LOOM: Showing the Dynamics of Power Laws in Twitter Data

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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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