The Impact of Social Media Usage on Transport Policy: Issues, Challenges and Recommendations

Gal‐Tzur, Ayelet; Grant-Muller, Susan; Minkov, Einat; Nocera, Silvio

doi:10.1016/j.sbspro.2014.01.128

Cited by 51 publications

(35 citation statements)

References 13 publications

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“…It is important to note that although only some one percent of tweets can be obtained using public streaming API, the absolute quantity of the sample is still relatively large [71]. In the following analysis, we focus on transportation-related topics given the ongoing LRT project has elevated the issue of transportation within the Region and the general importance of transportation in many other locales [72].…”

Section: Datamentioning

confidence: 99%

Understanding Public Opinions from Geosocial Media

Zhang

Feick

2016

IJGI

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Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments' needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens' transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens' concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment.

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Section: Datamentioning

confidence: 99%

Understanding Public Opinions from Geosocial Media

Zhang

Feick

2016

IJGI

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“…Natural language may also include irony and sarcasm which add to the challenge [51]. Analysis of transport sentiment data [52] illustrated the difficulty with sarcasm in service quality related text. The message 'train service is just fantastic' needs the surrounding context for interpretation.…”

Section: Sentiment Analysismentioning

confidence: 99%

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

Grant-Muller

Gal‐Tzur²,

Minkov

et al. 2015

IET Intelligent Transport Systems

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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.

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“…Similarly, Gal-Tzur, Grant-Muller and Minkov (2014) describe the typology of information related to the functions of SM, which are mostly used by operators in the transport industry to communicate with users. These are: informational function, referring to informing the users about the changes introduced on the website of the entity, about the undertaken initiatives, organized events, etc.…”

Section: No 2/2015mentioning

confidence: 99%

Social media and tourism. The analysis of selected current and future research trends

Stepaniuk

2015

Zeszyty Naukowe Uniwersytetu Szczecińskiego. Service Management

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Abstr actThe issue of the article concerns the analysis of current research trends related to tourism and social media. Based on the results of a critical analysis of the literature, an outline of the directions of future scientific research in this area was also shown. These studies of interdisciplinary nature would include the issues from the field of social and economic sciences with particular emphasis on crowdsourcing and the possibilities of its implementation in the social media for the development of tourism in the region.

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The Impact of Social Media Usage on Transport Policy: Issues, Challenges and Recommendations

Cited by 51 publications

References 13 publications

Understanding Public Opinions from Geosocial Media

Understanding Public Opinions from Geosocial Media

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

Social media and tourism. The analysis of selected current and future research trends

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