Toward Visually Analyzing Dynamic Social Messages and News Articles Containing Geo-Referenced Information

The number of social media posts and news articles that are being published every day is high. This makes them an attractive source of human-generated information for different domain experts such as journalists and business analysts but also emergency responders, particularly if posts contain references to geolocations. Visual analytics approaches can help to gain insights into such datasets and inform decision-makers. However, the high volume and the veracity of the data, as well as the velocity in the case of streaming data, pose challenges when supporting explorative analysis with interactive visualization. Based on four exemplary approaches, we outline recently proposed strategies to tackle these challenges. We describe how geo-aware filtering and anomaly detection methods can help to inform stakeholders based on geolocated tweets. We show that data-aware tag maps can provide analysts with an overview-first, details-on-demand visual summary of large amounts of text content over time. With space-filling curves, we can visualize the temporal evolution of geolocations in a two-dimensional plot without relying on animations that would impede comparative analyses. Additionally, we discuss the use of an efficient dynamic clustering algorithm for enabling large-scale visual analyses of streaming posts.