In March 2013 Devin Gaffney and I collected 2 weeks of #CPC and #NDP (the Twitter hashtags associated with the Conservative Party of Canada and the New Democratic Party of Canada). We wanted to know who was most influential. The problem is, there are a lot of ways to measure influence and they don’t all line up!
In this paper we talk about some of the most basic ways researchers identify “influence” on Twitter, we consider the theoretical underpinnings, and we compare them using the #cpc and #ndp datasets we created. We explain how different measures tend to cater to different facets of influence. We go on to argue that to measure influence in the Katz and Lazarsfeld – opinion leader – sense, we need to incorporate measures of social connectedness locally. We make a preliminary case for making use of the clustering coefficient.