  In India, people identify with a particular group based on certain attributes
such as religion. The same religious groups are often provoked against each
other. Previous studies show the role of provocation in increasing tensions
between India's two prominent religious groups: Hindus and Muslims. With the
advent of the Internet, such provocation also surfaced on social media
platforms such as WhatsApp.
  By leveraging an existing dataset of Indian WhatsApp posts, we identified
three categories of provoking sentences against Indian Muslims. Further, we
labeled 7,000 sentences for three provocation categories and called this
dataset PACO. We leveraged PACO to train a model that can identify provoking
sentences from a WhatsApp post. Our best model is fine-tuned RoBERTa and
achieved a 0.851 average AUC score over five-fold cross-validation.
Automatically identifying provoking sentences could stop provoking text from
reaching out to the masses, and can prevent possible discrimination or violence
against the target religious group.
  Further, we studied the provocative speech through a pragmatic lens, by
identifying the dialog acts and impoliteness super-strategies used against the
religious group.
