Absolutely
Let's start with just one observation from network theory. Let's say N is the number of people the community. Then the number of possible relationships between 2 users in a community is proportional to N-squared (more precisely, N(N-1)/2).
- So for a 1,000 person community there are ~500,000 possible relationships between people. For a 100,000 person community, there are ~5 billion possible relationships.
- That is to say, growing a community by 100x actually increases the density of possible relationships by 10,000x.
In reality this doesn't happen since users don't actually have the time or interest to form a relationship with everyone else in a community, but the N-squared proportionality gives rise to some dramatic dynamics so it's a helpful model to consider.
What are some possible effects?
Terrorist effect - All it takes is one person to ruin your day. If users are able to post content or comments publically, scaling the user base can dramatically increase the incidence of nastiness on a site. For example, let's say 1 out of every 1000 is a troll, and posts nasty content once a year. With 1,000 users your community has to deal with a public troll on average once a year. With 100,000 users you have to deal with a troll once every 3 days.
Content inundation - As the user base grows, user attention budget doesn't grow. So if an average user posts new content once every 10 days and has enough time to digest a maximum 100 pieces of content a day then:
- For a 1,000 user community, there are 100 pieces of content produced every day, which exactly fits the attention budget for each user. i.e. supply of content meets capacity to digest content.
- For a 100,000 user community, there are 10,000 pieces of content produced very day, which is 100x the amount of content users can digest in a day. So now the dynamics change a lot....concepts like curation, crowdsourcing, filtering, and search become important to ensure a balance between supply and demand for content.
Accretion effects - User bases rarely grow linearly. They tend to grow exponentially. This has some important effects because the earliest users will become very different from the newest users. Some examples:
- In Yelp, a brand new restaurant with no reviews has a hard time competing with a 5-star restaurant with 1,000 reviews. The restaurants may be of identical quality, but one has a tremendous advantage of seniority.
- In StackExchange, the earliest users who answered the simplest and most common questions about Javascript have a tremendous advantage over new users who are trying to answer less common questions.
- On Twitter, senior musicians who have accumulated huge follower bases have a tremendous advantage over new musicians who start with only a handful of followers.
Heterogeneity effects - Early user bases tend to follow more narrow cultural behaviors (e.g. because they are from the founder's locale, age group, friends/family, etc). As user bases reach tipping point and expand rapidly, the demographics often broaden (standard deviation may or may not stay the same or even narrow, but range broadens with N). This can change the complexion of a community, e.g. an environmental awareness site may start in San Francisco where the issues are easy to discuss, but as the community grows may need to deal wtih language, culture, and political issues in India, Japan, Germany, etc.
Herd bias - For platforms that use ratings or show views/votes/shares/likes, this is a well known bias. For example, if 10,000 people immediately condemn a suspected murderer online, the next user who opens his news feed is more likely to also condemn the murderer rather than read an article and form his own unbiased opinion.
Any of these dynamics may follow linear, quadratic, or exponential dynamics as a site grows so a lot of social networking site growth concerns itself with adopting moderation, rules, curation, filtering, search, and normative/equalizing dynamics in order to counteract negative network effects and support positive effects.
This is only a partial list of scaling dynamics with communities, but is hopefully enough to illustrate that behavior can vary quite dramatically as sites scale.
