scholarly journals Propaganda 2.0: Herman and Chomsky’s Propaganda Model in the Age of the Internet, Big Data and Social Media in the Age of the Internet, Big Data and Social Media

2018 ◽  
pp. 71-92 ◽  
Author(s):  
Christian Fuchs ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
Propaganda Model

scholarly journals EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

2021 ◽  
pp. 074391562199967
Author(s):  
Raffaello Rossi ◽  
Agnes Nairn ◽  
Josh Smith ◽  
Christopher Inskip
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Volume Content ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
The Internet ◽  
Policy Recommendations ◽  
Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

scholarly journals Making Inferences Using Incidentally Collected Data

2016 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
User Interactions ◽  
Social Media Data ◽  
Broad Definition ◽  
User Data ◽  
The Cost ◽  
Media Data

This chapter discusses the use of large quantities of incidentallycollected data (ICD) to make inferences about politics. This type of datais sometimes referred to as “big data” but I avoid this term because of itsconflicting definitions (Monroe, 2012; Ward & Barker, 2013). ICD is datathat was created or collected primarily for a purpose other than analysis.Within this broad definition, this chapter focuses particularly on datagenerated through user interactions with websites. While ICD has beenaround for at least half a century, the Internet greatly expanded theavailability and reduced the cost of ICD. Examples of ICD include data onInternet searches, social media data, and user data from civic platforms.This chapter briefly explains some sources and uses of ICD and thendiscusses some of the potential issues of analysis and interpretation thatarise when using ICD, including the different approaches to inference thatresearchers can use.

Big Data: A Very Short Introduction

2017 ◽  
Author(s):  
Dawn E. Holmes
Keyword(s):  
Social Media ◽  
Big Data ◽  
Online Shopping ◽  
The Internet ◽  
Gps Data ◽  
Security Risks ◽  
Short Introduction ◽  
Business World ◽  
The World ◽  
Diverse Cultures

Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world’s population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, videos, and photos; all our social media traffic; our online shopping; even the GPS data from our cars. Big Data: A Very Short Introduction explains how big data works and is changing the world around us, the effect it has on our everyday lives and in the business world, and it considers the attendant security risks.

Aporia and Wonder in the Age of Big Data

2019 ◽  
Vol 23 (2) ◽  
pp. 137-152
Author(s):  
Murray Skees ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Media Use ◽  
Internet User ◽  
The Other ◽  
The Internet ◽  
Social Media Use ◽  
Other Hand

My argument in this paper is given in two parts. In Part I, I review the ancient understanding of aporia, focusing on works by Plato and Aristotle. I illustrate two ways of understanding aporia: “cathartic” and “zetetic.” Cathartic aporia refers to the experience of being purged of hubris and ignorance through the dialectic. Zetetic aporia, on the other hand, requires us to engage in, recognize, and work through certain philosophical puzzles or problems. In Part II, I discuss the idea of Big Data and then argue that in the “age of answers” neither conception of aporia appears to be necessarily cultivated by the average Internet user. Our experience of wonder suffers when we rely so heavily on the Internet as a “surrogate expert,” and when our social media use betrays the fact that we always seem to gravitate towards the like-minded.

scholarly journals An Agenda in the Interest of Audiences: Facing the Challenges of Intrusive Media Technologies

2018 ◽  
Vol 20 (2) ◽  
pp. 184-198 ◽  
Author(s):  
Brita Ytre-Arne ◽  
Ranjana Das
Keyword(s):  
Social Media ◽  
Big Data ◽  
Internet Of Things ◽  
Emerging Technologies ◽  
Research Network ◽  
The Internet ◽  
Audience Research ◽  
Media Technologies ◽  
Critical Data ◽  
The Internet Of Things

This article formulates a five-point agenda for audience research, drawing on implications arising out of a systematic foresight analysis exercise on the field of audience research, conducted between 2014 and 2017, by the research network Consortium on Emerging Directions in Audience Research (CEDAR). We formulate this agenda in the context of the rapid datafication of society, amid emerging technologies, including the Internet of Things, and following a transformative decade, which overlapped with the pervasion of social media, proliferation of connected gadgets, and growing interest in and concern about big data. The agenda we formulate includes substantial and intellectual priorities concerning intrusive technologies, critical data literacies, labour, co-option, and resistance, and argues for the need for research on these matters, in the interest of audiences.

Marketing 4.0

Web Services ◽  
2019 ◽  
pp. 2172-2195 ◽  
Author(s):  
Ana Isabel Jiménez-Zarco ◽  
Asher Rospigliosi ◽  
María Pilar Martínez-Ruiz ◽  
Alicia Izquierdo-Yusta
Keyword(s):  
Social Media ◽  
Big Data ◽  
Marketing Strategies ◽  
The Internet ◽  
Academic Journals ◽  
Is Research ◽  
Brand Relationship ◽  
Marketing Approach ◽  
Internet And Social Media

Marketing evolves in parallel with technology. During the last five years, Marketing 3.0 has become the most innovative marketing approach, but of growing, is research focusing on Marketing 4.0: the marketing of big data. Much has been speculated, but academic journals have published little about Marketing 4.0. Maybe, because the total understanding of Marketing 4.0 requires: firstly, a depth knowledge about the evolution of marketing, especially about Marketing 3.0, and secondly, an analysis of how a range of technology –not only the Internet and social media- can be used to design marketing strategies that enhance the brand-consumer relationship. Taking into account how consumers' behavior has been changing since the beginning of this century, this chapter seeks to review Marketing 4.0 concepts, analyzing how big data can be used to enhance the consumer-brand relationship.

Sentiment Analysis on Twitter Data using Big Data

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Obada Almonajed ◽  
Samed Jukić ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
The Internet ◽  
Twitter Data

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.

Data Management and Big Data Analytics

2019 ◽  
pp. 1-23
Author(s):  
Vellingiri Jayagopal ◽  
Basser K. K.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Digital Images ◽  
Big Data Analytics ◽  
Database Systems ◽  
The Internet ◽  
Processing Capacity

The internet is creating 2.5 quintillion bytes of data, and according to the statistics, the percentage of data that has been generated from last two years is 90%. This data comes from many industries like climate information, social media sites, digital images and videos, and purchase transactions. This data is big data. Big data is the data that exceeds storage and processing capacity of conventional database systems. Data in today's world (big data) is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc. About 80% of data captured today is unstructured. All this data is also big data.

scholarly journals How internet users in Moscow, Saint-Petersburg and Sevastopol Reacted to the Covid-19 Pandemic (Based on Material from a Media-Analysis Study)

2020 ◽  
Vol 11 (3) ◽  
pp. 121-142
Author(s):  
Olga V. Yarmak ◽  
Ekaterina V. Strashko ◽  
Tatyana V. Shkayderova
Keyword(s):  
Social Media ◽  
Big Data ◽  
Media Analysis ◽  
The Internet ◽  
Human Beings ◽  
Analysis Study ◽  
Saint Petersburg ◽  
Post Conflict ◽  
Internet Users ◽  
At Home

This article presents the results of the authors’ media-analysis study of social media in central federal cities – Moscow, Saint Petersburg and Sevastopol – on search queries such as “coronavirus”, “covid 19”, “sitting at home” and “stay at home” which came up during the first three weeks of self-isolation – from March 23rd to April 12th 2020. This allowed for analyzing trends in social media threads that emerged due to the lockdown and the epidemiological crisis, and for understanding the specifics of how a certain response to common threats and challenges was formulated in regional online-communities. The cybermetric analysis of social media conducted by the authors, using a big data mining system for monitoring and analyzing social networks called “Medialogiya”, allowed for tracking the develpment of media and communication trends associated with an ambiguous evaluation on behalf of internet users of the situation with the coronavirus pandemic and the lockdown, as well as the emergence of new digital forms of interaction used by individuals in their day to day affairs. The study was carried out within the framework of a project called “Developing methods of agent modeling and big data for analyzing social media in post-conflict societies”. The research group defines the information attained from “Medialogiya’s” system as “big sociological data”, which allows for analyzing interactions between human beings and information, as well as their behavior in the internet. The research results prove the development of regional specifics when discussing the pandemic and the issues associated with the ensuing lockdown experienced by internet users from Moscow and Sevastopol, which speaks to the emergence of a sort of regional solidarity in the face of this new threat and the challenges it poses. Sevastopol’s segment of the internet displayed not only regional, but also “peninsula” solidarity. New conditions of everyday life brought us to view the new viral infection as a socio-political phenomenon, which in turn creates the grounds for new forms of consolidation within society, caused by various reactions to the crisis. One of the tasks currently faced by social sciences would be developing scenarios and outlines to explain the phenomenon in question.

scholarly journals Misinformation on Misinformation: Conceptual and Methodological Challenges

2021 ◽  
Author(s):  
Sacha Altay ◽  
Manon Berriche ◽  
Alberto Acerbi
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science ◽  
Future Research ◽  
Reception Studies ◽  
The Internet ◽  
Research Needs ◽  
Methodological Challenges ◽  
The Impact ◽  
Practical Implications

Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research questioning the use of big data in social science and reception studies, we identify six misconceptions about misinformation and examine the conceptual and methodological challenges they raise. The first three misconceptions concern the prevalence and circulation of misinformation. First, the internet is not rife with misinformation or news, but with memes and entertaining content. Second, scientists focused on social media because it is methodologically convenient, but misinformation is not just a social media problem. Third, falsehoods don’t spread faster than the truth, how we define (mis)information influences our results and their practical implications. The second three misconceptions concern the impact and the reception of misinformation. First, people don’t believe everything they see on the internet: sheer volume of engagement should not be conflated with belief. Second, misinformation’s influence on people’s behavior is overblown since it often preaches to the choir. Third, people are more likely to be uninformed than misinformed, surveys overestimate misperceptions and say little about the causal influence of misinformation. To appropriately understand and fight misinformation, future research needs to address these challenges.

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