Social Impact and Social Media Analysis Relating to Big Data

2016 ◽  
pp. 293-313 ◽  
Author(s):  
Nirmala Dorasamy ◽  
Nataša Pomazalová
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Impact ◽  
Media Analysis ◽  
Social Media Analysis

Social Media Analysis through Big Data Analytics: A Survey

2019 ◽  
Author(s):  
Sheela Singh ◽  
Priyanka Arya ◽  
Alpna Patel ◽  
Arvind Kumar Tiwari
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Media Analysis ◽  
Social Media Analysis

Social Media as a Tool to Understand Behaviour on the Railways

2021 ◽  
pp. 793-808
Author(s):  
David Golightly ◽  
Robert J. Houghton
Keyword(s):  
Social Media ◽  
Big Data ◽  
Big Data Analysis ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Use Of Technology ◽  
Nature Of Information ◽  
Information Stream ◽  
Time Critical ◽  
Shed Light

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.

scholarly journals Social Media Analysis through Big Data Using Map Reduce Algorithm

2019 ◽  
Vol 8 (S1) ◽  
pp. 1-3
Author(s):  
S. Lingeswari
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Use ◽  
Media Analysis ◽  
Internet Usage ◽  
Map Reduce ◽  
Social Media Analysis ◽  
Huge Number ◽  
The World

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.

scholarly journals Utilization of Social Media Analysis using Big Data

2013 ◽  
Vol 13 (2) ◽  
pp. 211-219 ◽  
Author(s):  
Byoung-Yup Lee ◽  
Jong-Tae Lim ◽  
Jaesoo Yoo
Keyword(s):  
Social Media ◽  
Big Data ◽  
Media Analysis ◽  
Social Media Analysis

Social Media as a Tool to Understand Behaviour on the Railways

2018 ◽  
pp. 224-239
Author(s):  
David Golightly ◽  
Robert J. Houghton
Keyword(s):  
Social Media ◽  
Big Data ◽  
Big Data Analysis ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Use Of Technology ◽  
Nature Of Information ◽  
Information Stream ◽  
Time Critical ◽  
Shed Light

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.

scholarly journals Data Warehouse Design to Support Social Media Analysis in a Big Data Environment 

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 ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Warehouse ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Data Environment ◽  
Warehouse Design

scholarly journals The Rebellious Social Network Reaction to COVID-19

2020 ◽  
Vol 65 (1) ◽  
pp. 111-130
Author(s):  
Ștefana Cioban ◽  
Dragoş Vîntoiu
Keyword(s):  
Social Media ◽  
Social Impact ◽  
Media Analysis ◽  
Social Media Analysis ◽  
State Of Emergency ◽  
Natural Tendency ◽  
The Social ◽  
Depth Analysis ◽  
Current Context ◽  
Social Networking Tools

AbstractGathering social media content and analysing the heavy and unstructured text coming from posts, comments and reactions can come as a powerful tool in understanding how people react to the information they receive. In this article we present the results from a social media analysis of 10771 headlines, with their subsequent text bodies and comments posted in a subreddit destined for Romanians during the state of emergency declared in Romania, from March 16 to May 15, 2020. Our objective was to model the main topics debated by this targeted population of people that tend to use Reddit to discuss current issues and to identify the sentiment polarity towards these topics. As expected, Romanians are mostly concerned with their social condition in the context of the pandemic caused by CoVID-19, as our research has revealed a word frequency for the term “Coronavirus” prominently higher than any other preferred term. However, the analysis brings up a surprising turnaround as the overall sentiment of the text posted in this dataset is predominantly neutral with a higher frequency of positive posts compared to the negative ones. This was unforeseen by our initial expectations: a natural tendency to more negative posts than positive considering the context of the chosen study period. Moreover, when compared to the time series of the CoVID-19 infections and caused deaths in Romania, spikes of extremely high or low mean sentiment scores per day can be correlated to the fluctuations of the declared cases. Not only does this bring us closer to understanding the social impact of CoVID-19 in the current context, but the outcome of this analysis can be easily extrapolated for further investigations upon other social networking tools or for more in-depth analysis on our studied corpus.

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