This paper explores the use of organic data to predict forced migration from Ukraine to five neighboring countries. The study combines Twitter conversations with event and fatality data from the Armed Conflict Location and Event Data Project (ACLED) and develops predictive models of forced displacement. The results suggest that Twitter variables were more important predictors in the first phase of the conflict, while event-based predictors were more important in the second phase.
We propose a novel reinforcement learning framework for misinformation detection on Twitter. We release both code, data and pre-trained models.
We propose pre-trained language models for political Twitter data. We evaluate all models and report results. We release both data and pre-trained models.
We propose a novel language modeling for stance detection. We release both data and pre-trained models.