Towards an Exploratory Visual Analytics System for Griefer Identification in MOBA Games

TL;DR

This paper introduces GrieferLens, an interactive visual analytics system designed to assist game developers in identifying griefers in MOBA games by analyzing player behavior and match events, addressing data annotation challenges.

Contribution

The paper presents a novel visual analysis tool that facilitates efficient detection and labeling of griefers in MOBA games, improving upon manual review methods.

Findings

Enhanced detection efficiency for griefers

Improved accuracy in behavior analysis

Supports fairer gameplay environments

Abstract

Multiplayer Online Battle Arenas (MOBAs) have gained a significant player base worldwide, generating over two billion US dollars in annual game revenue. However, the presence of griefers, who deliberately irritate and harass other players within the game, can have a detrimental impact on players' experience, compromising game fairness and potentially leading to the emergence of gray industries. Unfortunately, the absence of a standardized criterion, and the lack of high-quality labeled and annotated data has made it challenging to detect the presence of griefers. Given the complexity of the multivariant spatiotemporal data for MOBA games, game developers heavily rely on manual review of entire game video recordings to label and annotate griefers, which is a time-consuming process. To alleviate this issue, we have collaborated with a team of game specialists to develop an interactive visual analysis interface, called GrieferLens. It overviews players' behavior analysis and synthesizes their key match events. By presenting multiple views of information, GrieferLens can help the game design team efficiently recognize and label griefers in MOBA games and build up a foundation for creating a more enjoyable and fair gameplay environment.