A Multiscale Approach To Network Event Identification Using Geolocated Twitter Data

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.

Chao Yang, Ian Jensen, and Paul Rosen. A Multiscale Approach To Network Event Identification Using Geolocated Twitter Data. Computing, 2014.

@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.}
}