A post in the Harvard Business Review asks this in relation to data generated by the browser logs of a New York Times article and twitter feeds which link to the same article. The result of this study is a platform for visualizing and cross-referencing these two data-streams. The author, Sinan Aral, professor at MIT and Scholar-In-Residence at the NYTimes R&D Lab discusses the work:
We’re doing this to understand and predict when an online cascade or conversation will result in a tidal wave of content consumption on the Times, and also when it won’t. More importantly we are interested in how the word-of-mouth conversation drives readership, subscriptions, and ad revenue; how the Times can improve their own participation in the conversation to drive engagement; how we can identify truly influential readers who themselves drive engagement; and how the Times can then engage these influential users in a way that complements the users’ own needs and interests. Do it, and we can turn that statistical analysis, as you’ll see below, into elegant, artistic real time data streams.
These visualizations, elegant and useful as the are, do not seem to go beyond the tracing of the complex network of agency between nytimes content and their related twitter feeds. What are the next steps in making the visualizations actionable in and of themselves? – i.e. to go beyond providing “decision-relevant information” and actually serve as a point of interface into these unfolding interactions. In other words, how can a platform like this imbed itself directly into the ecology of twitter feeds and nytimes content, becoming the third actor or point of interface in this ecology, not only tracing but also redirecting and deflecting the events in realtime?
Perhaps when dealing in the digital realm alone, this Big Data triumvirate: content producer + social network + data visualizer – is sufficient to close the loop, but I suspect, when dealing with domains that traverse the boundary from digital to physical (and back again), more complex systems of relationships and interface are needed. Here, the visualization must become the content, the content must provide the venue for social networks to emerge, and these social networks serve as the manpower for more content, etc. Rethinking content as physical could include anything from a building, a farmers market, a bus stop, a share vehicle, an alternate forms of production and retail, and so on.
These visualizations are an outgrowth of the Cascade Project, also by the NYTimes R&D Lab.
- The week in big data on Twitter, visualized (gigaom.com)
- Curing cancer with data visualization (successfulworkplace.com)
- Processing Cascades (Creative Applications)
- NYTLabs Cascade: How Information Propagates through Social Media (information aesthetics)