Deciphering Multiple Aspects of Uncertainty in the Visual Analysis of Social Media Data

Volunteered Geographic Information (VGI) is a kind of information provided by citizens with different cultural backgrounds, experiences, interests, and level of education. They contribute, maintain, exchange, and share spatiotemporal data driven by a wide variety of interests and in different formats, for example, maps, image, and text.

To extract meaningful VGI from these heterogeneous and sometimes contradictory datasets is challenging. One way to start tackling this challenge is by understanding the different levels of uncertainty from the data, and during the visual analytical process.

In this talk, I will present my current efforts in the analysis of the uncertainty coming from social media data through the Visual Analytics process. From the raw data, through the data transformation, visual mapping, and interaction.