Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data. Issue 4 (1st May 2015)

• Record Type: Journal Article
• Authors: Grant‐Muller, Susan M.; Gal‐Tzur, Ayelet; Minkov, Einat; Nocera, Silvio; Kuflik, Tsvi; Shoor, Itay
• Is Part Of: IET intelligent transport systems. Volume 9:Issue 4(2015)
• Page Start: 407
• Page End: 417
• Publication Date: 2015-05-01
• Subjects: text analysis -- data mining -- social networking (online); data mining -- institutional issues -- measuring accuracy -- location names -- ontologies -- sentiment analysis -- information extraction -- text mining process -- social media sources -- data collection; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2013.0214
• Languages: English
• ISSNs: 1751-956X

Abstract

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.