Mining the Social Web, 3rd Edition

Chapter 4. Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More

This chapter introduces techniques and considerations for mining the troves of data tucked away at LinkedIn, a social networking site focused on professional and business relationships. Although LinkedIn may initially seem like any other social network, the nature of its API data is inherently quite different. If you liken Twitter to a busy public forum like a town square and Facebook to a very large room filled with friends and family chatting about things that are (mostly) appropriate for dinner conversation, then you might liken LinkedIn to a private event with a semiformal dress code where everyone is on their best behavior and trying to convey the specific value and expertise that they can bring to the professional marketplace.

Given the somewhat sensitive nature of the data that’s tucked away at LinkedIn, its API has its own nuances that make it a bit different from many of the others we look at in this book. People who join LinkedIn are principally interested in the business opportunities that it provides as opposed to arbitrary socializing and will necessarily be providing sensitive details about business relationships, job histories, and more. For example, while you can generally access all of the details about your LinkedIn connections, educational histories, and previous work positions, you cannot determine whether two arbitrary people are “mutually connected.” The absence of such an API ...