Social Media as a Tool to Understand Behaviour on the Railways

Author(s):  
David Golightly ◽  
Robert J. Houghton

Social media plays an increasing role in how passengers communicate to, and about, train operators. In response, train operators and other rail stakeholders are adopting social media to contact their users. There are a number of opportunities for tapping this big data information stream through the overt use of technology to analyse, filter and present social media, including filtering for operational staff, or sentiment mapping for strategy. However, this analysis is predicated on a number of assumptions regarding the manner in which social media is currently being used within a railway context. In the following chapter, we present data from studies of rail social media that shed light on how big data analysis of social media exchange can support the passenger. These studies highlight important factors such as the broad range of issues covered by social media (not just disruption), the idiosyncrasies of individual train operators that need to be taken into account within social media analysis, and the time critical nature of information during disruption.

Author(s):  
David Golightly ◽  
Robert J. Houghton

Social media plays an increasing role in how passengers communicate to, and about, train operators. In response, train operators and other rail stakeholders are adopting social media to contact their users. There are a number of opportunities for tapping this big data information stream through the overt use of technology to analyse, filter and present social media, including filtering for operational staff, or sentiment mapping for strategy. However, this analysis is predicated on a number of assumptions regarding the manner in which social media is currently being used within a railway context. In the following chapter, we present data from studies of rail social media that shed light on how big data analysis of social media exchange can support the passenger. These studies highlight important factors such as the broad range of issues covered by social media (not just disruption), the idiosyncrasies of individual train operators that need to be taken into account within social media analysis, and the time critical nature of information during disruption.


2019 ◽  
Author(s):  
Sheela Singh ◽  
Priyanka Arya ◽  
Alpna Patel ◽  
Arvind Kumar Tiwari

2019 ◽  
Vol 8 (S1) ◽  
pp. 1-3
Author(s):  
S. Lingeswari

Few years back the Internet usage was very low when compared now-a-days. It has become a very important part in our day to day life. Billions of people are using social media and social networking every day all over the world. Such a huge number of people generate a large number of data which have become a quite difficult to manage. Here solving these types of problem by using a term called Big Data. It refers to the huge number of datasets. Data may be structured, unstructured or semi structured. Big data is defined by three Vs such as Volume, Velocity and Variety. Big Data use an algorithm known as Map Reduce algorithm. Large number of datasets is very difficult to manage. This problem has been solved using Map Reduce algorithm. In this paper, we focus to analyze social media through big data using Map Reduce algorithm.


2013 ◽  
Vol 13 (2) ◽  
pp. 211-219 ◽  
Author(s):  
Byoung-Yup Lee ◽  
Jong-Tae Lim ◽  
Jaesoo Yoo

2020 ◽  
Vol 16 (2) ◽  
pp. 126-136
Author(s):  
Carlos Roberto Val�ncio ◽  
Luis Marcello Moraes Silva ◽  
William Tenório ◽  
Geraldo Francisco Donegá Zafalon ◽  
Angelo Cesar Colombini ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document