Understanding Citizens' Direct Policy Suggestions to the Federal Government: A Natural Language Processing and Topic Modeling Approach
Document Type
Conference Proceeding
Publication Date
2015
Digital Object Identifier (DOI)
https://doi.org/10.1109/HICSS.2015.257
Abstract
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.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
2015 48th Hawaii International Conference on System Sciences, p. 2134-2143
Scholar Commons Citation
Hagen, Loni; Uzuner, Özlem; Kotfila, Christopher; Harrison, Teresa M.; and Lamanna, Dan, "Understanding Citizens' Direct Policy Suggestions to the Federal Government: A Natural Language Processing and Topic Modeling Approach" (2015). School of Information Faculty Publications. 327.
https://digitalcommons.usf.edu/si_facpub/327
