  Current studies of bias in NLP rely mainly on identifying (unwanted or
negative) bias towards a specific demographic group. While this has led to
progress recognizing and mitigating negative bias, and having a clear notion of
the targeted group is necessary, it is not always practical. In this work we
extrapolate to a broader notion of bias, rooted in social science and
psychology literature. We move towards predicting interpersonal group
relationship (IGR) - modeling the relationship between the speaker and the
target in an utterance - using fine-grained interpersonal emotions as an
anchor. We build and release a dataset of English tweets by US Congress members
annotated for interpersonal emotion -- the first of its kind, and 'found
supervision' for IGR labels; our analyses show that subtle emotional signals
are indicative of different biases. While humans can perform better than chance
at identifying IGR given an utterance, we show that neural models perform much
better; furthermore, a shared encoding between IGR and interpersonal perceived
emotion enabled performance gains in both tasks. Data and code for this paper
are available at https://github.com/venkatasg/interpersonal-bias
