  In location-based social networks (LBSNs), such as Gowalla and Waze, users
sense urban point-of-interest (PoI) information (e.g., restaurants' queue
length and real-time traffic conditions) in the vicinity and share such
information with friends in online social networks. Given each user's social
connections and the severe lags in disseminating fresh PoI to all users, major
LBSNs aim to enhance users' social PoI sharing by selecting $k$ out of $m$
users as hotspots and broadcasting their PoI information to the entire user
community. This motivates us to study a new combinatorial optimization problem
by integrating two urban sensing and online social networks. We prove that this
problem is NP-hard and also renders existing approximation solutions not
viable. Through analyzing the interplay effects between the sensing and social
networks, we successfully transform the involved PoI-sharing process across two
networks to matrix computations for deriving a closed-form objective,
{\color{black}which we find holds desirable properties (e.g., submodularity and
monotonicity).} This finding enables us to develop a polynomial-time algorithm
that guarantees a ($1-\frac{m-2}{m}(\frac{k-1}{k})^k$) approximation of the
optimum. Furthermore, we allow each selected user to move around and sense more
PoI information to share. To this end, we propose an augmentation-adaptive
algorithm, which benefits from a resource-augmented technique and achieves
bounded approximation, ranging from $\frac{1}{k}(1-\frac{1}{e})$ to
$1-\frac{1}{e}> 0.632$ by adjusting our augmentation factors. Finally, our
theoretical results are corroborated by our simulation findings using both
synthetic and real-world datasets across different network topologies.
