NDLI: Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data

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Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data

Content Provider	IET Digital Library
Author	Muller, Susan M. Grant Tzur, Ayelet Gal Minkov, Einat Nocera, Silvio Kuflik, Tsvi Shoor, Itay
Abstract	Social media data now enriches and supplements information flow in various sectors of society. The question addressed here is whether social media can act as a credible information source of sufficient quality to meet the needs of transport planners, operators, policy makers and the travelling public. A typology of primary transport data needs, current and new data sources is initially established, following which this study focuses on social media textual data in particular. Three sub-questions are investigated: the potential to use social media data alongside existing transport data, the technical challenges in extracting transport-relevant information from social media and the wider barriers to the uptake of this data. Following an overview of the text mining process to extract relevant information from the corpus, a review of the challenges this approach holds for the transport sector is given. These include ontologies, sentiment analysis, location names and measuring accuracy. Finally, institutional issues in the greater use of social media are highlighted, concluding that social media information has not yet been fully explored. The contribution of this study is in scoping the technical challenges in mining social media data within the transport context, laying the foundation for further research in this field.
Starting Page	407
Ending Page	417
Page Count	11
ISSN	1751956X
Volume Number	9
e-ISSN	17519578
Issue Number	Issue 4, May (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/9/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2013.0214
Journal	IET Intelligent Transport Systems
Publisher Date	2014-11-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Collection Data Mining Document Processing Technique Information Analysis And Indexing Information Extraction Information Network Institutional Issues Knowledge Engineering Technique Location Names Measuring Accuracy Ontologies Sentiment Analysis Social Media Source Social Networking (online) Text Analysis Text Mining Process
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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