Index

Citation

Tong, J. (2022), "Index", Journalism, Economic Uncertainty and Political Irregularity in the Digital and Data Era, Emerald Publishing Limited, Leeds, pp. 135-141. https://doi.org/10.1108/978-1-80043-558-220221011

Publisher

:

Emerald Publishing Limited

Copyright © 2023 Jingrong Tong

---

INDEX

Academics

, 15

Adversary

, 83

Advertisers

, 13

decision

, 28

Advertising

, 13

Advertising Standards Authority Ltd. (ASA)

, 26

African newsrooms

, 34

After the Storm’ interactive documentary

, 54

Age

, 13

Airbnb (Economy platform service providers)

, 16

Algorithmisation

algorithmised audience measurement

, 69

of news business

, 44

of society

, 17

Algorithms

, 3, 16, 44, 96

different scenarios of using

, 32–35

for journalism

, 31–32

Alphabet Company

, 56

Amazon

, 6, 16, 19–20, 32, 41, 46, 56

Amazon Video (social media platform)

, 37

Amazon Web Services (AWS)

, 38

promotion video of

, 40

American journalism

, 65

Analytics

, 55

Anglo-American societies

, 8

APIs

, 46

Apple News

, 6, 18

Apple’s iCloud (social media platform)

, 37

Artificial Intelligence (AI)

, 16

technologies

, 33

Audiences

, 58

data

, 22

emotions

, 23

measurements

, 22, 69

participants

, 62

scarcity of audiences attention

, 96

Audio semantic processing

, 40

Australian Broadcasting Corporation (ABC)

, 54

Authoritarianism

, 89

Auto transcribing

, 45

Automated audience analytics

, 69

Automated tapeless production system

, 32

Automated text analysis

, 45

Banks

, 16, 42, 56

BBC Academy

, 22, 31, 40

@BBCNews (British news media Twitter account)

, 72, 75

Belgian context

, 12

Bias

, 23

Big data

, 40

Big tech companies

, 19–21

BILD

, 66

Bloomberg’s Cyborg Project

, 33

British news media

statistics of tweets

, 74

tabloidisation in tweets

, 72–76

Twitter accounts

, 72

British newspapers

, 65

revenue

, 7

Broadsheets

, 76

BuzzFeed (Digital media companies)

, 13, 19

California Consumer Privacy Act (CCPA)

, 87

Celebrities

, 15, 88

Channel 4 organisation

, 42

Chartbeat

, 16

Chicago Tribune, The

, 88

Chinese nationals

, 16

Chinese society

, 16

Clickbaits

, 23, 67

Cloud

, 37

cloud-tag extraction

, 45

services providers

, 41

Cloud computing

, 3, 20, 32, 35–37, 44, 96

adoption by news media

, 37–41

for journalism

, 31–32

potential issues of using cloud computing for journalism

, 41–44

technologies

, 35

Cloud computing service providers (CCSP)

, 42

Cloud model

, 35

broad network access

, 35

measured service

, 36

on-demand self-service

, 35

rapid elasticity

, 36

resource pooling

, 36

CNN

, 42

Commercial companies

, 15, 56

Commercial news media

, 86

Commercial newspapers

, 82

Committee of Advertising Practice Ltd. (CAP)

, 26

Communication channels

, 91

Communities

, 16

Community Cloud

, 37

Competitors

, 19

Computers

, 31, 56

scientists

, 62

tools

, 49, 56

Content management system

, 32

Conventional journalistic methods

, 51

Cookies algorithm

, 21

Copyright infringement

, 43

Corruption

, 23

Cost-per-click (CPC)

, 27

COVID-19

, 1, 89

case

, 4

case study of data journalism during

, 59–62

Community Mobility data

, 56

data

, 61

pandemic

, 1, 9–10, 13, 59, 61

vaccine rollout

, 66

CPS

, 40

Crimes

, 11, 64

Crises

, 54

Crosschecking

, 57

Cross-referencing methods

, 51

Curiosity

, 23

D3. js

, 55

Daily active users (DAUs)

, 20

Daily Mail, The

, 25

Daily Telegraph, The

, 24, 31, 81

@DailyMirror (British news media Twitter account)

, 72–76

Data

, 56

analysts

, 62

analytics

, 6

data-related domains

, 62

era

, 11, 15

journalists

, 57, 59

project managers

, 62

storage

, 35

verification

, 57

visualizations

, 58–59

Data journalism

, 3–4, 50, 59

importance of data journalism

, 52–55

limitations of data journalism

, 55–59

promise and problems of data reporting

, 59–62

rise of

, 49–52

Data Protection Act (2018)

, 87, 90

Data reporting

, 52

skills

, 49

Databases

, 46

Datafication of society

, 17

Defamation Act

, 87

Deliveroo (Economy platform service providers)

, 16

Democratic countries

, 2, 90

Design subjectivity

, 55

Didi (Economy platform service providers)

, 16

Digital advertising

, 24–25

revenues

, 19

Digital audiences

, 21–24

Digital cameras

, 31

Digital chips

, 31

Digital data

, 55

Digital era

, 11, 15

Digital living

, 11

Digital logic

, 66

importance of human interest

, 67–70

tabloidisation boundaries

, 70–72

tabloidisation of online content

, 66–67

Digital media companies

, 13

Digital Media Initiative (DMI)

, 39

Digital technology

, 2–3, 17, 96

Digital transformation of newsrooms, continuity of

, 31–32

Disasters

, 54, 64

Disorientation

, 17

Double-page spread (DPS)

, 28

Dream Term

, 27

Dropbox (social media platform)

, 37, 42

Earthquakes

, 11

Eats (Economy platform service providers)

, 16

eBay

, 16

‘Echo-chamber’ effect

, 33

Economic challenges

, 3

Economic uncertainty era

, 96

Economy platform service providers

, 16

Education

, 6

Elections

, 50, 54

Electricity

, 37

Employed women

, 11

Entertainment companies

, 19

Entrepreneurial journalism

, 19

Excel

, 33, 55

Extensible Markup Language (XML)

, 42

Facebook

, 12, 16–18, 20–22, 28, 37, 50, 56, 66, 72

algorithms

, 32

attention-based ad-charge model

, 28

news feed algorithm

, 14, 22

Facebook Journalism Project

, 21

Facebook-Cambridge Analytica data scandal

, 88

False news

, 23

Family Educational Rights and Privacy Act (FERPA)

, 87

FAZ

, 66

Financial crisis

, 52

Financial income

, 71

Financial Times, The

, 27, 39, 60–61, 82

Fitbit

, 15

Flourish

, 55

FOI laws

, 52, 89

Freedom of Information Act (FOIA)

, 56, 87–88

Funding strategies

, 24–29

Garmin

, 15

Gender

, 13

Gmail

, 37

Good journalists

, 34

Google

, 12, 16–18, 20–22, 28, 32, 41, 46, 56

algorithms

, 32

attention-based ad-charge model

, 28

docs

, 37

news ranking algorithms

, 22

search algorithms

, 46

tools

, 55

Google Ads

, 27

Google News

, 18

Google News Initiative

, 21

Google Trends

, 56

Governments

, 15–16, 92

documents

, 57

officials

, 55

and politicians

, 88–92

surveillance

, 43

Guardian, The

, 19–20, 24–25, 27, 31, 39, 42, 49, 61, 72, 75, 85

Haemorrhaging

, 8

Handy (Economy platform service providers)

, 16

High-profile leaks

, 86

Hong Kong’s FactWire

, 29

HU2 News

, 19

Huffington Post

, 19

Human interest

, 64

importance of

, 67–70

Human Rights Act (1998)

, 87

Human societies

, 9, 83

Hybrid Cloud

, 37

Income

, 13

Independent, The

, 72

Independent journalism start-ups

, 29

Information and communication technologies

, 11, 31

Information industry

, 6

Information processing system

, 46

Infotainment

, 65

Infrastructure as a Service (IaaS)

, 37

Infrastructures

, 44

Instagram

, 37, 50, 66–67

International Consortium of Investigative Journalists (ICIJ)

, 84

International news media

, 29, 60, 85

International News Media Association (INMA)

, 25

Internet

, 2, 18, 31, 67

competing for internet users attention

, 15

platform

, 83

Interviewing methods

, 45, 51

Interviews

, 15, 53

Inverse Document Frequency (IDF)

, 45

Investigative journalism

, 53

ITVorganisation

, 42

Jabs Army campaign

, 78–79

Journalism

, 15

boom of leaks

, 84–85

confrontations between

, 80

different scenarios of using algorithms for

, 32–35

friends or foes

, 78–84

governments and politicians

, 88–92

implications for

, 44–47

leaks and exposes as threat to national security and privacy

, 85–88

new forms of

, 3

and politicians

, 80

potential issues of using cloud computing for

, 41–44

responses of news media

, 92–94

role in democracy

, 91

start-ups

, 19

transformation of

, 1, 96–97

Journalistic autonomy

, 35

Journalistic interviews

, 57

Journalistic legitimacy

, 71

Journalistic methods

, 53

Journalists

, 55

Latent Dirichlet Allocation analysis (LDA analysis)

, 73

Law

, 81

Leaks, boom of

, 84–85

Legitimacy

, 71

LinkedIn (social media platform)

, 37

Local News Chatter (LNC)

, 45

Lockdowns

, 89

‘Lower-profile politicians’ scandals

, 86

Machine learning

, 40

algorithms

, 41

MailOnline

, 26, 72, 74–76

Market forces

, 35

Material resources

, 17

Measuring audiences website

, 22

Media

, 6

ecosystem

, 22

media-constructed reality

, 61

reports

, 57

MediaHarmony (network file server)

, 39

Medical organisations

, 56

Medicine

, 6, 81

Migrants

, 52

Mirror, The

, 25, 88

Mobiles

, 9, 11

Multimedia departments

, 31

National news media

, 29

National Security and Privacy, leaks and exposes as threat to

, 85–88

Natural Language Generation (NLG)

, 33

Natural language processing (NLP)

, 45

Netflix (Entertainment companies)

, 19, 37

Network infrastructures

, 35

New Statesman

, 24

New York Consumer Privacy Act (NYPA)

, 87

New York Times, The

, 24, 42, 49, 51, 60–61, 80, 82, 85

News aggregators

, 6, 18

algorithms

, 32

News business

collapse of traditional funding models

, 7–9

competing with multiple players for attention

, 18–21

importance peril of attention

, 14–18

new funding strategies

, 24–29

reasons

, 9–14

striving to win over digital audiences

, 21–24

News Corporation

, 18

News Feed algorithm

, 72

News media

, 7, 13, 18–19, 24, 39, 41–42, 49, 58, 61, See also Social media)

adoption of cloud computing by

, 37–41

endeavouring to control

, 88–92

measuring audiences attention

, 22

newsbots

, 34

responses of

, 92–94

News of the World, The

, 88, 90

Newspapers

, 13, 26, 84

Newsrooms

, 31

continuity of digital transformation of

, 31–32

digital transformation

, 4

reliance on tech giants

, 32

NLP techniques

, 45

Non-news content products

, 46

Norway’s NRK

, 31

Objective journalism

, 91

Objective reality

, 61

Occupational Employment and Wage Statistics (OEWS)

, 8

Online content

, 66

Online shopping platforms

, 16

Open-Refine

, 55

Ordinary Internet users

, 19

Outrage

, 23

Panama Papers

, 84, 86

Panama revelations

, 86

Paradise Papers

, 84

Platform as a Service (PaaS)

, 36–37

Platformisation of society

, 17

Political authorities

, 92

Political dynamics

, 3

Political institutions

, 79

Political irregularities

in countries

, 1

era

, 96

Politicians

, 15, 83, 92

governments and

, 88–92

Populism

, 52

Portable devices

, 23

Portable digital devices

, 2

Poverty

, 52

Precision journalism

, 49

Prejudice

, 23

Print departments

, 31

Private Cloud

, 37

Professional fields

, 6

Public Cloud

, 37

Python

, 55

Quality journalism

, 96

Questionnaire surveys

, 15

R software

, 55

Racial discrimination

, 52

Reality

, 61

media-constructed

, 61

objective

, 61

subjective

, 61

Refugees

, 52

Regression analysis

, 33

Reporters Committee

, 92

Responsible journalism

, 81

Retail brands

, 26

Right of Access to Information Act (RTIA)

, 52

Robot reporters

, 33

Russia’s invasion of Ukraine

, 2

SAS statistical packages

, 55

Satellite Navigation system

, 32

Satellites

, 31

Saturday Evening Post, The

, 25

Scandals

, 64–65

Scholars

, 8

Search engines

, 56

algorithms

, 32–34

Semantic tagging annotation

, 45

Seoul Broadcasting System

, 49

Server

infrastructures

, 35

server-based content management system

, 40

Sex

, 65

Sheets

, 55

Single Column Centimetre (SCC)

, 28

Sinn Fein party

, 1

‘Sky News’ revenues

, 20

Sleaze

, 65

Small screens

, 10, 50, 62

Smartphones

, 23, 50

Snowden leak

, 86

Snowden revelations

, 85–86

Social Engine Optimisation (SEO)

, 23

Social entities

, 15

Social inequalities

, 23, 52

Social media

, 67

algorithms

, 32–34

companies

, 20, 56, 88

content

, 66

platforms

, 23, 37, 50, 72, 91

Social Media Optimisation (SMO)

, 23

Social platforms

, 16

companies

, 17

Social problems

, 23, 52

Social science methods

, 49

Social systems

, 35

Software as a Service (SaaS)

, 36

Southern Metropolitan Daily, the

, 82, 86

Southern Weekend, the

, 82, 86

Soviet authoritarian system

, 80

Spanish media groups

, 7

Spyware (phone-hacking technology)

, 43

Statistical analysis

, 49

Statistical models

, 33

Steel

, 17

Subjective reality

, 61

Subscription

, 7

Sun, The

, 25, 27, 72, 79, 81

Sun Xiaoguo case

, 82

Sunday Telegraph, The

, 31

Sunday Times, The

, 27

Supermarkets

, 56

@TheSun (British news media Twitter account)

, 72, 74–76

Surveys

, 49

Sveriges Television

, 49

Tableau

, 55

Tablets

, 9, 23, 50

Tabloidisation

, 4

boundaries

, 70–72

concept and existing views

, 64–66

as digital logic

, 66–72

of online content

, 66–67

in tweets of British news media’s twitter handles

, 72–76

Tabloids

, 25–26, 72, 75–76

@DailyMirror

, 74

@Mailonline

, 74

@TheSun

, 74

journalism

, 64

news media

, 65

tweets

, 76

Twitter handles

, 74, 76

Tabula

, 55

Targeted audiences

, 13

Tech giants

, 12, 18, 20–22, 28, 32, 46, 56

Technological advances

, 3

Telegraph, The

, 25–26, 72, 75

Television

, 13

Tencent’s WeChat

, 16

Term Frequency (TF)

, 45

Text extraction

, 45

TikTok

, 20, 66

Time

, 11

costs

, 11

Times, The

, 24–27

Traditional funding models

, 12

collapse of

, 7–9

Traditional journalistic methods

, 45

Traditional news media

, 13

Traditional professions

, 81

Trump presidency

, 3

Tsunamis

, 11

Twitter

, 16, 18, 28, 50, 66–67, 72

cards

, 23

Uber (Economy platform service providers)

, 16, 88

Uber Game

, 58

UK Office for National Statistics (ONS)

, 6n1

UK tabloids

, 88

UK’s privacy laws

, 87

Unemployment

, 13

United States (US)

, 1, 49

Data Protection Compliance and Regulations

, 87

journalism

, 91

newspaper circulation

, 7

Tampa group

, 31

US-based companies

, 88

US National Institute of Standards and Technology (NIST)

, 35

US National Security Agency (NSA)

, 84

User-Generated-Content (UGC)

, 18

Vice (Digital media companies)

, 13

Virtual Machine (VM)

, 42

Voice recognition

, 45

Washington Post, The

, 21, 27, 33, 49, 66, 80

Wearing devices

, 15

Web analytics

, 69

Web metrics

, 69

Web–scraping techniques

, 56

Website, The

, 31

Wikileaks

, 84

YouTube

, 16, 18, 37