TY - GEN
T1 - Automatic Bill Recommendation for Statehouse Journalists
AU - Perkonigg, Michelle
AU - Khosmood, Foaad
AU - Gütl, Christian
N1 - Publisher Copyright:
© 2023, IFIP International Federation for Information Processing.
PY - 2023
Y1 - 2023
N2 - AI4Reporters is a project designed to produce automated electronic tip sheets for news reporters covering the statehouses (state level legislatures) in the United States. The project aims to capture the most important information that occurred in a bill discussion to allow reporters to quickly decide if they want to pursue a story on the subject. In this paper, we present, discuss and evaluate a module for the tip sheets that is designed to recommend additional bills to investigate for the reporter that receives the tip sheet. Similar in concept to movie recommendations, this module is designed to find other bills with their own meetings and discussions, that are most relevant to the discussion captured in the given tip sheet. Specifically we present similarity algorithms along three dimensions that our investigation suggests are distinct reasons for journalists to be interested in a recommendation. These include similarity in content, individuals or geographical locations. We validate the system by fielding a user study of 29 subjects for hour-long surveys resulting in 870 decisions being captured. We find that between 63.4% and 82.8% of the human selections are in agreement with our system’s recommendations.
AB - AI4Reporters is a project designed to produce automated electronic tip sheets for news reporters covering the statehouses (state level legislatures) in the United States. The project aims to capture the most important information that occurred in a bill discussion to allow reporters to quickly decide if they want to pursue a story on the subject. In this paper, we present, discuss and evaluate a module for the tip sheets that is designed to recommend additional bills to investigate for the reporter that receives the tip sheet. Similar in concept to movie recommendations, this module is designed to find other bills with their own meetings and discussions, that are most relevant to the discussion captured in the given tip sheet. Specifically we present similarity algorithms along three dimensions that our investigation suggests are distinct reasons for journalists to be interested in a recommendation. These include similarity in content, individuals or geographical locations. We validate the system by fielding a user study of 29 subjects for hour-long surveys resulting in 870 decisions being captured. We find that between 63.4% and 82.8% of the human selections are in agreement with our system’s recommendations.
KW - artificial intelligence
KW - bill recommendation
KW - digital government
KW - legislatures
UR - http://www.scopus.com/inward/record.url?scp=85172011014&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-41138-0_9
DO - 10.1007/978-3-031-41138-0_9
M3 - Conference paper
AN - SCOPUS:85172011014
SN - 9783031411373
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 128
EP - 143
BT - Electronic Government - 22nd IFIP WG 8.5 International Conference, EGOV 2023, Proceedings
A2 - Lindgren, Ida
A2 - Csáki, Csaba
A2 - Kalampokis, Evangelos
A2 - Tambouris, Efthimios
A2 - Janssen, Marijn
A2 - Zuiderwijk, Anneke
A2 - Viale Pereira, Gabriela
A2 - Virkar, Shefali
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd IFIP WG 8.5 International Conference on Electronic Government
Y2 - 5 September 2023 through 7 September 2023
ER -