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In this paper, we present a computational analysis of the Persian languageTwitter discourse with the aim to estimate the shift in stance toward genderequality following the death of Mahsa Amini in police custody. We present anensemble active learning pipeline to train a stance classifier. Our noveltylies in the involvement of Iranian women in an active role as annotators inbuilding this AI system. Our annotators not only provide labels, but they alsosuggest valuable keywords for more meaningful corpus creation as well asprovide short example documents for a guided sampling step. Our analysesindicate that Mahsa Amini's death triggered polarized Persian languagediscourse where both fractions of negative and positive tweets toward genderequality increased. The increase in positive tweets was slightly greater thanthe increase in negative tweets. We also observe that with respect to accountcreation time, between the state-aligned Twitter accounts and pro-protestTwitter accounts, pro-protest accounts are more similar to baseline PersianTwitter activity.

For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles