Understanding Offline Political Systems by Mining Online Political Data

D. Lazer, Oren Tsur, Tina Eliassi-Rad
{"title":"Understanding Offline Political Systems by Mining Online Political Data","authors":"D. Lazer, Oren Tsur, Tina Eliassi-Rad","doi":"10.1145/2835776.2855112","DOIUrl":null,"url":null,"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.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2835776.2855112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

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.
通过挖掘在线政治数据来理解离线政治系统
亚里士多德断言:“人是天生的政治动物”。这种政治性质体现在我们产生的数据和我们在网上留下的痕迹上。在本教程中,我们将讨论一些关于政治数据挖掘的基本问题:什么类型的数据可以被视为政治数据?我们能从这些数据中学到什么?我们可以用这些数据来预测政治变化等等吗?如何有效地完成这些预测任务?我们能否利用在线社会政治数据来更好地了解我们的政治制度和最近的政治变化?使用在线数据进行政治分析的陷阱和固有缺点是什么?近年来,随着数据的丰富,这些问题以及其他问题变得越来越重要,特别是考虑到全球政治动荡和即将到来的2016年美国总统大选。我们介绍了相关的政治科学理论,描述了计算社会科学框架内的挑战,以及连接社会网络分析、图挖掘和自然语言处理的最新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信