The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Spatiotemporal Network Analysis and Visualization
Abstract
Spatiotemporal social network analysis shows relationships among people at a particular time and location. This paper presents an algorithm that mines text for person and location words and creates connections among words. It shows how this algorithm output, when chunked by time intervals, may be visualized by third-party social network analysis software in the form of standard network pin diagrams or geographic maps. Its data sample comes from newspaper articles concerning the 2006 Darfur crisis in Sudan. Given an immense data sample, it would be possible to use the algorithm to detect trends that would predict the next geographic center(s) of influence and types of actors (foreign dignitaries or domestic leaders, for example). This algorithm should be widely generalizable to many text domains as long as the external resources are modified accordingly.
Related Content
|
Salwa Saidi, Anis Ghattassi, Samar Zaggouri, Ahmed Ezzine.
© 2021.
19 pages.
|
|
Mehmet Sevkli, Abdullah S. Karaman, Yusuf Ziya Unal, Muheeb Babajide Kotun.
© 2021.
29 pages.
|
|
Soumaya Elhosni, Sami Faiz.
© 2021.
13 pages.
|
|
Symphorien Monsia, Sami Faiz.
© 2021.
20 pages.
|
|
Sana Rekik.
© 2021.
9 pages.
|
|
Oumayma Bounouh, Houcine Essid, Imed Riadh Farah.
© 2021.
14 pages.
|
|
Mustapha Mimouni, Nabil Ben Khatra, Amjed Hadj Tayeb, Sami Faiz.
© 2021.
18 pages.
|
|
|