Community health/Metrics

From Strategic Planning
Jump to navigation Jump to search

We'd like to track community health going forward, which is multi-faceted and doesn't lend itself to easy measures. This page lists potential metrics based on the substantial discussion on this wiki.

In general, it would be really useful if we could slice up the dataset. Imagine that we could find out that we're getting more editors, but only for articles about music! Then we could look at other stats around music articles, and figure out what's helping that music sub-community grow while other parts of Wikipedia are stagnating. Numbers are just data. But when you can compare them to something, you can understand what the heck is really going on.

Editor Metrics

Total number of active contributors

There are currently about 100,000 active contributors in all projects. The total number of active editors started to decline in 2007, after which it stabilized, and has been flat since. We don't know what an appropriate number of editors might be for the Wikimedia projects: for example, it may be that the mature projects require a smaller number of editors once they reach maturity, than they did in heavy article-growth mode. Text Letters in the wikimedia requires more bold in sie. Had it been so in all the computer literature eye sight of many editors, citiens prevailing in India, Brazil would have been much brighter. For this alone the wikimedia should invent more ways and means to conduct eyesight camps in interior parts of India in each state in the name of Wikimedia.This provides sympathy and empathy om Wikimedia, increases its circulation base, knowledge base, mobile net work database.

Retention of active contributors

Let's say there are two subcategories of editor. 1) Let's say half of editors are permanently committed to the projects. They will take wikibreaks due to the ebb and flow of their outside-Wikimedia obligations, but as a group let's say their numbers are stable. And 2) let's say half of editors are what we might call "life-stage" editors: they join us while they are in post-secondary education, edit for let's say five years total, then stop editing as they shift their focus to careers and family. That would suggest that every year we would lose 10% of editors, and that 10% would be replaced by new "life-stage" editors coming in. If that construct were true, we would expect to see a "loss" of 10% of editors annually -- and as long as they were being replaced by 10% new people, we would likely consider that perfectly fine.

So the first thing we need to do is establish a baseline -- figure out what is actually happening today. If there's 10% turnover/churn annually, that by itself doesn't tell us much. But if the 10 goes to 20, that would be cause for investigation. And if the 10 goes to 5, that would probably be a good sign.

Other metrics:

  • Users inactive for 30, 60, 90, 120 days
  • Previously active users who are newly inactive

Editor engagement/satisfaction

The simplest way to measure community health may simply be to ask people (via a regular survey) how engaged/satisfied they are feeling in their work on the projects.

We probably don't want to measure "happiness" -- because the purpose of the Wikimedia projects isn't to make editors happy, and in theory at least, editors could be super-happy and yet not very productive. (If for example they decided to have wiki-parties all the time, and they formed great friendships and had lots of fun, but didn't write any articles.) But you do want to measure overall engagement/satisfaction. This is true in organizations too -- HR departments have learned over time not to measure employee happiness, but rather to measure employee engagement or satisfaction.)

Note that the Former Contributors Survey Results actually showed that some of the most dissatisfied editors are actually our most engaged. Not sure why that is. Maybe it's pathological OCD. Maybe it's that more engaged editors are willing to accept higher levels of BS. But the real point: we're not going to be able to measure health by checking the population for satisfaction and growth. Dissatisfaction and stagnation are the symptom, not the diagnosis.

Again, we need a baseline here. Let's imagine that at any given moment in time maybe 1% of active Wikimedia editors are unconstructive, don't share our mission and goals, don't really understand the work we're trying to do, and generally are unhappy because they're not aligned with us. They will leave us soon, but they haven't left us yet. And let's further imagine that at any given moment in time maybe 15% of active Wikimedia editors are feeling angry or unhappy about a dispute they're engaged in at that particular moment, although they are otherwise generally satisfied. And let's further imagine that 20% of the world is always going to report feeling dissatisfied, because that's just the kind of people they are. In that construct, we'd expect that a "normal" level of self-reported dissatisfaction would be 36%. So if we found ourselves with a 36% dissatisfied baseline, we would know that we're never going to get to 0% dissatisfied, but we might take steps aimed at trying to help some of the 15% be less situationally frustrated. In this construct, if we could get to something like 25% dissatisfied, that would be good progress.

It won't be easy to parse out the "bad fit" people from the situationally-dissatisfied from the constitutionally-dissatisfied, but that's what we should be aiming to do. And we should go in with the understanding that we'll never achieve 0% dissatisfaction, but that we should be aiming to trend towards less.

Editor demographics

Broadly, we want editor demographics to look more like the general population. I don't think we should aspire to map exactly against gen-pop, because I don't think that would be realistic or even desirable.

Basically:

  • Some demographic skew is inevitable, and outside our control. For example, people in poor countries will always edit less --on the whole-- than people in rich countries, because people in rich countries have more leisure time, better connectivity and equipment, higher education and literacy levels and so forth. Similarly, women will likely always edit less than men, because they have less free time. We should still aspire to make it easier for those groups to edit, but they will likely never achieve representation-on-Wikimedia proportionate to their representation in the general population.
  • Some demographic skew is --at least partly-- open to influence by us. For example, we have speculated that women would be likelier to edit if they were invited and thanked, and if there were increased opportunities for face-to-face interaction. By thanking, by inviting, by having meet-ups and conferences, and/or by specific targeted outreach, we would likely be able to attract more women.
  • I hesitate to say this because it risks sounding elitist, but to a certain extent we don't want gen-pop representation. I sometimes think the most important defining feature of Wikimedians is their unusually high intelligence. Jimmy has sometimes posed the rhetorical question: what kind of person edits an encyclopedia in their spare time, for fun? (Answer: smart geeks.) By that very fact, we know that Wikimedians are generally extremely intelligent. And we know that Wikimedia biases to encourage smart people -- a large part of reputation here is driven by doing work that visibly manifests intelligence, or is dependent on being intelligent. So, it makes sense to me that editors might skew better-educated-than-average, more professional-career-than-average, maybe even higher-income-earning-than-average. (I want to say here: I'm not saying that less-educated, less-professional-career, lower-income people are by definition less intelligent -- for many individuals for many reasons, that is of course not even remotely true. But I am saying that if Wikimedians are somewhat better-educated, more likely to be in professional careers, and higher income-earning, that shouldn't surprise us, and --as a fact by itself-- it shouldn't necessarily trouble us.)

So upshot on demographics: I think that we do not want to map identically to gen-pop. But I do think we should aspire to map somewhat more closely to gen-pop, particularly in the areas where we see a huge gap --- e.g., gender. I think the projects will be better and richer and more comprehensive if we have input from people who are currently underrepresented. So I think we need to use the UNU-Merit data as our baseline, and track change-over-time, with the goal of coming somewhat closer to gen-pop than we currently are.

Other Metrics

  • Number of new contributors

Article Metrics

Abandoned Edits

How many editors abandon edits without pushing save? (Obviously some amount is normal. But, it would be a huge sign of improvement to get from 50% abandonment down to 40%, or what not.) That would measure how easy and convenient people are finding it to edit.

Reverted Edits

How many edits are reverted? Again, some amount is normal. But a revert shows a problem on two ends. On one hand, it shows a community that is hostile to change. On the other hand, it shows an influx of editors who may be making inappropriate changes that upset community norms. Whose fault is it -- the reverter or the revertee? It almost doesn't matter. As much as reverting is natural, we know that too much is a bad sign.

Dispute Resolution Activity

Drama usually goes there. Drama will probably grow with the population (more people means more disputes), but if it's growing faster than the population then we have a problem with community health.

Other Metrics

  • Articles by edits (trending)
  • Number of Reverts
  • Number of edits
  • Number of new articles
  • Number of new files
  • Ratio of human to bot edits
  • Number of orphaned articles / ratio of orphaned articles to total articles
  • Number of article deletions

Admin Metrics

  • Number of edits by admins
  • Number of blocks
  • Admins with most blocks
  • Number of speedy deletes
  • Number of posts to ANI
  • Number of new admins
  • Highly active admins
  • Admins inactive for 30, 60, 90, 120 days
  • Number of full process deletes
  • Number of reports to AIV
  • Global blocks
  • Number of steward activities
  • Number of permissions changes
  • active users to admin ratio

Help Metrics