Committee meetings are a fundamental part of the legislative process in whichconstituents, lobbyists, and legislators alike can speak on proposed bills at thelocal and state level. Oftentimes, unspoken “rules” or standards are at play inpolitical processes that can influence the trajectory of a bill, leaving constituentswithout a political background at an inherent disadvantage when engaging withthe legislative process. The work done in this thesis aims to explore the extent towhich the language and phraseology of a general public testimony can influence avote, and examine how this information can be used to promote civic engagement.
The Digital Democracy database contains digital records for over 40,000 realtestimonies by non-legislator public persons presented at California Legislaturecommittee meetings 2015-2018, along with the speakers’ desired vote outcomeand individual legislator votes in that discussion. With this data, we conduct alinguistic analysis that is then leveraged by the Constituent phraseology AnalysisTool (CPAT) to generate a user-based intelligent statistical comparison betweena proposed testimony and language patterns that have previously been successful.
The following questions are at the core of this research: Which (if any) lan-guage features are correlated with persuasive success in a legislative context?Does the committee’s topic of discussion impact the language features that canlend to a testimony’s success? Can mirroring a legislator’s speech patterns changethe probability of the vote going your way? How can this information be used tolevel the playing field for constituents who want their voices heard?
Given the 33 linguistic features developed in this research, supervised classifi-cation models were able to predict testimonial success with up to 85.1% accuracy,indicating that the new features had a significant impact on the prediction ofsuccess. Adding these features to the 16 baseline linguistic features developedin Gundala’s [18] research improved the prediction accuracy by up to 2.6%. Wealso found that balancing the dataset of testimonies drastically impacted theprediction performance metrics, with 93% accuracy achieved for the imbalanceddataset and 60% accuracy after balancing. The Constituent Phraseology AnalysisTool showed promise in the generation of linguistic analysis based on previouslysuccessful language patterns, but requires further development before achievingtrue usability. Additionally, predicting success based on linguistic similarity to alegislator on the committee produced contradictory results. Experiments yieldeda 4% increase in predictive accuracy when adding comparative language featuresto the feature set, but further experimentation with weight distributions revealedonly marginal impacts from comparative features.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4224 |
| Date | 01 March 2023 |
| Creators | Cossoul, Marine |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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