Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation
Sustainability
; 14(9):5711, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1847403
ABSTRACT
We live in a complex world characterised by complex people, complex times, and complex social, technological, economic, and ecological environments. The broad aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we introduce the concept of deep journalism, a data-driven deep learning-based approach, to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems that industry and academia seem to ignore. In contrast, academia produces much broader and deeper knowledge on subjects such as pollution that are not sufficiently explored in industry. Our deep journalism approach could find the gaps in information and highlight them to the public and other stakeholders.
Environmental Studies; natural language processing (NLP); topic modelling; BN1 -https://media.proquest.com/media/hms/PFT/1/ykgsM?_a=ChgyMDIyMDUxNzE1MjYzMjYwMzoyNzA4NTkSBTg4MjU5GgpPTkVfU0VBUkNIIg4xNTguMTExLjIzNi45NSoHMjAzMjMyNzIKMjY2MzEyMzcxNzoNRG9jdW1lbnRJbWFnZUIBMFIGT25saW5lWgJGVGIDUEZUagoyMDIyLzAxLzAxcgoyMDIyLzEyLzMxegCCATJQ; ERT; transportation; newspaper; magazine; academic research; journalism; deep learning; smart cities; Failure; Collaboration; Datasets; Newspapers; Science; Politics; Transportation industry; Clustering; Decision making; COVID-19; Big Data; Decision analysis; Pandemics; Sustainability; Taxonomy; Coronaviruses; Parameters; Government; Data sets
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Sustainability
Year:
2022
Document Type:
Article
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