Forecasting Ukrainian Refugee Flows With Organic Data Sources

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.

Detecting and Understanding of Information Pollution on Social Media

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 …

Traditional and Context-specific Spam Detection in Low Resource Settings

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.

Research note: Lies and presidential debates: How political misinformation spread across media streams during the 2020 election

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

Understanding high-and low-quality URL Sharing on COVID-19 Twitter streams

This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. We distinguish between high quality health sources, traditional news sources, and low quality misinformation sources. We find that …