Topic Tomographies (TopTom): a visual approach to distill information from media streams. (10th July 2019)

• Record Type: Journal Article
• Authors: Gobbo, B.; Balsamo, D.; Mauri, M.; Bajardi, P.; Panisson, A.; Ciuccarelli, P.
• Is Part Of: Computer graphics forum. Volume 38:Number 3(2019)
• Page Start: 609
• Page End: 621
• Publication Date: 2019-07-10
• Subjects: CCS Concepts -- Human‐centered computing → Visualization -- Information systems → Document topic models -- Expert search; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13714
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.