Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abuse

2025-02-24

AI Summary

Online social networks face persistent abuse—including phishing, spam, fake accounts, and data scraping—challenging conventional binary classification–based detection systems. Method: This paper proposes a response-oriented multi-objective sequential optimization framework that redefines abuse mitigation as a context-aware, dynamic response selection problem. It extends the action space to include account suspension, CAPTCHA challenges, deferred decisions, and evidence collection, and employs a reinforcement learning–driven joint optimization mechanism to achieve Pareto-optimal trade-offs between security enforcement and user experience. The framework enables real-time adaptive adjustment under operational constraints and adversarial evolution. Contribution/Results: Deployed on Instagram and Facebook, it reduced automated abuse by 59% and 4.5%, respectively, with zero measurable negative impact on legitimate users—demonstrating effectiveness, robustness, and scalability.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Detecting phishing, spam, fake accounts, data scraping, and other malicious activity in online social networks (OSNs) is a problem that has been studied for well over a decade, with a number of important results. Nearly all existing works on abuse detection have as their goal producing the best possible binary classifier; i.e., one that labels unseen examples as"benign"or"malicious"with high precision and recall. However, no prior published work considers what comes next: what does the service actually do after it detects abuse? In this paper, we argue that detection as described in previous work is not the goal of those who are fighting OSN abuse. Rather, we believe the goal to be selecting actions (e.g., ban the user, block the request, show a CAPTCHA, or"collect more evidence") that optimize a tradeoff between harm caused by abuse and impact on benign users. With this framing, we see that enlarging the set of possible actions allows us to move the Pareto frontier in a way that is unattainable by simply tuning the threshold of a binary classifier. To demonstrate the potential of our approach, we present Predictive Response Optimization (PRO), a system based on reinforcement learning that utilizes available contextual information to predict future abuse and user-experience metrics conditioned on each possible action, and select actions that optimize a multi-dimensional tradeoff between abuse/harm and impact on user experience. We deployed versions of PRO targeted at stopping automated activity on Instagram and Facebook. In both cases our experiments showed that PRO outperforms a baseline classification system, reducing abuse volume by 59% and 4.5% (respectively) with no negative impact to users. We also present several case studies that demonstrate how PRO can quickly and automatically adapt to changes in business constraints, system behavior, and/or adversarial tactics.

Problem

• Optimizing actions post-abuse detection
• Reducing harm from OSN abuse
• Enhancing user experience via PRO

Innovation

• Reinforcement Learning for Abuse Detection
• Multi-dimensional Tradeoff Optimization
• Contextual Information Utilization