  Online games are dynamic environments where players interact with each other,
which offers a rich setting for understanding how players negotiate their way
through the game to an ultimate victory. This work studies online player
interactions during the turn-based strategy game, Diplomacy. We annotated a
dataset of over 10,000 chat messages for different negotiation strategies and
empirically examined their importance in predicting long- and short-term game
outcomes. Although negotiation strategies can be predicted reasonably
accurately through the linguistic modeling of the chat messages, more is needed
for predicting short-term outcomes such as trustworthiness. On the other hand,
they are essential in graph-aware reinforcement learning approaches to predict
long-term outcomes, such as a player's success, based on their prior
negotiation history. We close with a discussion of the implications and impact
of our work. The dataset is available at
https://github.com/kj2013/claff-diplomacy.
