  Live online social broadcasting services like YouTube Live and Twitch have
steadily gained popularity due to improved bandwidth, ease of generating
content and the ability to earn revenue on the generated content. In contrast
to traditional cable television, revenue in online services is generated solely
through advertisements, and depends on the number of clicks generated. Channel
owners aim to opportunistically schedule advertisements so as to generate
maximum revenue. This paper considers the problem of optimal scheduling of
advertisements in live online social media. The problem is formulated as a
multiple stopping problem and is addressed in a partially observed Markov
decision process (POMDP) framework. Structural results are provided on the
optimal advertisement scheduling policy. By exploiting the structure of the
optimal policy, best linear thresholds are computed using stochastic
approximation. The proposed model and framework are validated on real datasets,
and the following observations are made: (i) The policy obtained by the
multiple stopping problem can be used to detect changes in ground truth from
online search data (ii) Numerical results show a significant improvement in the
expected revenue by opportunistically scheduling the advertisements. The
revenue can be improved by $20-30\%$ in comparison to currently employed
periodic scheduling.
