  The portrayal of negative emotions such as anger can vary widely between
cultures and contexts, depending on the acceptability of expressing full-blown
emotions rather than suppression to maintain harmony. The majority of emotional
datasets collect data under the broad label ``anger", but social signals can
range from annoyed, contemptuous, angry, furious, hateful, and more. In this
work, we curated the first in-the-wild multicultural video dataset of emotions,
and deeply explored anger-related emotional expressions by asking
culture-fluent annotators to label the videos with 6 labels and 13 emojis in a
multi-label framework. We provide a baseline multi-label classifier on our
dataset, and show how emojis can be effectively used as a language-agnostic
tool for annotation.
