Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and online communities.
He's a regular blogger on the Lithosphere and previously wrote in the Analytic Science blog.
You can follow him on Twitter at mich8elwu.
Last time, I introduced a very simple model of influence and outlined the basic ingredients that are required for successful WOM influence. Today, I will focus on the influencers and show you how to find them. Since this article builds on my previous post, I recommend you read "The 6 Factors of Social Media Influence" before diving into this article.
Influencers on Social Media Channels
In the real world, pretty much anyone can be an influencer, and the number of effective influencers is quite large compared to the number of potential targets. However, when we move onto a social media channel, the number of effective influencers is significantly reduced. The primary reason for this reduction is because the ability of the influencer to transmit their knowledge through the particular social media channel is greatly impeded.
Some of the reasons are:
This alleviates a problem for marketers -- it reduces the number of influencers in the channel of interest to a somewhat manageable amount. This enables marketers to have deeper engagement with the influencers and drive more effective marketing campaigns.
Because influencer identification is the first step in any WOM and influencer marketing, this has been the focus of most marketers. Most of the effort has been focus on finding influencers with high bandwidth in the social media channel of concern. Less effort has been focused on finding influencers with high credibility in the relevant domain. However, a powerful influencer needs to be both credible and have high bandwidth, yet credibility and bandwidth are not always correlated.
Finding the High Bandwidth Users
Finding the influencers is actually the easiest part, because there is plenty of data available to achieve this. Three types of data have been widely used in identifying high bandwidth users:
1. Participation velocity data: For example, number of tweets per day on Twitter, number of articles per month on a blog, number of messages per week in a community, etc.
2. Social equity data: For example, the number of follower on Twitter, number of friends/links on a social network, cumulative number of posts in a community, total viewership or total number of unique readers on a blogs, etc.
3. Social graph data: Certain social equity data can also be derived from the social network analysis (SNA) of social graph data. But it depends critically on what kind of relationship is encoded in the graph. For example, SNA on the friendship graph of a social network (where the graph edges indicate friendship relationship) could easily reveal the popular friends and the gregarious social butterflies in the network. Likewise, SNA on a social graph of following/followers relationship on Twitter would give you the fans, enthusiasts, and celebrities. In these types of social graph, where the users are not connected by a common domain of knowledge, SNA can discover the high bandwidth users.
Finding the Credible Users
Four types of data can be used to find credible users
1. Reciprocity data: The best way to find credible users is to use data on what others say about them in the particular domain of interest. Online, this is found in rating data. For example, if a user answered some questions in a support community, did he get a good rating for his answers? What percentage of his answers was marked helpful? If the user has a blog how many people tweeted his articles, how many hyperlinks linked to his blog, what was the blog's PageRank? If he wrote reviews, how many and what fraction of the people who read his reviews found them useful? How many people recommend that user on LinkedIn?
2. Reputation data: Reputation engines often summarize reciprocity data and assign a reputation score, or rank, to the user. This data is also valuable for finding credible users.
3. Self-proclaimed data: This is data that users assign to themselves in their user profile. It can be their career experience on LinkedIn, or any professional or non-professional groups they joined. Since these data are mostly self-proclaimed and are not validated, they are less reliable. However, for very specialized and niche domains where reciprocity and reputation data is not available, self-proclaimed data with some validation from an independent third party does come in handy for identifying credible users.
4. Social graph data:Although social network analysis (SNA) on a social graph can be used to identify credible users, how the graph is constructed is of key importance as well as what kind of relationship is captured in the edges of the graph. Using the same example as in my earlier discussion, a friendship graph on a social network would not be appropriate for finding credible users. Just ask yourself, do all your friends on Facebook have the same domain expertise as you? I think the answer is clearly "No." Likewise, a social graph of following/followers relationship on Twitter is also not suitable for finding the credible users. Unless we can remove the part of the graph that is irrelevant to the domain of interest, these social graphs will only give you the high bandwidth users, which are not necessarily the same as the credible users.
So what kinds of social graph can give us the credible users? A graph where the edge encoded relationship is domain specific. For example, the social graph of group members in a LinkedIn group, or the graph of the conversation network on a particular subject. These would be more suitable for finding the credible users.
Alright, now that you know how to find the high bandwidth users as well as the credible users, you are ready to find the influencers in your domain. You simply take the intersection (the users that are in both the high bandwidth group and the credible group) as shown in the Venn diagram above. In the next few weeks, I will continue to cover the science of influence, focusing on the issue of relevance and timing. As always, if you have questions, comments or feedback, then please let us know.
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