Predicting the Vote Using Legislative Speech

Budhwar, Aditya

doi:10.15368/theses.2018.18

Cited by 8 publications

(20 citation statements)

References 7 publications

(8 reference statements)

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“…(Patil et al, 2019;Kraft et al, 2016;Karimi et al, 2019;Peng et al, 2016) extend this congress model to learn embeddings for legislators and congress bills using other sources of data (e.g., Twitter, knowledge graphs). More recently, (Budhwar et al, 2018) evaluates different models for predicting roll-call votes based on verbal statements that legislators make during questioning.…”

Section: Technical Contributionsmentioning

confidence: 99%

Understanding the Language of Political Agreement and Disagreement in Legislative Texts

Davoodi¹,

Waltenburg²,

Goldwasser

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level. Despite this fact, there is little awareness of the dynamics that lead to adopting these policies. In this paper, we take the first step towards a better understanding of these processes and the underlying dynamics that shape them, using data-driven methods. We build a new large-scale dataset, from multiple data sources, connecting state bills and legislator information, geographical information about their districts, and donations and donors' information. We suggest a novel task, predicting the legislative body's vote breakdown for a given bill, according to different criteria of interest, such as gender, rural-urban and ideological splits. Finally, we suggest a shared relational embedding model, representing the interactions between the text of the bill and the legislative context in which it is presented. Our experiments show that providing this context helps improve the prediction over strong text-based models.

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Section: Technical Contributionsmentioning

confidence: 99%

Understanding the Language of Political Agreement and Disagreement in Legislative Texts

Davoodi¹,

Waltenburg²,

Goldwasser

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…The transcription process, and therefore T tid , is composed of: startup time, text correction, speaker identification, splitting and merging utterances, as well as passive interactions. Equations (4) and 5 (5) Startup time can be classified as the time span between loading the transcription screen and first user interaction. Text correction time is probably the most elementary interaction between transcriber and tool.…”

Section: Transcriber Interactionsmentioning

confidence: 99%

Deconstructing Human-assisted Video Transcription and Annotation for Legislative Proceedings

Ruprechter

Khosmood

Guetl

2020

Digit. Gov.: Res. Pract.

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Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information during such proceedings. Even if professional-quality automated transcriptions existed, other tasks such as speaker or rhetorical position identifications are not fully automatable. This work focuses on improving and evaluating the transcription software used by the Digital Democracy initiative, named Transcription Tool. Human transcribers work to up-level state legislative proceedings using this tool. Five phases of tool improvements are introduced and for each phase, the resulting change in efficiency is measured. We investigate over 12,000 individual transcription sessions (2,300 hours of video), where each session is the record of one bill discussion. A set of about 3,200 sessions belonging to a single cohort of 20 transcribers is further evaluated. Through introduction of new tool features, human-assisted transcription efficiency can be improved by 19.4% over five phases. Furthermore, investigation into transcriber usage patterns reveals that transcription time is composed of passive time, speaker identification, text correction, tool startup, as well as splitting and merging utterances. We analyze and rank these as a contribution.

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“…In March of 2018, Aditya Budhwar tried to predict a legislators vote by examining their speech prior to a vote [19]. In this thesis, we also analyzed a legislator speech prior to voting using a less in-depth methodology.…”

Section: Vader Sentimentmentioning

confidence: 99%

“…In this thesis, we also analyzed a legislator speech prior to voting using a less in-depth methodology. While we used similar methods to [19], the objective of our work is completely different. For the scope of this thesis, we needed to analyze whether or not the legislator had a non-neutral sentiment and collected features about the amount of time a legislator spent speaking on a bill.…”

Section: Vader Sentimentmentioning

confidence: 99%

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DiSH: Democracy in State Houses

Russo

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In our current political climate, state level legislators have become increasingly important. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislator's activity on bills. Second, we created embeddings that represented a legislator's level of activity and engagement for a given bill using a custom model called Democracy2Vec. Third, we provided a case study focused on the 2015-2016 California State Legislator and had our results verified by a political expert. Our results show that our embeddings can explain relationships between legislator and how they will likely act during the legislative process. iv ACKNOWLEDGMENTS Thanks to:• My family, for their unwavering love and support throughout this process.• Bailey Blair, for constantly supporting me and pushing me to be my best.• My advisor, Alex Dekhtyar, for shaping my career through his indispensable advice and guidance.• My committee members, Foaad Khosmood and Franz Kurfess, for their feedback and assistance throughout this unusual process.• Daniel Kauffman, for letting me use his bill topic modeling system.

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Predicting the Vote Using Legislative Speech

Cited by 8 publications

References 7 publications

Understanding the Language of Political Agreement and Disagreement in Legislative Texts

Understanding the Language of Political Agreement and Disagreement in Legislative Texts

Deconstructing Human-assisted Video Transcription and Annotation for Legislative Proceedings

DiSH: Democracy in State Houses

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