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The Social Media Research Foundation's Data Approach

We talk to Marc Smith about his presentation ahead of his appearance in San Francisco

27Feb

Ahead of his return to the Social Media & Web Analytics Summit in San Francisco on April 29 & 30, we spoke to Marc Smith, Director at the Social Media Research Foundation.

Marc is a sociologist specializing in the social organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group and lives and works in Silicon Valley, California. He co-founded the Social Media Research Foundation, a non-profit devoted to open tools, data, and scholarship related to social media research. 

Innovation Enterprise: What are the most important recent innovations improving Social Media Intelligence platforms?

Marc Smith: The big innovations in social media are related to network analysis: understanding that every post, tweet, and update is a part of a collection of connections. A network perspective steps back from each connection to see an emergent pattern or shape. Networks matter because they can reveal the general shape of the crowd and the people who occupy key positions within it. Our group is focused on integrating and simplifying existing network science and content analysis techniques. We want to build a free and open, point-and-shoot digital camera for taking pictures of crowds in social media. Our innovation is to make it possible for more types of people to access the power of social network analysis by wrapping it up in a familiar environment: the Excel spreadsheet. Now, if you can make a pie chart, you can make a network chart.

What influence has the SMR Foundation had on these innovations?

The Social Media Research Foundation is dedicated to open tools, open data, and open scholarship. We have created and supported NodeXL, an open, free, and easy to use tool for collecting, analyzing, visualizing and reporting insights into social media networks (http://nodexl.codeplex.com). NodeXL is not the most powerful or most advanced network science tool. But it is the easiest one to use for most non-programmers. And it is designed for automatic, simple, push-button operation. This has meant that many more people are now able to create these kinds of datasets and analysis. That has meant that network science is entering many corners of the world that it was not able to enter before. Our influence is likely to be related to spreading the established techniques of social network analysis more than an innovation in network science itself. That said, we have innovated in the integration of many components and in our way of visualizing the network's clusters in bounded regions (a method we call "Group-in-a-box"). The impact we are having is visible when you search Google Scholar for "NodeXL" -- a growing number of peer reviewed papers are appearing (more every year!). These papers often come from scholars who probably do not have software development skills, but do have questions about the nature of online discussions and other connected structures.

What got you into Social Media Analytics?

I am a sociologist. I was trained at UCLA in three major sociological traditions: Interactionist sociology, collective action dilemma theory, and network theory. I was drawn to the potential and power of what we then called "online community" (and later called "virtual community", "groupware", "collaboration", "social software", and now "social media" among other terms). Sociologists are interested in why some groups of people get together and get stuff done, while others might come together only to fail. Looking at the early social web, I saw many examples of successful collective action, of collaboration, and generalized reciprocity. Newsgroups filled with questions and answers, medical mutual support groups, efforts to create encyclopedias, or maps or the world. Ambitious efforts that seemed impossible were created with some regularity. That said, there were and still are many failures: most efforts to get people to all work together towards a common goal are doomed to fail. The question is: what makes the difference? Collective action dilemma theory says part of the answer is in how people share information about each other's behavior's and histories.

Network theory extends collective action dilemma theory by providing a structural model for representing who interacts with whom and how (and when, how much, etc). It suggests that the answer to why some groups fail and others succeed is partially visible in the different network structures groups form. By mapping social media networks, we can reveal the shape of the crowd and document the range of different kinds of crowds that are possible. Within each crowd, there are a few relatively rare positions occupied by people with strategic location and power. These influential people are often important to identify and engage for an effective social media strategy.

What are you going to be discussing at the Social Media & Web Analytics Innovation Summit? 

I look forward to returning to the event this year.

I plan to share our NodeXL social media network maps of the hashtags and discussions related to the event. I will demonstrate how most people could set up NodeXL and start making social media network maps in about 15 minutes. We will review what the output of these maps and reports means: how to interpret the structure and meaning of networks. This will build on our recent publication in collaboration with Pew Research Internet Project: Mapping Twitter Topic Networks (see: http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/).

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