Context. The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions (e.g., e-sports or speed-running) or for making business by entertaining others (e.g., streamers). The latter daily produce a large amount of gameplay videos in which they also comment live what they experience. But no software and, thus, no video game is perfect: Streamers may encounter several problems (such as bugs, glitches, or performance issues) while they play. Also, it is unlikely that they explicitly report such issues to developers. The identified problems may negatively impact the user's gaming experience and, in turn, can harm the reputation of the game and of the producer. Objective. In this paper, we propose and empirically evaluate GELID, an approach for automatically extracting relevant information from gameplay videos by (i) identifying video segments in which streamers experienced anomalies; (ii) categorizing them based on their type (e.g., logic or presentation); clustering them based on (iii) the context in which appear (e.g., level or game area) and (iv) on the specific issue type (e.g., game crashes). Method. We manually defined a training set for step 2 of GELID (categorization) and a test set for validating in isolation the four components of GELID. In total, we manually segmented, labeled, and clustered 170 videos related to 3 video games, defining a dataset containing 604 segments. Results. While in steps 1 (segmentation) and 4 (specific issue clustering) GELID achieves satisfactory results, it shows limitations on step 3 (game context clustering) and, above all, step 2 (categorization).

翻译：背景。游戏产业近年来持续增长。每天有数百万人游玩电子游戏，不仅是作为爱好，还用于职业竞赛（如电子竞技或竞速通关），或通过娱乐他人来开展业务（如主播）。后者每天制作大量游戏视频，并在其中实时解说自身体验。然而，没有任何软件（包括电子游戏）是完美的：主播在游玩时可能遇到多种问题（如缺陷、故障或性能问题）。此外，他们不太可能主动向开发者报告这些问题。已识别的问题可能对用户的游戏体验产生负面影响，进而损害游戏及其开发商的声誉。目的。本文提出并实证评估了GELID方法，该方法通过以下方式从游戏视频中自动提取相关信息：(i) 识别主播经历异常的视频片段；(ii) 根据异常类型（如逻辑或呈现）对其进行分类；(iii) 根据出现背景（如关卡或游戏区域）进行聚类；(iv) 根据具体问题类型（如游戏崩溃）进行聚类。方法。我们为GELID的步骤2（分类）手动定义了训练集，并为独立验证GELID的四个组件构建了测试集。我们总共对3款电子游戏的170个视频进行了手动分割、标注和聚类，构建了包含604个片段的数据集。结果。尽管GELID在步骤1（分割）和步骤4（具体问题聚类）中取得了满意结果，但在步骤3（游戏背景聚类）以及尤其步骤2（分类）方面表现出局限性。