Data-driven political science

Ingmar Weber, Ana Maria Popescu, Marco Pennacchiotti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The tutorial will summarize the state-of-the art in the growing area of computational political science. Like many others, this research domain is being revolutionized by the availability of open, big data and the increasing reach and importance of social media. The surging interest on the part of the academic community is matched by intense efforts on the part of political campaigns to use online data in order to learn how to best disseminate information and reach the right potential donors or voters. In this context, a tutorial can summarize existing methods in a fascinating, high-interest area and allow participants with diverse backgrounds to get inspiration from the methods and problems studied. The tutorial will feature seminal research concerning (i) political polarization, (ii) election prediction and polling, and (iii) political campaigning and influence propagation. The goal is not only to familiarize attendees with ideas from related conferences such as WWW, ICWSM or CIKM, but also to present ideas and quantitative methods closer to political science such as Poole's and Rosenthal's NOMINATE score for a politician's political orientation.

Original languageEnglish
Title of host publicationWSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
Pages777-778
Number of pages2
DOIs
Publication statusPublished - 28 Feb 2013
Event6th ACM International Conference on Web Search and Data Mining, WSDM 2013 - Rome, Italy
Duration: 4 Feb 20138 Feb 2013

Other

Other6th ACM International Conference on Web Search and Data Mining, WSDM 2013
CountryItaly
CityRome
Period4/2/138/2/13

Fingerprint

World Wide Web
Availability
Polarization
Big data

Keywords

  • big data
  • election prediction
  • political science

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Weber, I., Popescu, A. M., & Pennacchiotti, M. (2013). Data-driven political science. In WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining (pp. 777-778) https://doi.org/10.1145/2433396.2433498

Data-driven political science. / Weber, Ingmar; Popescu, Ana Maria; Pennacchiotti, Marco.

WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013. p. 777-778.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Weber, I, Popescu, AM & Pennacchiotti, M 2013, Data-driven political science. in WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining. pp. 777-778, 6th ACM International Conference on Web Search and Data Mining, WSDM 2013, Rome, Italy, 4/2/13. https://doi.org/10.1145/2433396.2433498
Weber I, Popescu AM, Pennacchiotti M. Data-driven political science. In WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013. p. 777-778 https://doi.org/10.1145/2433396.2433498
Weber, Ingmar ; Popescu, Ana Maria ; Pennacchiotti, Marco. / Data-driven political science. WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013. pp. 777-778
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