Social media speaks to how people interact and share information with each other in an online connected environment. Let’s look at a couple of the most common business problems that social media can help to inform. The first is helping us understand how our organization is perceived in the market. Analysis of the patterns and content of comments made about our organization can help us identify what things are going well and whether there might be issues we need to address. This in turn can help shape strategy and tactics in the market. So, how do we actually do it? Typically, we start with a tool or service that scrapes relevant content from a set of social media sites, blogs or other sources. Using this data, we can construct simple measures, like how many times people refer to our organization and where or when people are talking about us. We can also employ a technique called sediment analysis which uses text mining to identify words or phrases that are positive or negative or which refer to specific ideas or events of interest. This type of social media analysis tends to focus on how people relate to our organization individually or as a whole. However, there are some really interesting insights that can be gained by examining how people interact with each other. There are some ways this can be done using publicly available data but the richest insights are possible when an organization has access to a complete set of interaction. Social networks themselves obviously have access to this type of data. But so do organizations like telecommunications companies who can see things like calling patterns across all customers. If we do have access to broad network data, we employ methods like social network analysis to identify interaction patterns, groupings and even the roles of individuals within their own personal networks.
Let’s say we are able to see how a group of people interact with each other. We can use the presence of interactions to construct a network graph of the group. Right away we can see what looks like two or more connected subgroups linked by a few individuals with common connections between those subgroups. Now with only tens people it’s pretty easy to see how the connections exist. But you can imagine how difficult visual interpretations might be with hundreds, thousands or even millions of people.
It turns out that we can use some numerical matrices to identify people who occupy key positions within a social network. Here are a few popular measures each of which has a specific numerical calculations behind them. The first is Network Centrality, which is the number of direct neighbors someone has in the network. As the name suggests, these are people who tend to sit at the center of network clusters. A second measure is called the Closeness Centrality which is basically how close each person is to all others in the network. A third measure is Betweeness Centrality, which is a measure that identifies key linkages between nodes of a network. A final measure is called Clustering Coefficient Centrality which is also known as the all my friends know each other measure. It is the degree to which someone’s direct connections are connected to each other.