Understanding Citizens' Direct Policy Suggestions to the Federal Government: A Natural Language Processing and Topic Modeling Approach
- Resource Type
- Conference
- Authors
- Hagen, Loni; Uzuner, Ozlem; Kotfila, Christopher; Harrison, Teresa M.; Lamanna, Dan
- Source
- 2015 48th Hawaii International Conference on System Sciences System Sciences (HICSS), 2015 48th Hawaii International Conference on. :2134-2143 Jan, 2015
- Subject
- Computing and Processing
Government
Natural language processing
Public policy
Computational modeling
Internet
Web sites
e-petitions
e-government
topic modeling
natural language processing
policy informatics
e-paticipation
- Language
- ISSN
- 1530-1605
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.