Visual analysis of volunteered geographic information for interactive situation modeling and real-time event assessment

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In the first phase of this project, we proposed a novel visual analytics approach that enables analysts to acquire holistic situation assessment based on VGI features of social media data. Particularly, we investigated how the deep integration of visual interactive schemes with automated machine learning tools could be used to create, manage, and adapt scalable ad-hoc regression models for time-critical decision-making. The initial results of these efforts, in the form of working system prototypes, have been evaluated with domain experts in actual application scenarios as well as under lab conditions. From the domain experts' feedback, we conclude that the existing methodology needs to be extended in three major directions. First, situation analysis under real-world conditions is mostly a collaborative effort. Models and queries for information gathering are established, used, and modified by a range of people in various roles but with limited capacities in terms of work load. In addition, results have to be collected and presented in a way that is meaningful to all stakeholders. Accordingly, advanced visual interfaces for ad-hoc model building and information gathering should allow simultaneous, transparent use by multiple collaborators and provide tailored perspectives that support different levels of analytical depth and decision-making. Second, while our first project phase considered novel VGI data channels in an isolated fashion, the analysis can be much more valuable if it supports direct correlation and comparison with other, more traditional sources of information, such as news media. Recent results in language modeling and language dimensionality reduction made it possible to efficiently connect similar or related textual information in real-time data streams. Combined with data visualization and interactive reasoning, these techniques can be used to cross-examine the significance and validity of textual content from multiple sources as well as to trace back the origins of individual pieces of information. Third, the massive occurrence of inaccurate, erroneous, or even deceitful information in social media channels before, during, and after critical events emerged as a salient phenomenon and major challenge for situation analysis. Due to the unprecedented scales of such misinformation - sometimes just inadvertently shared by unsuspecting users, sometimes deliberately distributed in entire campaigns by professional groups or through automated content generation software - there is a need for tools that help to identify ongoing anomalies as well as to assess their spatial, temporal, and topological extent. In this case, visual analytics techniques that allow a large-scale integrated analysis of VGI features of the data, such as known geolocations of messages and users, and combine them with the textual content, attached media, and meta-data of the entries provide a promising leverage point.