  In this paper, we present a computational analysis of the Persian language
Twitter discourse with the aim to estimate the shift in stance toward gender
equality following the death of Mahsa Amini in police custody. We present an
ensemble active learning pipeline to train a stance classifier. Our novelty
lies in the involvement of Iranian women in an active role as annotators in
building this AI system. Our annotators not only provide labels, but they also
suggest valuable keywords for more meaningful corpus creation as well as
provide short example documents for a guided sampling step. Our analyses
indicate that Mahsa Amini's death triggered polarized Persian language
discourse where both fractions of negative and positive tweets toward gender
equality increased. The increase in positive tweets was slightly greater than
the increase in negative tweets. We also observe that with respect to account
creation time, between the state-aligned Twitter accounts and pro-protest
Twitter accounts, pro-protest accounts are more similar to baseline Persian
Twitter activity.
