  People come to social media to satisfy a variety of needs, such as being
informed, entertained and inspired, or connected to their friends and
community. Hence, to design a ranking function that gives useful and
personalized post recommendations, it would be helpful to be able to predict
the affective response a user may have to a post (e.g., entertained, informed,
angered). This paper describes the challenges and solutions we developed to
apply Affective Computing to social media recommendation systems.
  We address several types of challenges. First, we devise a taxonomy of
affects that was small (for practical purposes) yet covers the important
nuances needed for the application. Second, to collect training data for our
models, we balance between signals that are already available to us (namely,
different types of user engagement) and data we collected through a carefully
crafted human annotation effort on 800k posts. We demonstrate that affective
response information learned from this dataset improves a module in the
recommendation system by more than 8%. Online experimentation also demonstrates
statistically significant decreases in surfaced violating content and increases
in surfaced content that users find valuable.
