Understanding Citizens' Direct Policy Suggestions to the Federal Government: A Natural Language Processing and Topic Modeling Approach

被引：24

作者：

Hagen, Loni ^{[1
]}

Uzuner, Ozlem ^{[1
]}

Kotfila, Christopher ^{[1
]}

Harrison, Teresa M. ^{[2
]}

Lamanna, Dan ^{[1
]}

机构：

[1] SUNY Albany, Coll Comp & Informat, Albany, NY 12222 USA

[2] SUNY Albany, Dept Commun, Albany, NY 12222 USA

来源：

2015 48TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS) | 2015年

关键词：

D O I：

10.1109/HICSS.2015.257

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We report on our initial efforts to make sense of e-petitions as policy suggestions by using the NLP technique of "topic modeling" to identify the "topics" that emerge in e-petitions. Using a sample of petitions submitted to the Obama Administration's WtP petitioning system as a case study, we produced 30 emergent topics. 21 out of the 30 topics were initially coded as high-quality topics. Upon qualitative investigation, all but one of these 21 topics were determined to have a coherent theme. Our results imply that topic modeling has the potential to enable the interpretation of large quantities of citizen generated policy suggestions through a largely automated process, with potential application to research on e-participation and policy informatics.

引用

页码：2134 / 2143

页数：10

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