First Mover Effects
When an application handles quantitative measures based on user input, whether it's ratings or measuring participation by counting the number of contributions to a site, several issues arise-all resulting from bootstrapping of communities-that we group together under the term first-mover effects.
Early Behavior Modeling and Early-Ratings Bias
The first people to contribute to a site have a disproportionate effect on the character and future contributions of others. After all, this is social media, and people usually try to fit into any new environment. For example, if the tone of comments is negative, new contributors will also tend to be negative, which will also lead to bias in any user-generated ratings. See Ratings Bias Effects.
When an operator introduces user-generated content and associated reputation systems, it is important to take explicit steps to model behavior for the earliest users in order to set the pattern for those who follow.
Discouraging New Contributors
Take special care with systems that contain leaderboards when they're used either for content or for users. Items displayed on leaderboards tend to stay on the leaderboards, because the more people who see those items and click, rate, and comment on them, the more who will follow suit, creating a self-sustaining feedback loop.
This loop not only keeps newer items and users from breaking into the leaderboards, it discourages new users from even making the effort to participate by giving the impression that they are too late to influence the result in any significant way. Though this phenomenon applies to all reputation scores, even for digital cameras, it's particularly acute in the case of simple point-based karma systems, which give active users ever more points for activity so that leaders, over years of feverish activity, amass millions of points, making it mathematically impossible for new users to ever catch up.