the 90-9-1 rule for participation in an online community

According to Jakob Nielsen‘s Alertbox, in most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.

A tiny minority of users usually accounts for a disproportionately large amount of the content and other system activity. This phenomenon of participation inequality was first studied in depth by Will Hill of Bell Communications Research in the early 1990s.

 When you plot the amount of activity for each user, the result is a Zipf curve, which shows as a straight line in a log-log diagram.

User participation often more or less follows a 90-9-1 rule:

  • 90% of users are lurkers (i.e., read or observe, but don’t contribute).
  • 9% of users contribute from time to time, but other priorities dominate their time.
  • 1% of users participate a lot and account for most contributions: it can seem as if they don’t have lives because they often post just minutes after whatever event they’re commenting on occurs.

Early Inequality Research

Before the Web, researchers documented participation inequality in media such as Usenet newsgroups, CompuServe bulletin boards, Internet mailing lists, and internal discussion boards in big companies. A study of more than 2 million messages on Usenet found that:

  • 27% of postings were from people who posted only a single message.
  • 25% were posted by the most active 3%.

Obviously, if you want to assess the “feelings of the community” it’s highly unfair if one subgroup’s 19,000 members have the same representation as another subgroup’s 580,000 members. More importantly, such inequities would give you a biased understanding of the community, because many differences almost certainly exist between people who post a lot and those who post a little. And you would never hear from the silent majority of lurkers.

Inequality on the Web

Blogs have even worse participation inequality than is evident in the 90-9-1 rule that characterizes most online communities. With blogs, the rule is more like 95-5-0.1.

There are about 1.1 billion Internet users, yet only 55 million users (5%) have blogs, according to Technorati. With only 1.6 million postings per day and some people posting multiple times per day, only 0.1% of users post daily.

Wikipedia inequalities:  More than 99% of users are lurkers. According to Wikipedia’s “about” page, it has only 68,000 active contributors, which is 0.2% of the 32 million unique visitors it has in the U.S. alone. Wikipedia’s most active 1,000 people — 0.003% of its users — contribute about two-thirds of the site’s edits. Wikipedia is thus even more skewed than blogs, with a 99.8-0.2-0.003 rule.

Amazon.com, for example had sold thousands of copies of a book that had only 12 reviews, meaning that less than 1% of customers contribute reviews. At the time this was written, 167,113 of Amazon’s book reviews were contributed by just a few “top-100” reviewers; the most prolific reviewer had written an incredible 12,423 reviews.

Downsides of Participation Inequality

Visualization of the amount of contributions from different user segments

Is Participation Inequality Unfair?

Participation inequality is not necessarily unfair because if lurkers want to contribute, they are usually allowed to do so.

But it is not representative of average Web users. On any given user-participation site, you almost always hear from the same 1% of users, who almost certainly differ from the 90% you never hear from. This can cause trouble for several reasons:

  • Customer feedback. If your company looks to Web postings for customer feedback on its products and services, you’re getting an unrepresentative sample.
  • Reviews. Similarly, if you’re a consumer trying to find out which restaurant to patronize or what books to buy, online reviews represent only a tiny minority of the people who have experiences with those products and services.
  • Politics. If a party nominates a candidate supported by the “netroots,” it will almost certainly lose because such candidates’ positions will be too extreme to appeal to mainstream voters. Postings on political blogs come from less than 0.1% of voters, most of whom are hardcore leftists (for Democrats) or rightists (for Republicans).
  • Search. Search engine results pages (SERP) are mainly sorted based on how many other sites link to each destination. When 0.1% of users do most of the linking, we risk having search relevance get ever more out of whack with what’s useful for the remaining 99.9% of users. Search engines need to rely more on behavioral data gathered across samples that better represent users, which is why they are building Internet access services.
  • Signal-to-noise ratio. Discussion groups drown in flames and low-quality postings, making it hard to identify the gems. Many users stop reading comments because they don’t have time to wade through the swamp of postings from people with little to say.

Skewed Lurker–Contributor Ratio for Non-Profit Social Network

According to the Washington Post, the “Causes” application on Facebook had 25 million users in April 2009, but only 185,000 had given a donation, even though the application offers the ability to give to 179,000 different non-profit organizations.

Thus, social networking for charity fundraising has a 99.3% lurkers and 0.7% contributors rule — even more skewed than the other participation inequalities we have seen. The data doesn’t say how many of the 0.7% of users who donated have been frequent contributors, but most likely it’s less than 1/10, meaning that the full rule would look something like 99-1-0.

This finding has three implications:

  • Facebook is just another collaborative environment, in which long-established laws for online communities hold.
  • Donating money is a stronger form of action than writing content, so extremely strong participation inequality should be expected.
  • Research on the user experience of donating to charities online found that most non-profits don’t provide the information users want before they’re willing to donate, or don’t provide it  in a sufficiently Web-oriented way.

Can You Overcome Participation Inequality?

To dealing with participation inequality is to recognize that it will always be there, and has existed in every online community and multi-user service that has ever been studied.

Your only choice is in how you manage the inequality curve’s angle. Are you going to have the “usual” 90-9-1 distribution, or the more radical 99-1-0.1 distribution common in some social websites? Can you achieve a more equitable distribution of, say, 80-16-4?

Some ways to better equalize participation include:

  • Make it easier to contribute. The lower the overhead, the more people will contribute. Netflix lets users rate movies by clicking a star rating, which is much easier than writing a natural-language review.
  • Make participation a side effect. Users can participate with no effort if you can make their contributions a side effect of something else they’re doing. For example, Amazon’s “people who bought this book, bought these other books” recommendations are a side effect of people buying books in which your book preferences are automatically entered into the system. Will Hill coined the term read wear for this type of effect because, like a dogeared page in a book, the very activity of reading or using something will register.
  • Edit, don’t create. Editing a pre-existing template is more enticing and has a gentler learning curve than facing the horror of a blank page. In avatar-based systems like Second Life, for example, most users modify standard-issue avatars rather than create their own.
  • Reward — but don’t over-reward — participants. Rewarding people for contributing will help motivate users who have lives outside the Internet, and thus will broaden your participant base. Money is always good, but you can also give preferential treatment such as discounts, advance notice of new offerings, or recognition, such as gold stars on their profiles. But too much reward to the most active participants will only encourage them to dominate the system even more.
  • Promote quality contributors. If you display all contributions equally, then people who post only when they have something important to say will be drowned out by the torrent of material from the hyperactive 1%. Instead, give extra prominence to good contributions and to contributions from people who’ve proven their value, as indicated by their reputation ranking.

Your website’s design undoubtedly influences participation inequality for better or worse. Being aware of the problem is the first step to alleviating it, and finding ways to broaden participation will become even more important as the Web’s social networking services continue to grow.

References

Laurence Brothers, Jim Hollan, Jakob Nielsen, Scott Stornetta, Steve Abney, George Furnas, and Michael Littman (1992): “Supporting informal communication via ephemeral interest groups,”Proceedings of CSCW 92, the ACM Conference on Computer-Supported Cooperative Work(Toronto, Ontario, November 1-4, 1992), pp. 84-90.

William C. Hill, James D. Hollan, Dave Wroblewski, and Tim McCandless (1992): “Edit wear and read wear,” Proceedings of CHI’92, the SIGCHI Conference on Human Factors in Computing Systems (Monterey, CA, May 3-7, 1992), pp. 3-9.

Steve Whittaker, Loren Terveen, Will Hill, and Lynn Cherny (1998): “The dynamics of mass interaction,” Proceedings of CSCW 98, the ACM Conference on Computer-Supported Cooperative Work (Seattle, WA, November 14-18, 1998), pp. 257-264.

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