Multiplayer Online Battle Arenas (MOBAs) have garnered a substantial player base worldwide. Nevertheless, the presence of noxious players, commonly referred to as "actors", can significantly compromise game fairness by exhibiting negative behaviors that diminish their team's competitive edge. Furthermore, high-level actors tend to engage in more egregious conduct to evade detection, thereby causing harm to the game community and necessitating their identification. To tackle this urgent concern, a partnership was formed with a team of game specialists from a prominent company to facilitate the identification and labeling of high-level actors in MOBA games. We first characterize the problem and abstract data and events from the game scene to formulate design requirements. Subsequently, ActorLens, a visual analytics system, was developed to exclude low-level actors, detect potential high-level actors, and assist users in labeling players. ActorLens furnishes an overview of players' status, summarizes behavioral patterns across three player cohorts (namely, focused players, historical matches of focused players, and matches of other players who played the same hero), and synthesizes key match events. By incorporating multiple views of information, users can proficiently recognize and label high-level actors in MOBA games. We conducted case studies and user studies to demonstrate the efficacy of the system.

翻译：多人在线战术竞技（MOBA）游戏已在全球范围内吸引了庞大的玩家群体。然而，被称为“演员”的有害玩家会通过展现降低团队竞争力的消极行为，严重破坏游戏公平性。高级演员更倾向于采取恶劣手段以逃避检测，从而对游戏社区造成危害，亟需对其进行识别。为应对这一紧迫问题，我们与某知名公司的游戏专家团队合作，共同推进MOBA游戏中高级演员的识别与标注工作。我们首先对问题进行建模，从游戏场景中抽象数据与事件以制定设计需求。随后，开发了可视化分析系统ActorLens，用于过滤低级演员、检测潜在高级演员，并辅助用户标注玩家。ActorLens提供玩家状态概览，总结三类玩家群体（聚焦玩家、聚焦玩家历史对战、同一英雄其他玩家对战）的行为模式，并合成关键对局事件。通过多视角信息整合，用户可高效识别并标注MOBA游戏中的高级演员。我们通过案例研究与用户研究验证了该系统的有效性。