Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.
Two recent videos featured on FlowingData tell the story of information abundance and the need for information visualization to grasp its meaning.
See: http://www.gapminder.org/videos/the-joy-of-stats/ for the recent BBC documentary featuring Professor Hans Rosling and his entertaining presentation of historical changes in world health and wealth statistics over two hundred years.
In this video interview, John and I summarize the themes discussed at the symposium including the political implications of inequality of technology access and the literacy to use it. John describes his efforts to map the global blogosphere and I describe the ways social media creates social networks that can be extracted and mapped. What does it take to be a communicator in a digital media environment? We discuss the privacy rights of public data and the use of data in ethical ways. Not everyone with a fiber-optic cable and server room operates under ethical guidelines. Given that digital communication is inherently traceable communication, could it be that not everyone should take the risks of communicating? Digital communication makes messages more findable and available which is a virtue when you want your message heard widely. It is getting harder to limit distribution of content to select audiences. I like to argue that the destiny of all information is to be made public if only because information never becomes less public.
IE University has a YouTube channel with lots of interesting video (in English and Spanish) related to communication, innovation, and social media.
I had an interesting discussion with Deborah Grove about the ways social media impacts the costs of computing and the efforts to “green” IT by making servers and networks more power efficient. Social media is a growing segment of all the data stored and processed on earth. But social media has different properties than previous classes of data like transactions and documents. Some social media is more resource intensive than others (a YouTube video consumes more energy to store, transmit and view than a Twitter tweet) but even light weight social media can cause heavier media types to be suddenly accessed in a high spike of demand, for example when a brief tweet points to a video that then drawn thousands of viewers.
More efficient IT is clearly important as computing consumes a growing slice of our power consumption. Social media is both a new source of demand for compute resources (and by extension, power) and a possible method for gaining new efficiency as server farms are better tuned and maintained. I would like to see major resource consuming services like YouTube and Google surface the costs of computing more. While google, for example, reports how many fractions of a second it takes for them to return your search results, it would be useful to see how many shot glasses of diesel fuel were needed to power the servers that generated those results. PCs and server farms do no have tailpipes spewing emissions but they are not carbon neutral: lots of coal, oil, gas, and uranium (and not nearly as much wind, hydo, and solar) are needed to keep the net alive. Making the energy costs of computing more visible would be a good step toward IT conservation. I know I have too many computers running all the time (short boot up times would help!) and should think more about the energy consumption impact of my information diet.
I would have been smarter in the interview if I read How Green is my Blog on Slate by Jacob Leibenluft which does a good job of digging into the real costs of moving a gigabyte around the net. Still I wonder if all costs are being accounted for. Storage and transmission are one set of computing energy costs, how much computation do different classes of data require? The weather report takes more computation to deliver than the comics page. A web crawl and index may cost more than a hosted data set that is only minimally analyzed or recalculated. Some bytes change more often than others. Not every byte has the same amount of energy expended to get it into its current state.
