  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.
