LOOM: Showing the Dynamics of Power Laws in Twitter Data
|Title:||LOOM: Showing the Dynamics of Power Laws in Twitter Data||Authors:||Doyle, Maryanne
Keane, Mark T.
|Permanent link:||http://hdl.handle.net/10197/9058||Date:||14-Jul-2017||Abstract:||LOOM is advanced as a new visualisation for changes in ranks and trends in power-law data that is changing dynamically over time. A comparison between LOOM and existing methods for visualising such data (e.g.,time-series graphs, typical analytics dashboards). Several exemplar data sets are shown, using LOOM, drawn from the tracking of news stories on Twitter. The basis for the LOOM visualisation is elaborated and it is shown how it avoids the pitfalls arising in other line-graph representations.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2017 IEEE||Keywords:||Explanatory visualisation;Visual analytics||DOI:||10.1109/iV.2017.50||Language:||en||Status of Item:||Peer reviewed||Is part of:||Information Visualisation (IV), 2017 21st International Conference||Conference Details:||21st International Conference on Information Visualisation (iV2017) London South Bank University, London, United Kingdom, 11-14 July 2017|
|Appears in Collections:||Insight Research Collection|
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