  This abstract proposes an approach towards goal-oriented modeling of the
detection and modeling complex social phenomena in multiparty discourse in an
online political strategy game. We developed a two-tier approach that first
encodes sociolinguistic behavior as linguistic features then use reinforcement
learning to estimate the advantage afforded to any player. In the first tier,
sociolinguistic behavior, such as Friendship and Reasoning, that speakers use
to influence others are encoded as linguistic features to identify the
persuasive strategies applied by each player in simultaneous two-party
dialogues. In the second tier, a reinforcement learning approach is used to
estimate a graph-aware reward function to quantify the advantage afforded to
each player based on their standing in this multiparty setup. We apply this
technique to the game Diplomacy, using a dataset comprising of over 15,000
messages exchanged between 78 users. Our graph-aware approach shows robust
performance compared to a context-agnostic setup.
