Tuesday, October 14, 2008

Social Media and Operations Research

Social media and Web 2.0 have been the buzz words in the internet marketing world for a few years now. Of course, we can count on the Numerati (the new term for Operations Researchers in reference to the title of the new book by Stephen Baker) to start scratching their heads and eventually come up with systematic ways of mining vast amount of data (i.e. analytics), and then applying the harvest of knowledge from other disciplines, such as psychology, to study people’s behaviours (hence diverting from the non-traditional OR application field of mechanical processes). Claudia Perlich from IBM and Sanmay Das from Rensselaer Polytechnic Institute individually explain ways they have used OR to dissect the world of blogs and Wikipedia to provide insight to marketers and to demonstrate the conversion of Wikipedia’s highly edited pages to a stable and credible source.

Ever since the existence of blogs, marketers have been nervous about the reputation of their products. Lucky for the IBMers, when the marketers at IBM are nervous about the client response to their product (i.e. Lotus), help is within reach from the IBM Research team. Marketers want to know: 1. What are the relevant blogs? 2. Who are the influencers? 3. What are they saying about us? 4. What are the emerging trends in the broad-topic space? Perlich’s team went about these four questions by starting with a small set of blogs identified by the marketers (the core “snowball” of blogs), then “rolling” over the “snowball” twice to discover other blogs related the core set (i.e. max 2 degrees of separation from the core). To find the authoritative sources in the snowball, Google’s Page Rank algorithm came to the rescue. Using sentiment labeling, the team was able to say whether the overall message of a blog was positive or negative. Then to allow useful interaction with and leveraging of the data by the users (i.e. marketers), a visual representation was rendered to show the general trend in the blogosphere about the product in question (see image). At which stage, marketers are able to dig into each blog that is identified as positive or negative, and the problem would seem much more manageable.

Das’ talk fits in rather nicely with Perlich’s, as he examines the trend of blog comment activities and Wikipedia’s edit histories to try to demonstrate the eventual conversion of these information sources to a stable state. The underlying assumptions are that the more edits a Wikipedia page has, the more reliable its information is, hence the higher the quality; and the higher the quality a page is, the less likely that there will be more talks/edits on that page, because most likely what needed to be said has already been said (aka informational limit). Das obtained the data dump of all pages on Wikipedia in May 2008, and obtained all 15,000 pages (out of 13.5 million in total) that had over 1,000 edits. Using dynamic programming to model a stochastic process, Das was able to find a model for the edit rate of an aggregation of these highly edited Wikipedia pages. Then he applied the same model to blog comment activities. In both cases, the model fit extremely well to the data, and surprisingly the shape of the activity pattern over time looked very much alike between blog comment and Wikipedia page edit activities. An interesting inference made by Das was that people contribute less of their knowledge to Wikipedia pages than blogs.

This is the beauty of Operations Research. It is a horizontal plane that can cut through and be applied to many sciences and industries. Aren’t you proud to be a dynamic Numerati?

Credits: The talk was given at the INFORMS 2008 conference in Washington DC. The track session was MA12 & MB12. Speakers are Claudia Perlich, T.J. Watson IBM Research, and Sanmay Das, Assistant Professor, Rensselaer Polytechnic Institute, Department of Computer Science. The talks were titled "BANTER: Anything Interesting Going On Out There in the Blogosphere?", and "Why Wikipedia Works: Modeling the Stability of Collective Information Update Processes".

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