Health Factor 5: Interaction

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This is the fifth blog in the miniseries on the Health Factors of CHI. Other blogs already in this miniseries are:

1. Traffic
2. Content
3. Members
4. Liveliness

Last time we talked about the most troublesome predictive health factor: Liveliness. This time we will discuss Interaction.

The Interaction Health Factor

When you are able to create a lively community, the hard work is half done because by definition a lively community has solved a difficult conundrum of participatory media: How do you get people to participate? However, in a healthy community, it is not enough just to participate. There must be interaction with other users. Otherwise, where is the social of social media?

There are two important dimensions to interaction:

1. The amount of conversation you have with a particular user.
2. The number of different users you've communicated with.

Clearly, the more you talk to a user, the more interaction you have with that user, but talking to different users also increases the level of interactions within your community. In fact, the latter has a greater effect on the overall health of the community. The interaction health factor measures the average number of unique participants per topic weighted by the amount of conversation between them. Thus, this health factor provides an estimate of the average number of members a potential user is likely to interact with and the estimated amount of messages that will be exchanged between them. Accordingly, this health factor may be interpreted as the expected scope of the engagement for potential visitors.

Different Modes of Interactions on the Lithium Platform

Because different communities have vastly different kinds of interactions, the interaction health factors also vary greatly across communities. For example, the dominant mode of interaction of a support community consists of a troubled user asking a question and a knowledgeable user answering the question. This kind of interaction usually involves few users with relatively short diagnostic dialogs before arriving at the solution, so support communities tend to have a steady and modest value for the interaction health factor. In contrast, the dominant mode of interaction for an enthusiast community often involves extended discussions among many users. Also, because the topic and amount of discussion is usually event-driven, enthusiast communities tend to have a higher, but more volatile value for their interaction health factor. B2B and internal communities also tend to have lower level of interactions than that of enthusiast communities.

The Current and Future of Interaction

Besides the difference in community purpose, the different web applications, such as forums, blogs, ideas, and tribal knowledge base (TKB), also have drastically different modalities of interaction. However, the current health factors and CHI were developed based on analyses of primarily forum interactions. Blogs, Ideas, and TKB are relatively new products in our platform that do not have 10 years of historical data. Consequently, the expected level of healthy interaction in the current algorithm may be too stringent for these new interaction modalities. As a result, the interaction health factor for communities that use these new applications may appear slightly lower than expected. We have made note of this issue and we will address it in the next revision of our formulae.

I hope this blog gave you a better understanding of interaction and addressed the concerns you may have concerning this health factor. Next time, we will move on to the last health factor, responsiveness. Watch for updates at mich8elwu.