Social Media in Transportation Research and Promising Applications

2018 ◽  
pp. 23-45 ◽  
Author(s):  
Zhenhua Zhang ◽  
Qing He
Keyword(s):  
Social Media ◽  
Transportation Research

scholarly journals Investigating transportation research based on social media analysis: a systematic mapping review

2021 ◽  
Author(s):  
Tasnim M. A. Zayet ◽  
Maizatul Akmar Ismail ◽  
Kasturi Dewi Varathan ◽  
Rafidah M. D. Noor ◽  
Hui Na Chua ◽  
...  
Keyword(s):  
Social Media ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Systematic Mapping ◽  
Mapping Review ◽  
Transportation Research ◽  
Systematic Mapping Review

Social media based transportation research: the state of the work and the networking

2017 ◽  
Vol 4 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Yisheng Lv ◽  
Yuanyuan Chen ◽  
Xiqiao Zhang ◽  
Yanjie Duan ◽  
Naiqiang Li Li
Keyword(s):  
Social Media ◽  
The State ◽  
Transportation Research

scholarly journals Inferring Activities from Social Media Data

2017 ◽  
Vol 2666 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou ◽  
Georgia Aifadopoulou ◽  
Loukas Dimitriou
Keyword(s):  
Social Media ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Use Of Social Media ◽  
Definition Of ◽  
The Individual ◽  
Transportation Research ◽  
Direct Use ◽  
Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

Twelve-Year Analysis of Transportation Research Board Annual Meeting’s Official Hashtag

2021 ◽  
pp. 036119812110372
Author(s):  
Subasish Das ◽  
Anandi Dutta
Keyword(s):  
Social Media ◽  
Traffic Safety ◽  
General Trend ◽  
Annual Conference ◽  
Transportation Engineering ◽  
Active Engagement ◽  
Social Media Platforms ◽  
Mode Of Transportation ◽  
Virtual Platform ◽  
Transportation Research

Twitter, among other social media platforms, has become more popular over time. Social media platforms underpin the way scholars share ideas, propagate the latest emergence of evidence, and adopt new practices, by providing a virtual platform for interacting, socializing, and sharing information from academic conferences with the outside world beyond the physical location. The Transportation Research Board Annual Meeting (TRBAM) is the largest annual conference for transportation engineering and science, and the hashtag for the conference, #TRBAM, was used first in 2009. This paper aims to perform an observational study based on the interactions on Twitter surrounding this hashtag by collecting all original #TRBAM tweets for 12 years (2009–2020). A general trend in the data is that the quantitative measures (tweets, retweets, and favorites) are all much higher during the conference month compared with other months. Top trending topics included: transit, safety, bike or non-motorized mode of transportation, data, and freight. Overall, the communication pattern shows more dispersion than the central tendency. The findings of this study highlight the need to implement and improve strategies to help transportation research communities encourage continuous and active participation during and after conferences. More active engagement among attendees will help maintain the momentum of information sharing and expand the traffic safety platform globally.

Where Social Media Meets AAC

ASHA Leader ◽  
2015 ◽  
Vol 20 (7) ◽  
Author(s):  
Vicki Clarke
Keyword(s):  
Social Media

Making Twitter Work For You

ASHA Leader ◽  
2013 ◽  
Vol 18 (5) ◽  
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Speech Language Pathologists

As professionals who recognize and value the power and important of communications, audiologists and speech-language pathologists are perfectly positioned to leverage social media for public relations.

Doctors employ social media

2013 ◽  
Vol 44 (1) ◽  
pp. 4
Author(s):  
Jane Anderson
Keyword(s):  
Social Media
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