TweetViz: Following Twitter Hashtags to Support Storytelling

How can visualizations of massive amounts of information be made more useful for data journalists? The availability of large amounts of publicly available user generated content is opening new opportunities to study social, cultural, and communications phenomenon. Computer assisted analysis now makes it possible to explore the relationship between nodes and text without having to choose between data size and depth. To create a visualization technique that would allowed us to reveal the network of actors and the main themes hidden in a large dataset, we had to work in a method of inquiry for social sciences. Based on the actor-network theory (ANT) we explored a dataset extracted from Twitter in order to map relationships and indicate new possibilities for journalists by discovering main themes around a hashtag, this way we interpret a layer of text multiple times, analyzing the nodes in its many attributes. Beyond the boundaries of 140 characters, this approach can succeed as it reproduces and reveals the dynamic connections contained in a collective phenomenon. In the last section, we demonstrate a prototype visualization that reveals behaviors and discourses within the large sample datasets. . We use the D3 visualization library to overlap related links and nodes to produce a comprehensible interactive visualization. Our model is interactive and allows us to identify part and whole pattern relationships constant with the three principles of information visualization: overview first, zoom and filter, then details on demand. This paper analyses networks from the perspective of ANT in order to create a visualization ready to support users when telling a story with data.