Suchergebnisse

AbstractWhen the 2022 Russian full-scale invasion of Ukraine forced millions of people to leave their homes, officials worldwide scrambled to estimate the number of people who would seek refuge in their countries. There were a limited number of official tools in place to lean on to help determine this estimate. In this article, we investigate the possibility of using various publicly available organic (i.e. non-designed) data to predict forced movement from Ukraine early in the crisis. In particular, we establish Ukrainian-language insecurity and contextual indicators from multiple data sources, namely Google Trends, Twitter/X, local newspapers, the ACLED database, and the GDELT database. We compare the usefulness of these indicators in predicting forced migration into three neighboring countries: Poland, Slovakia, and Hungary. To minimize the challenge of temporal misalignment between the organic data and actual movement, we develop a lagging and aggregation framework. Findings reveal Google Trends variables are a robust leading indicator of observed forced migration for this conflict. While other indicators are less strong, they still capture shifts in forced migration flows, highlighting the potential for using publicly available organic data during emerging forced displacement crises.

The Changed Information Environment of Presidential Campaigns -- What Might Have Made News: Big Issues, Historic Candidates, and Hillary Clinton's Strange Email Scandal -- What the Media Covered, Journalists Tweeted, and the Public Heard about the Candidates -- The August 2015 Republican Debate: A Study of Information Flow in the 2015-2016 Republican Nomination Contest -- The Language and Tone of the 2016 Campaign -- The Things People Heard about Trump and Clinton -- Public Attention to Events in the 2016 Election: What Mattered? -- Fake News Production and Consumption -- Conclusions: Determining What (Words) Mattered.