数据驱动的政治学

Ingmar Weber, Ana-Maria Popescu, M. Pennacchiotti
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引用次数: 6

摘要

本教程将总结计算政治学这一不断发展的领域的最新进展。与许多其他研究领域一样,开放的大数据的可用性以及社交媒体的日益普及和重要性正在彻底改变这一研究领域。与学术界的兴趣高涨相匹配的是,政治竞选活动也在努力利用在线数据,以了解如何最好地传播信息,并接触到合适的潜在捐助者或选民。在这种情况下,一个教程可以总结现有的方法在一个迷人的,高度感兴趣的领域,并允许不同背景的参与者从研究的方法和问题中获得灵感。本教程将以以下方面的开创性研究为特色:(1)政治两极分化,(2)选举预测和投票,以及(3)政治竞选和影响力传播。我们的目标不仅是让与会者熟悉相关会议(如WWW、ICWSM或CIKM)的观点,而且要展示更接近政治学的观点和定量方法,如普尔和罗森塔尔的政治家政治取向的提名分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven political science
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.
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