The public speaks: Using large-scale public comments data in public response research

被引:3
|
作者
Dokshin, Fedor A. [1 ]
机构
[1] Univ Toronto, Dept Sociol, 725 Spadina Ave, Toronto, ON M5S 1L2, Canada
关键词
Public response; Public participation; Methodology; Computational text analysis; UNCONVENTIONAL OIL; GAS DEVELOPMENT; TOPIC MODELS; TEXT; MOVEMENT; POLITICS;
D O I
10.1016/j.erss.2022.102689
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Understanding public response to controversial projects, industries, and policies is of central focus to social scientists. During many of these contentious episodes, governmental agencies collect input from the public in the form of public comments. Public comments offer rich data for research on public response, and in recent years researchers have begun to tap their potential. In particular, large-scale, digitized public comments, when combined with advances in computational techniques raise exciting opportunities for research on public response. This paper reviews existing studies that have used large-scale public comments, identifies the key features that make public comments data uniquely useful in studies of public response, and discusses the common challenges at the data collection, data processing, and analysis stages, with particular attention to computational techniques for text analysis and for linking administrative records. The paper concludes by identifying promising directions for future research using large-scale public comments data.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] The public speaks: Using large-scale public comments data in public response research
    Dokshin, Fedor A.
    [J]. ENERGY RESEARCH & SOCIAL SCIENCE, 2022, 91
  • [2] Big Data, Large-Scale Text Analysis, and Public Health Research
    Chowkwanyun, Merlin
    [J]. AMERICAN JOURNAL OF PUBLIC HEALTH, 2019, 109 : 5126 - 5127
  • [3] Large-scale public data reuse to model immunotherapy response and resistance
    Jingxin Fu
    Karen Li
    Wubing Zhang
    Changxin Wan
    Jing Zhang
    Peng Jiang
    X. Shirley Liu
    [J]. Genome Medicine, 12
  • [4] Large-scale public data reuse to model immunotherapy response and resistance
    Fu, Jingxin
    Li, Karen
    Zhang, Wubing
    Wan, Changxin
    Zhang, Jing
    Jiang, Peng
    Liu, X. Shirley
    [J]. GENOME MEDICINE, 2020, 12 (01)
  • [5] Research on Sustainability Assessment of Large-scale Public Building
    Yu, Junqi
    Gao, Jing
    Yan, Guiyong
    [J]. ADVANCES IN ENERGY SCIENCE AND TECHNOLOGY, PTS 1-4, 2013, 291-294 : 1050 - 1054
  • [6] Outlier Ranking for Large-Scale Public Health Data
    Joshi, Ananya
    Townes, Tina
    Gormley, Nolan
    Neureiter, Luke
    Rosenfeld, Roni
    Wilder, Bryan
    [J]. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20, 2024, : 22176 - 22184
  • [7] Public Transit Passenger Profiling by Using Large-Scale Smart Card Data
    Wang, Lewen
    Wang, Yu
    Sun, Xiaofei
    Wu, Yizheng
    Peng, Fei
    Chen, Chun-Hung Peter
    Song, Guohua
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, 2023, 149 (04)
  • [8] Translating research findings into large-scale public programs and policy
    ZervigonHakes, AM
    [J]. FUTURE OF CHILDREN, 1995, 5 (03): : 175 - 191
  • [9] Framework research on sustainable capacity of large-scale public project
    Zhou Hong
    Cheng Hu
    [J]. Proceedings of 2006 International Conference on Construction & Real Estate Management, Vols 1 and 2: COLLABORATION AND DEVELOPMENT IN CONSTRUCTION AND REAL ESTATE, 2006, : 541 - 544
  • [10] Large-Scale Radar Localization using Online Public Maps
    Hong, Ziyang
    Petillot, Yvan
    Zhang, Kaicheng
    Xu, Shida
    Wang, Sen
    [J]. 2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA, 2023, : 3990 - 3996