Abstract

We propose a multi-scale text mining methodology and develop a visual intelligence platform for tracking the diffusion of online social movements. The algorithms utilize large amounts of text collected from a wide variety of organizations' media outlets to discover their hotly debated topics, and their discriminative perspectives voiced by opposing camps organized into multiple scales. We utilize discriminating perspectives to classify and map individual Tweeter's message content to social movements based on the perspectives expressed in their weekly tweets. We developed a visual intelligence platform, named LookingGlass, to track the geographical footprint, shifting positions and flows of individuals, topics and perspectives between groups.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PublisherAssociation for Computing Machinery
Pages1020-1027
Number of pages8
ISBN (Print)9781450322409
DOIs
Publication statusPublished - 2013
Event2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON, Canada
Duration: Aug 25 2013Aug 28 2013

Other

Other2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
CountryCanada
CityNiagara Falls, ON
Period8/25/138/28/13

Keywords

  • Multi-scaling
  • Social movements
  • Text mining

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Kim, N., Gokalp, S., Davulcu, H., & Woodward, M. (2013). LookingGlass: A visual intelligence platform for tracking online social movements. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (pp. 1020-1027). Association for Computing Machinery. https://doi.org/10.1145/2492517.2500275