B Butler found* that large, active, groups were able to attract more members. However, they also lost more. While more activity helped retain members, it was not as powerful as group size.
For a Community manager, my takeaways were:
- Spend your energy not in helping large groups communicate but breaking them down to smaller sizes.
- If you are looking to have a large churn of members, then you want to go big. A more long term membership would stay small.
What sorts of posts elicit replies?
1. Not being a newcomer.
2. Post on topic.
3. Use first person, I, and convey what you are thinking and feeling.
4. Ask a question.
5. Use simple language.
When Dr. Brian Butler, et al, surveyed about 3000 listserv members about who does what work in a community, their found they correlate to the benefits individuals get from the community. This knowledge can be a big help to Community Managers.
Five findings that struck me and what they may prescribe-
Owners spent more time creating content than reading it. Knowing this a CM should be aware of this potential blind spot and be sure to keep up with what the community is saying.
Nice study done by B Irwin shows that having a partner that exercises with you at the same time, even if online, is significantly better than going it alone. (Like twice as better!!)
This is a big win for why one would want to schedule simultaneous events. Also, if I have members that want/need to do more on an individual task, it is great to know I can bring in an online partner and get some well known group dynamics going.
Another aspect of this paper is that having a stronger online partner can help motivate the other.
Aerobic Exercise Is Promoted when Individual Performance Affects the Group: A Test of the Kohler Motivation Gain Effect B Irwin Annals of Behavioral Medicine Volume 17 / 1995 – Volume 43 / 2012 Link to paper Link to article.
S Chellappan took a bit of a different tact when he looked at internet usage via network stats, then compared that to where his users fell on a depressive scale. He found that there were different patterns for those ranked higher in depression. 1
The paper interpreted the stats as showing depressed students had:
- More video and gaming use.
- More switching between applications.
- Greater peer to peer file sharing.
- Greater chatting.
- More email usage, including merely checking for messages.