Guardian Transportation Dataset (GT202201)

Published: 16 May 2022| Version 1 | DOI: 10.17632/yvxx6s5xhh.1
Contributors:
Istiak Ahmad,
,
,

Description

The Guardian Transportation Dataset (GT202201) comprises all transport-related articles from a UK-based newspaper “The Guardian”. We collected the dataset using a web scraping technique. All the articles were collected from the newspaper that contain the word “transport” in the title of the news, the full text of the news article, or the meta-information about the article. The dataset comprises about 14,855 articles belonging to the time period from September 1825 to January 2022. Each document in the dataset has five attributes: News Article, Heading, Article Link, Topic, and Publication Date. This dataset was built to discover parameters for public, governance, and political aspects of transportation as part of our deep journalism approach and DeepJournal tool. The deep journalism approach uses big data, deep learning, and digital methods 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 discovered a total of 25 parameters from this dataset and grouped them into 6 macro-parameters, namely Road Transport, Rail Transport, Air Transport, Crash & Safety, Disruptions & Causes, and Employment Rights, Disputes, & Strikes. The other two transportation datasets related to this dataset used in the deep journalism approach include Traffic Technology Today Transportation Dataset (TTIT202201: http://dx.doi.org/10.17632/k4bgjwktyp.1) and Web of Science Transportation Dataset (WST202201: http://dx.doi.org/10.17632/tnfw2dh5nj.1). Further details of the dataset, its collection, and usage for deep journalism including detection of the multi-perspective parameters for transportation can be found in our article here: https://doi.org/10.3390/su14095711.

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Institutions

King Abdulaziz University

Categories

Computer Science, Transport, Natural Language Processing, Machine Learning, Deep Learning, Textual Analysis

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