  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. Since no software and, thus, no video game is perfect, streamers
may encounter several problems (such as bugs, glitches, or performance issues).
However, 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. We
aim at proposing and empirically evaluating 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 and context in which appear (e.g., bugs
or glitches appearing in a specific level or scene of the game); and (iii)
clustering segments that regard the same specific issue. Method. We will build
on top of existing approaches able to identify videos that are relevant for a
specific video game. These represent the input of GELID that processes them to
achieve the defined objectives. We will experiment GELID on several gameplay
videos to understand the extent to which each of its steps is effective.
