CommunityCompare: Visually Comparing Communities for Online Community Leaders in the Enterprise

We introduce CommunityCompare, a visual analytic system to enable online community leaders to make sense of their community’s activity with comparisons.CommunityCompare

Emma, an online community leader, wants to learn how she might increase member visits (views) by looking at example communities that are doing well on this metric. Each line in the main chart represents a community; Emma’s community is highlighted in orange. She wants to find example communities that have similar size and age compared to her community, and at the same time have more views than hers. To achieve this, she creates two visual filters on the community size and community age axes around her community, and creates another filter on the views axis above her community. She now sees 8 communities shown in the main chart and listed in the Select Communities widget. She wants to know what has been posted in these communities, so she clicks on the name of each community in the list to view their Most Valuable Posts below. She sees several examples of posts that give her new ideas for discussion topics in her community. She then saves the list of communities and decides to contact the leaders to learn more about their activities.

We motivate and inform the system design with formative interviews of community leaders. From additional interviews, a field deployment, and surveys of leaders, we show how the system enabled leaders to assess community performance in the context of other comparable communities, learn about community dynamics through data exploration, and identify examples of top performing communities from which to learn.

For more, see our full paper, CommunityCompare: Visually Comparing Communities for Online Community Leaders in the Enterprise.
Anbang Xu, University of Illinois at Urbana-Champaign
Jilin Chen, IBM Research – Almaden
Tara Matthews, IBM Research – Almaden
Michael Muller, IBM Research – Cambridge
Hernan Badenes , IBM Research – Almaden

5 thoughts on “CommunityCompare: Visually Comparing Communities for Online Community Leaders in the Enterprise

  1. Thanks for posting, this is interesting work.

    I was wondering, did you get a sense of how much of what makes for a successful community can be captured by quantitative metrics and looking at posts from those communities? On one hand, I can definitely see how a system like this could give a community leader inspiration on ways to increase engagement by users. On the other hand, some elements of what makes for a successful community might have to do with hidden factors, tied up in the personalities of community members, their passion for their work, etc. I was just wondering if you got a sense of how much of a role these latter factors play, because they seem more difficult to get at.

    • Hi Ben, Thanks for the question! There is currently no consensus on whether qualitative or quantitative measures are better to access community performance. But I do think qualitative features like the leader’s popularity, organizational support, or the community’s topic area can affect community performance. It would be interesting to explore how to automatically categorize such qualitative features and integrate them into CommunityCompare.

  2. Looks like a really great tool Anbang.

    If you go to http://www.flickr.com/photos/bnegelmann/3829689942/ you’lll see my attempt in 2009 to create simple tool for community managers to benchmark their performance vs a constant across communities. Using the ratio of 1:10 contributors to readers as this constant at least in theory this tool would allow community managers to see if their community content was performing as expected.

    Too many contributors to readers means an echo-chamber effect is taking place, with little interest from readers. A ratio illustrating too many readers to contributors means too few people are dominating the discussions.

    Would be neat to see if your data upheld the value of my tool, or if it disproves then why if the ratio of contributors to readers is pretty stable?

    • Hi Stuart, Thank you for sharing the interesting graph. The 90–9–1 principle can definitely serve as a baseline for assessing the participation rate of a community, but in practice it is not always the norm. If we treat this baseline as a cumulative measure (data aggregated from the beginning of a community), many communities in our dataset have higher contributor rates than 10%; however, if we look at the data in a time window, many of them have lower contributor rates than 10%. Also, the contributor rate could be affected by other factors such as community size (e.g. small communities might have high contributor rates), community types and community goals.

      It is a very good point that too many contributors might spoil the content. It would be interesting to examine the negative effects of high contributor rates.

  3. Anbang,
    I note from your article that you have performed a field deployment of the CommunityCompare system. I was curious to know if you have applied the system within a large scale corporate social business enterprise with an extensive and established community network, or if you would be interested in discussing such an opportunity. Please feel free to reach out to me directly at gloria.burke@unisys.com

Comments are closed.