Litigation Risk Detection Using Twitter Data

被引:9
作者
Diao, Chengyuan [1 ]
Liang, Rachel [1 ]
Sharma, Deepak [2 ]
Cui, Qingbin [3 ]
机构
[1] Univ Maryland, Dept Civil & Environm Engn, Project Management Ctr Excellence, College Pk, MD 20742 USA
[2] Calif State Univ Fullerton, Dept Civil & Environm Engn, Fullerton, CA 92831 USA
[3] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA
关键词
Litigation risk; Twitter; Risk identification; Data analysis; Construction;
D O I
10.1061/(ASCE)LA.1943-4170.0000356
中图分类号
D9 [法律]; DF [法律];
学科分类号
0301 ;
摘要
Construction projects are notorious for their frequent disputes and costly legal battles. This is particularly true for infrastructure projects, where technical and institutional complexity increases the risks and challenges of facing litigation. Early studies have been focused on subject matter experts' ability to identify various risks and potential disputes. This paper presents a novel approach to estimate litigation risk using widely accessible social media data. By defining levels of legal risk and potential plaintiff's profile, this paper presents a data-driven risk estimation model to track litigation risk in real time. The Purple Line transit project from the state of Maryland is used as an example to demonstrate the litigation risk estimation model with continuous retrieval of Twitter data for analysis. The implication and effectiveness will be discussed in this case study. (C) 2019 American Society of Civil Engineers.
引用
收藏
页数:9
相关论文
共 32 条
  • [1] [Anonymous], 2007, PLAIN ENGL GUID CLEA
  • [2] [Anonymous], 2002, INTERNET WEB INF SYS
  • [3] Austermuhle M., 2017, FEDERAL COURT DISMIS
  • [4] Bajaj D., 1997, CONSTR MANAG ECON, V15, P363
  • [5] Caldwell LK, 1998, HARVARD ENVIRON LAW, V22, P203
  • [6] Carter B., 2014, FRIENDS CAPITAL CRES
  • [7] Chen Z., 2013, NAACL-HLT, P1041
  • [8] Di Caro M., 2016, ANOTHER DELAY PURPLE
  • [9] A Bayesian Network-Based Approach to the Critical Infrastructure Interdependencies Analysis
    Di Giorgio, Alessandro
    Liberati, Francesco
    [J]. IEEE SYSTEMS JOURNAL, 2012, 6 (03): : 510 - 519
  • [10] Inferring on the Intentions of Others by Hierarchical Bayesian Learning
    Diaconescu, Andreea O.
    Mathys, Christoph
    Weber, Lilian A. E.
    Daunizeau, Jean
    Kasper, Lars
    Lomakina, Ekaterina I.
    Fehr, Ernst
    Stephan, Klaas E.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2014, 10 (09)