A Multiscale Approach To Network Event Identification Using Geolocated Twitter Data
A Multiscale Approach To Network Event Identification Using Geolocated Twitter Data |
Abstract
The large volume of data associated with social networks hinders the unaided user from interpreting network content in real time. This problem is compounded by the fact that there are limited tools available for enabling robust visual social network exploration. We present a network activity visualization using a novel aggregation glyph called the clyph. The clyph intuitively combines spatial, temporal, and quantity data about multiple network events. We also present several case studies where major network events were easily identified using clyphs, establishing them as a powerful aid for network users and owners.
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Citation
Chao Yang, Ian Jensen, and Paul Rosen. A Multiscale Approach To Network Event Identification Using Geolocated Twitter Data. Computing, 2014.
Bibtex
@article{yang2014multiscale, title = {A Multiscale Approach to Network Event Identification Using Geolocated Twitter Data}, author = {Yang, Chao and Jensen, Ian and Rosen, Paul}, journal = {Computing}, volume = {96}, pages = {3--13}, year = {2014}, keywords = {Visualization; Internet; Big data; Social network; Twitter; Visualization algorithms}, note = {textit{Presented at First IMC Workshop on Internet Visualization (WIV 2012).}}, abstract = {The large volume of data associated with social networks hinders the unaided user from interpreting network content in real time. This problem is compounded by the fact that there are limited tools available for enabling robust visual social network exploration. We present a network activity visualization using a novel aggregation glyph called the clyph. The clyph intuitively combines spatial, temporal, and quantity data about multiple network events. We also present several case studies where major network events were easily identified using clyphs, establishing them as a powerful aid for network users and owners.} }