  Crowdsourcing services, such as Waze, leverage a mass of mobile users to
learn massive point-of-interest (PoI) information while traveling and share it
as a public good. Given that crowdsourced users mind their travel costs and
possess various preferences over the PoI information along different paths, we
formulate the problem as a novel non-atomic multi-path routing game with
positive network externalities among users in social information sharing. In
the absence of any incentive design, our price of anarchy (PoA) analysis shows
that users' selfish routing on the path with the lowest cost will limit
information diversity and lead to $PoA = 0$ with an arbitrarily large
efficiency loss from the social optimum. This motivates us to design effective
incentive mechanisms to remedy while upholding desirable properties such as
individual rationality, incentive compatibility, and budget balance for
practical users. Without requiring a specific user's path preference, we
present a non-monetary mechanism called Adaptive Information Restriction (AIR)
that reduces non-cooperative users' access to the public good as an indirect
penalty, which meets all the desirable properties. By meticulously adapting
penalty fractions to the actual user flows along different paths, our AIR
achieves non-trivial $PoA = \frac{1}{4}$ with low complexity $O(k\log k+\log
m)$, where $k$ and $m$ denote the numbers of involved paths and user types,
respectively. If the system can further enable pricing for users, we then
propose a new monetary mechanism called Adaptive Side-Payment (ASP), which
adaptively charges and rewards users according to their chosen paths,
respectively. Our ASP mechanism successively achieves a $PoA = \frac{1}{2}$
with even reduced complexity $O(k\log k)$. Finally, our theoretical findings
are well corroborated by our experimental results using a real-world public
dataset.
