Interactive Interfaces For Complex Network Analysis: An Information Credibility Perspective

This paper discusses and evaluates the impact of visualization and interaction strategies for extracting quality information from data in complex networks such as microblogs. Two different approaches to interactive visual representations of data are discussed: an interactive node-link graph and a novel approach where content is separated into interactive lists based on data properties. To assess the two approaches in terms of information credibility, the TopicNets system is compared with "Fluo", a novel system. An analysis scenario is performed through each system on a set of big data filtered from the Twitter message service. The exposure of content, trade-offs between algorithmic power and interaction complexity, methods for content filtering, and strategies for recommending new content are assessed for each system. Fluo is found to improve on TopicNets ability to efficiently find relevant content primarily by providing a more structured content view, however, TopicNets is more customizable and boasts features which are critical for an expert analyst. The paper concludes with general insights on interface design for information filtering systems to maximize perceived quality of information.