  Processing human affective behavior is important for developing intelligent
agents that interact with humans in complex interaction scenarios. A large
number of current approaches that address this problem focus on classifying
emotion expressions by grouping them into known categories. Such strategies
neglect, among other aspects, the impact of the affective responses from an
individual on their interaction partner thus ignoring how people empathize
towards each other. This is also reflected in the datasets used to train models
for affective processing tasks. Most of the recent datasets, in particular, the
ones which capture natural interactions ("in-the-wild" datasets), are designed,
collected, and annotated based on the recognition of displayed affective
reactions, ignoring how these displayed or expressed emotions are perceived. In
this paper, we propose a novel dataset composed of dyadic interactions
designed, collected and annotated with a focus on measuring the affective
impact that eight different stories have on the listener. Each video of the
dataset contains around 5 minutes of interaction where a speaker tells a story
to a listener. After each interaction, the listener annotated, using a valence
scale, how the story impacted their affective state, reflecting how they
empathized with the speaker as well as the story. We also propose different
evaluation protocols and a baseline that encourages participation in the
advancement of the field of artificial empathy and emotion contagion.
