In order to do so, we apply social network analysis to
network representations of the participation structures,
identifying both the prestige of the users and of the tasks.
We find that a task's prestige on Taskcn can slightly hinder
people's participation, hinting that tasks where even expert
users lose are attempted by fewer users on average. On the
other hand, tasks where many users participate (making the
task central in the network) tend to have lower average
participant expertise levels. These two observations imply
that "peripheral" tasks attract more prestigious participants,
while popular tasks attract on average less discriminating,
and less expert, participants. However this does not mean
that popular tasks are doomed to attract only mediocre
solutions. While the average participant may have a lower
expertise level, having more solutions submitted and in
particular, having solutions submitted by winners in other
tasks, significantly improves the chance that the winner
will be more expert in Taskcn. Finally, we find a user's
chance of winning depends on the number of submitters for
the task, her track record and her prestige level