Note: This is an edited extract from Analyzing the Social Web (opens in new tab) by Jennifer Golbeck, published by Morgan Kaufmann
Social media has become the dominant method of using the Internet, and it has infiltrated and changed the way millions of people interact and communicate.
Social networking in particular has become extremely popular, with over one billion users on Facebook alone and billions more accounts across thousands of social networking sites online.
Understanding social networks—both those explicitly formed on social networking websites and those implicitly formed in many other types of social media—has taken on new importance in light of this astounding popularity.
Analysis of these social connections and interactions can help us understand who the important people are in a network, what roles a person plays, what subgroups of users are highly interconnected, how things like diseases or rumors will spread through a network, and how users participate.
Applications of these analyses are extensive. Organizations can prevent or control the spread of disease outbreaks. Websites can support participation and contributions from many types of users. Businesses can provide immediate assistance to customers who have problems or complaints.
Users can band together to better understand their communities and government or take collective action. Content providers online can filter and sort information to show users the most relevant, interesting, and trusted content.
The methods for analyzing social networks have been around for decades or longer, but social media provides new challenges and opportunities. Networks online are orders of magnitude larger than the networks analyzed in the past.
Often, the networks are simply too big to be analyzed in their entirety. A good social network analyst working with social media needs to know how to analyze the structure of networks, apply sociological principles to understand user behavior, and deal with the size, scope, and application of the networks.
This book is designed to teach the reader a range of social analysis techniques, how to apply them specifically to social media networks, and to illustrate a number of specific social media cases to which the techniques can be applied.
This chapter will present a history of the social web and an overview of the major types of analysis. For background, the types and details of websites used throughout the book will be covered, as well as some free tools that are useful for visualizing and understanding networks.
Analyzing the Social Web
Classic social network analysis studies a network's structure. In a social network, a person is considered a node or vertex, and a relationship between people is a link or edge.
When all the people and relationships are identified, there are many statistics that can provide insight into the network. However, even before learning those statistics or anything about social network analysis, you can probably identify some important and interesting things in a network.
Consider Figure 1.1. Each circle is a person or node, and each line connecting them is a relationship or edge.
What things can you say about this network, without any training in social network analysis? We can see that node a has a lot of relationships.
There is a long series of relationships from a to b to b1 to b2 and so on. There are many relationships among the nodes a1 through a10 in the lower right. That might be a group of people with very close relationships.
The first part of this book will introduce formal methods for quantifying these types of insights. This will include measures of a person's importance, how well connected the people in the network are, and which people form communities or clusters together.
These statistics are frequently used and often provide good insight into the nature of a network. However, those quantitative measures are not the only inter- esting ways to understand a social network. We will also look at qualitative attri- butes of the network. Tie strength, which is the strength of the relationship