  Online reviews provide product evaluations for customers to make decisions.
Unfortunately, the evaluations can be manipulated using fake reviews ("spams")
by professional spammers, who have learned increasingly insidious and powerful
spamming strategies by adapting to the deployed detectors. Spamming strategies
are hard to capture, as they can be varying quickly along time, different
across spammers and target products, and more critically, remained unknown in
most cases. Furthermore, most existing detectors focus on detection accuracy,
which is not well-aligned with the goal of maintaining the trustworthiness of
product evaluations. To address the challenges, we formulate a minimax game
where the spammers and spam detectors compete with each other on their
practical goals that are not solely based on detection accuracy. Nash
equilibria of the game lead to stable detectors that are agnostic to any mixed
detection strategies. However, the game has no closed-form solution and is not
differentiable to admit the typical gradient-based algorithms. We turn the game
into two dependent Markov Decision Processes (MDPs) to allow efficient
stochastic optimization based on multi-armed bandit and policy gradient. We
experiment on three large review datasets using various state-of-the-art
spamming and detection strategies and show that the optimization algorithm can
reliably find an equilibrial detector that can robustly and effectively prevent
spammers with any mixed spamming strategies from attaining their practical
goal. Our code is available at https://github.com/YingtongDou/Nash-Detect.
