessary and more powerful. This panel showcased the ability to use maps to simplify analysis and make things more visual, but more importantly it makes it easier to separate the noise from the signal in social data.
Sean Gorman from Timbr discussed how this company focuses on the backend of data analysis and visualization. They are a platform for enabling algorithmic orchestrations with social data but he paraphrased this as giving people the tools to structure and clean social data to facilitate easy enrichment and binding of that data. Some enrichments around location he mentioned were friend-of-friend triangulation, finding location in text of Tweets, Gnip’s Profile Geo, and other ways to get location appended to social activities. He also pointed out that standard dashboard analytics providers want to add maps but don’t have the resources so they use Timbr.
Sean spoke about how there are anomaly detection algorithms as well as other algorithms on git and other places that they enable people to use and customize in their map making process. That map making process on Timbr includes live iteration of code to maps for quick visualizations.
All of the panelists on stage are working to lower the bar for coming up with custom tailored analytics for the questions people have with social data.
Part of custom tailored analytics is the visualization aspect which numerous panelists hit on at different times. Eric from Mapbox showcased their product which is a platform for designers and developers to make custom maps within their apps. Companies such as Foursquare, Pinterest, and the Financial Time use Mapbox to display their data. Eric showcased a number of maps made in conjunction with Gnip which show 3 billion geotagged Tweets and pointed out that the most amazing part of these visualizations is that there is not map behind the dots, the data is actually what is creating the maps.
Eric pointed out the analysis that can come from these types of visualizations. You can see the economic disparity in cities by looking at the regions where people post from an iPhone or and Android device. You can see the buying trends for countries such as the Blackberry usage in Malaysia, but nowhere else.
The panel also discussed how low opt-in on platforms for geolocation is an issue for creating great analyses. Only 1-2% of people opt-in t share their location along with their Tweet. Those who do share their location end up speaking for larger population when the analysis is done through a map visualization, which ends up creating a bias. Sean mentioned a preliminary study using 100,000 users that shows that the portion of Twitter that shares location is skewed to have a higher proportion of African Americans, a lower age, a higher proportion of renters, and a smaller household size than the general population. They are expanding the study to 1 million users.
Javier from CartoDB showcased a few visualization he made in conjunction with Twitter which show both a map and time in a single visualization for millions of Tweets. He talked about how social activities happen in a place which is important to see but also the concept that social activities happen at a time and that adds additional context to analysis. These visualizations allow you to explore the connection of time and place to understand how an idea spreads on social, he used a Beyonce surprise album release to showcase how Twitter “explodes” with news. Ideally for Javier you don’t have to be a designer or developer to tell these types of stories in powerful ways.
The panel also discussed the amount of conversation dedicated to data analysis but that there is not much talk of data visualization. Javier said that investment in the analytics and not the visualization is like having lots of power without any control.
Francesco from Pulsar talked about how they like to add in another filter, audience intelligence, to their analysis. He mentioned the example of people on Twitter talking about Coca-Cola is interesting but what are moms saying about Coca-Cola. Adding in this additional layer adds valuable context to the analysis.
He showed a map of people complaining about bad cell phone signal. A mobile carrier knows that when a network goes down it doesn’t happen in an instance but rather as a series of slow failures. Showing the maps of people complaining can allow the carrier to see where the network is failing and do something about it, which is a great example of social data being used in an engineering case, and not just for marketing or PR.
Conversation turned to geo outside of the USA and on other platforms besides Twitter. Sean mentioned that 10% on Sina Weibo activities have location attached, likely because of the emergence of phones along with emergence of internet in China and Weibo being a mobile first app. This lead to a discussion on how to incentivize people to share their location more. While issues like privacy were touched on it was clear that the panelists agreed that the biggest challenge in getting people to share location more was the lack of clear benefit to the consumer for doing so. Right now the conversation is more focused around how the industry can benefit from this resulting geo data and not on how the user can benefit from sharing it.
Many times in this panel the panelists mentioned that there is lots of challenges in the social geo space but also lots of opportunity. Javier said the next 12-18 months should be incredibly interesting in this space, as he touched on ideas around ability to control zoom and speed on maps that have time and place dimensions.