twitter

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.

Social media and the web have become primary sources for obtaining information and news. Given the speed and spread of information on social media, effects of poor-quality information, especially with respect to health-related information, can be …

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 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 taxonomy for false information on social media and a new concept of context-specific spam. We release both data and models.

More resources are coming soon

We analyze misinformation about the US 2020 presidential election. We release both data and code including interactive visualizations.

We propose a novel language modeling for stance detection. We release both data and pre-trained models.