Understanding offline political systems by mining online political data

David Lazer, Oren Tsur, Tina Eliassi-Rad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

"Man is by nature a political animal", as asserted by Aristotle. This political nature manifests itself in the data we produce and the traces we leave online. In this tutorial, we address a number of fundamental issues regarding mining of political data: What types of data could be considered political? What can we learn from such data? Can we use the data for prediction of political changes, etc? How can these prediction tasks be done efficiently? Can we use online socio-political data in order to get a better understanding of our political systems and of recent political changes? What are the pitfalls and inherent shortcomings of using online data for political analysis? In recent years, with the abundance of data, these questions, among others, have gained importance, especially in light of the global political turmoil and the upcoming 2016 US presidential election. We introduce relevant political science theory, describe the challenges within the framework of computational social science and present state of the art approaches bridging social network analysis, graph mining, and natural language processing.

Original languageEnglish
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages687-688
Number of pages2
ISBN (Electronic)9781450337168
DOIs
StatePublished - 8 Feb 2016
Externally publishedYes
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016

Publication series

NameWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining

Conference

Conference9th ACM International Conference on Web Search and Data Mining, WSDM 2016
Country/TerritoryUnited States
CitySan Francisco
Period22/02/1625/02/16

Keywords

  • Computational social science
  • Graph mining
  • Political data
  • Social and information networks

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