  We investigate the novel problem of voting-based opinion maximization in a
social network: Find a given number of seed nodes for a target campaigner, in
the presence of other competing campaigns, so as to maximize a voting-based
score for the target campaigner at a given time horizon.
  The bulk of the influence maximization literature assumes that social network
users can switch between only two discrete states, inactive and active, and the
choice to switch is frozen upon one-time activation. In reality, even when
having a preferred opinion, a user may not completely despise the other
opinions, and the preference level may vary over time due to social influence.
To this end, we employ models rooted in opinion formation and diffusion, and
use several voting-based scores to determine a user's vote for each of the
multiple campaigners at a given time horizon.
  Our problem is NP-hard and non-submodular for various scores. We design
greedy seed selection algorithms with quality guarantees for our scoring
functions via sandwich approximation. To improve the efficiency, we develop
random walk and sketch-based opinion computation, with quality guarantees.
Empirical results validate our effectiveness, efficiency, and scalability.
