The Future of Journalism: Artificial Intelligence And Digital Identities

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1

 
The Future of Journalism:

Artificial Intelligence

And Digital Identities


Noam Lemelshtrich Latar

Sammy Ofer School of Communications

IDC Herzliya

Israel

David Nordfors

Stanford Center for Innovation and Communication

Stanford University


Feb. 2011


2

 
Table
of Contents

1 INTRODUCTION.......................................................................................

......... 3

2 DEFINING JOURNALISM IN THE DIGITAL AGE
....................................... 7

3 ESTABLISHING THE DNA OF JOURNALISTIC CONTEN
T
........................... 9

3.1 C
ONTENT
B
ASED
I
MAGE
R
ETRIEVAL
(CBIR)

.................................................... 10

3.2 V
IDEO
I
NFORMATION
R
ETRIEVAL

...................................................................... 11

3.3 H
UMAN
C
ENTERED
C
O
NTENT
A
NALYSIS

......................................................... 11

3.4 T
HE
DNA
OF
L
ITERATURE


............................................................................ 12

4 JOURNALISM CONTENT AND CONSUMER ENGAGEMENT
..................... 12

4.1
T
HE
C
ONCEPT OF
M
EDIA
E
NGAGEMENT

.......................................................... 12

4.2 B
EHAVIORAL
T
ARGETING AND
J
OURNALISTIC
C
ONTENT

.................................... 15

4.3 B
EHAVIORAL
T
ARGETING IN
S
OCIAL
N
ETWORKS

...........................
.................... 15

4.4 P
ROJECT
‘S
MART
P
USH


................................................................................ 16

5 AI: DIGITAL IDENTITIES AND BEHAVIORAL TARGETING ENGINE

...... 17

5.1 M
ANAGING
D
IGITAL
I
DENTITIES

D
EVELOPING A
U
NIVERSA
L
S
TANDARD

.......... 17

5.2 D
IGITAL
I
DENTITIES AND
S
OCIAL
N
ETWORKS

.................................................... 18

5.3 S
OCIO
-
G
ENETICS AND
D
IGITAL
I
DENTITY

....................................................... 19

5.4 B
EHAVIORAL
T
ARGETING
AI E
NGI
NE
B
ASED ON
J
OURNALISTIC
C
ONTENT AND

C
ONSUMER
D
IGITAL
I
DENTITY

................................................................................ 20

6 DIGITAL IDENTITIES AND WEBLINING
......................................................
21

7 DIGITAL IDENTI
TIES AND THE PRACTICE OF JOURNALISM
..................
22

8 PRINCIPLES OF JOURNALISM AND DIGITAL IDENTITIES
........................
24

8.1 P
RINCIPLES FOR
U
SING
D
IGITAL
I
DENTITIES FOR
J
OURNALISM

.......................... 25

8.2 N
EED FOR
F
URTHER
D
ISCUSSION

B
ETWEEN
S
TAKEHOLDERS IN
S
OCIETY

......... 25

REFERENCES
...................
.................................................................................. 27

ABOUT THE AUTHORS
............................................................................
...... 30

END NOTES
........................
.................................................................................. 30





3

 
The Future of Journalism:

Artificial Intelligence and

Digital Identities

Interaction between journalism, the Internet and
social communities is

familiar and intensely discussed, helping us understand how journalism can

raise our collective intelligence. We discuss how artificial intelligence (AI)

will add to that picture and thus influence the future of journalism. We

describ
e 'Digital Identities' and their future interaction with journalism. We

summarize state
-
of
-
the
-
art AI methods usable to establish the 'DNA' of

journalistic content, how matching that content with digital identities enables

behavioral targeting for consumer
engagement. We review the driving forces

such procedures may introduce to journalism and show an example of a

journalistic behavioral
-
targeting engine. We highlight some concerns and

discuss how using digital identities and AI can be complex versus curren
t

journalistic principles. We stress the need for ethical principles in using

digital identities in journalism, and suggest examples of such principles. We

issue a call for stakeholders to jointly explore the potential effects of AI

algorithms on the journ
alism profession and journalism's role in a

democratic society and suggest questions to be explored.


1.

Introduction

Computer
-
assisted intelligence is part of life: ‘augmented intelligence’
i
of

individuals using personal computers and ‘collective intelligenc
e’ of groups when

networking. Finally, there is Artificial Intelligence (AI), when computers act

intelligently without human interaction, mimicking human intelligence (
Turing, 1950)



These intelligences are blending and converging. Augmented individual

in
telligence, Collective intelligence and AI are co
-
evolving. The Internet is

becoming part of our minds and our minds are becoming part of the Internet.

Journalism is part of IT
-
assisted intelligence. Personal computing entered

journalism in the 80s, the In
ternet in the 90s, and we are now seeing the explosion

of social interaction enter journalism, ranging from reader comments to crowdsourcing.

4

 

The interaction between journalism, the Internet and social interaction is familiar

and intensely discussed, help
ing us understand how journalism can help increase

our collective intelligence. Here we study how AI may contribute through

algorithms being developed for rating news, based on mixing systems for

aggregating crowd opinions (collective intelligence) and sma
rt algorithms for

contextual analysis (AI).


Ratings help control societal systems. Any recognized rating method influences

societal development; people will try to improve their ratings. A rating that

changes people’s lives represents a complex issue. Eve
n if everyone finds a rating

annoying and counterproductive, it will still influence the system, given that people

think others recognize the rating. Indeed, journalism must scrutinize and challenge

rating systems and explore alternatives. Intelligent algo
rithms rating journalism,

such as TechMeme
ii
, strive to share in public perception of which tech journalism

matters more than others. This may incent journalism to optimize stories to rank

high. This is only one example of how AI is co
-
evolving with journal
ism.


Journalism’s role is to focus attention on stories that interest the public. For

journalism to remain meaningful, it should also empower the audience. So how

does it interact with individuals’ augmented intelligence, society’s collective

intelligence
and machine AI? Ideally, journalism raises intelligence

empowering

the audience

as it uses higher intelligence around it, i.e. the audience and the

machines.


AI algorithms are changing professional journalism and related academic research.

AI is penetrat
ing journalism’s traditional pillars: journalistic content (via automatic

content analysis in all media formats and delivery systems) and advertising (by

measuring consumer attention and targeting ads per user ‘digital identity’ or

personality, measured by
behavior). Both content and advertising are changing

dramatically.


The new media and AI technology based on computing’s growing power are the

change agents. Interactive new media is permitting, for the first time, accurate

measurement of the attention ea
ch user gives to journalistic content. Advertisers

will demand full validation of consumer ratings. Existing measuring methods will

5

 
vanish. Fierce competition will arise in selling consumer attention to advertisers,

whose ROI (Return On Investment) will de
termine the fate of channels for

advertising, including journalism paid by ads, across all media formats.


Journalistic content is undergoing major changes via interactive platforms that

make media content available continually, everywhere. Until recently,
the mass

media for distributing content were controlled by the same companies that

produced content. The traditional business model for news and entertainment

included controlling and bundling both medium and content. But with the Internet,

a new generati
on of media incumbents is arising. Companies such as Twitter,

Facebook or Google consciously avoid producing content. They do not do

journalism; they only provide access to journalism.


Journalism is separating from ‘the media’
(Nordfors, 2008)
iii
. The lates
t generation of
producers of journalism is no longer involved in the processes or infrastructures of mass
communication. They focus on producing content and publishing on
-
line,

delivering it via the infrastructures of the new content
-
neutral media entities
. The

Huffington Post and TechCrunch, started as blogs, are now large and important

publications, without controlling the infrastructure for spreading content.


Traditional media spend hugely to measure readerships, estimating their sizes and

attention pro
babilities and creating statistics and probabilities for advertisers. On

the Internet, the new media offer content producers and advertisers not

probabilities but hard data: which user looked at what, where, when and for how

long. Advertisers know if a rea
der clicked their ad. Traditional media ads are

indiscriminate, broadcast to all consumers and costing the same, regardless of how

many people pay attention or act. The Internet enables ‘contextual advertising,’

where advertisements shown to each user are
selected and served by automated

systems based on content displayed to the user.


Monitoring users and adapting content and ads to individuals is revolutionizing

content, media and marketing. In a digital
-
interactive world, marketing must

account for media
spending. The ROI in advertising and targeting content is

becoming a science, driving development of advertising, media and content. In

2007, global ad spending was estimated at $385B
iv
, equal to the 2008 GDP of the

world’s 26
th
largest economy
v
.

6

 

Targetin
g content per consumer digital identity will require AI engines to analyze

multi
-
dimensional content vs. attributes of the engaging experience and a

consumer’s ‘total being’

relate human DNA, content DNA and context DNA

(attempts to identify successful mus
ic and literature DNA already exist). Research

in biology, genetics and psychology that explore and identify links between

individuals’ genetic codes, cognitive attributes and pro
-
/anti
-
social behavior is

merging with data mining relating to Web 2.0 social
-
network activities aimed at

consumer profiling. Digital Identities will integrate a person's genetic code with

data derived from web clicks. People will pay with privacy for social networks

benefits.


New AI algorithms analyze content

text, video, audio a
nd still images

to

annotate (tag) content automatically. Global efforts are creating unified digital
-

identity standards to individuals and use AI engines to target, code and annotate

content automatically vs. digital identity. This will affect journalisti
c content

significantly and may revolutionize journalism and its academic research.

Journalism must adapt and investigate new business models (
Lemelshtrich Latar

& Nordfors,
2009
)
vi
.


In this article, we describe digital identities and new global standards
for digital

identities, the use of social networks, genetics and virtual worlds for creating

digital identities and the new AI research being used for adapting content to digital

identities. Scientists are converting journalistic content to math formulatio
ns

(‘signatures’) to understand content and context. We probe the popular concept of

media engagement and its derivatives

behavioral targeting, contextual targeting,

and how AI is used in social networks to target content and ads. An AI ‘engine’

that can f
ilter and target journalistic content based on the consumer’s digital

identity, to maximize the ROI of every dollar spent on advertising will be

described.


We highlight some concerns and discuss how using digital identities and AI can be

complex versus cu
rrent journalistic principles. We stress the need for ethical

principles in using digital identities in journalism, and suggest examples of such

principles. We issue a call for stakeholders to jointly explore the potential effects

of AI algorithms on the j
ournalism profession and journalism's role in a democratic

7

 
society and suggest questions to be explored.


2. Defining Journalism in the Digital Age

What is happening to journalism in the digital age? Until now ‘journalism’ and ‘the

media’ were synonyms. Jo
urnalism was symbolized by the infrastructure for mass

communication and vice versa. “Stop the presses” meant ‘breaking news.’

Organizations controlling the infrastructure for mass communication also

controlled the content being broadcast. This is reflecte
d in the dictionary

definitions of journalism, as in the Compact Oxford English Dictionary, published

on
-
line on the Internet through AskOxford.com
vii
:

journalist • noun, a person who writes for newspapers or magazines or

prepares news or features to be broa
dcast on radio or television
.

Ironically, the on
-
line dictionary does not include the Internet in the list of

media. But merely including the Internet would not save the definition. Now

everybody can broadcast news over the Internet, but that does not make

everybody who does it a journalist.


Until now, there have been communication infrastructures for one
-
to
-
many

communication

media, and one
-
to
-
one communication

telephone. One
-
to
-
many

communication has been seen as ‘the media,’ mainly journalism and entert
ainment,

where publishers are responsible for broadcasting and consumers have no

responsibility

they can choose to receive the broadcast or not. One
-
to
-
one

communication, mainly telephone, has not been considered ‘media’ but personal

conversations, mediate
d by an impartial infrastructure and telecom service

provider. Nobody is responsible for the entire communication, the responsibility

lies between the interacting parties.


With the Internet there is no longer a difference between infrastructure used for

o
ne
-
to
-
one or one
-
to
-
many communication. What’s more, it enables many
-
to
-
many

communication. Web 1.0 spread the one
-
to
-
many communication possibility

beyond the media. Everybody could publish. Web 2.0 introduces many
-
to
-
many

communication. Now the crowd can
publish together. The new media companies


the ones not providing their own content

have no problems with this.


In trying to preserve their practices and identities, old media companies tend to

8

 
hold on to dated one
-
to
-
many media technologies. Their busin
ess models, based on

controlling the medium and the content, have been difficult to move to the Internet.

In cases where new business models for ads have succeeded, such as Google, eBay or
Craigslist, the brokering of ads is not integrated with the practic
e of journalism.


Journalism’s essence is described in ‘principles of journalism,’ as suggested by the

Pew Research Center’s Project for Excellence in Journalism (PEJ) and the

Committee of Concerned Journalists
viii
:


1.
Journalism’s first obligation is to the
truth;

2.
Its first loyalty is to the citizens;

3.
Its essence is a discipline of verification;

4.
Its practitioners must maintain independence from those they cover;

5.
It must serve as an independent monitor of power;

6.
It must provide a forum for publ
ic criticism and compromise;

7.
It must strive to make the significant interesting and relevant;

8.
It must keep the news comprehensive and proportional;

9.
Its practitioners must be allowed to practice their personal conscience.


These principles remain,
even when we no longer know what ‘the media’ are.


Consider a new, short definition of ‘journalism,’ separating it from ‘the media,’

connecting journalistic principles based on the relation between journalism and its

audience, rather than on its relation t
o the communications medium it uses (which

is what is causing the confusion today). Take, for example, the following

suggestion (
Nordfors, 2009
)



Journalism is the production of news and feature stories, bringing public

attention to issues that interest t
he public. Journalism gets its mandate from

the audience.


Journalism must act on behalf of its audience, not its sources or advertisers.

Journalism often has business models based on ‘attention work’

(
Nordfors, 2006).
generating
and brokering attention, s
uch as selling ads. Therefore, much journalism is attention work
performed with a mandate from the audience. When attention work is done with a mandate
from the sources, it is public relations and publicity, not

9

 
journalism.


Journalism’s role as agenda
-
set
ter of public debate (as described by McCombs and

Shaw in their agenda
-
setting theory [1972])

depends on journalism’s ability to focus

public attention on issues that interest the public. This is for many a
raison
-
d’etre

for journalism; it requires a busi
ness model that incentivizes focusing public

attention. Business models for journalism based on brokering knowledge, not

attention
-
such as newsletters
-
do not necessarily incentivize broad public

attention and may dis
-
incentivize it. Who will pay a high
price for a newsletter

containing information already known to the public, and probably available on the

Internet?


The central question for journalism’s survival as an independent business is not

how to adapt to the Internet but what new business models
will satisfy journalistic

principles in the innovation economy.


3. Establishing the DNA of Journalistic

Content

From the early nineties major interdisciplinary research efforts have been invested

in developing efficient ways to automatically retrieve info
rmation and knowledge

from multi
-
medial journalistic content. The main objective is to let consumers find

information they seek quickly and effectively. Today, major search engines such as

Google, Yahoo and others yield millions of links to any request and
cannot answer

consumer requests expressed by simple keywords. The community of researchers

involved in this multimedia information retrieval research (MIR) covers Human

Computer Interaction (HCI), Information Theory (IT), Statistics, Pattern

Recognition,
Psychology and recently, the Social Sciences. Recent papers in these

separate fields offer citations and borrow research methods and tools from the

other fields.


Substantial multi
-
disciplinary research into information retrieval from journalistic

content
is done from the perspective of consumer
-
initiated search for information

and knowledge. Our objective is to study the implications of the research

prospective, analyzing the significance of a new journalistic phenomenon where

10

 
content automatically searche
s for consumers based on their digital identities. But

first we describe current research on automatic knowledge retrieval from

journalism multimedia content. Most research tools used by the different

communities aim at dividing content into small content
digital units, analyzing

them, tagging these sub
-
content units and then carrying an integrative analysis to

conceptualize the entire content meaningfully for the consumers. Some researchers

convert the visual content into mathematical formulations that can
then be

subjected to analysis employing AI algorithms
 
(
Jeon, Laverenko, and Mammatha, 2007).


3.1 Content Based Image Retrieval (CBIR)

The primary method used in the search for image retrieval or automatically

conceptualizing visual content is dividing th
e visual frames into smaller

sections/regions termed ‘blobs.’ This is achieved by using statistical tools such as

clustering. Each blob is annotated with text. The visual image is described by

employing categories such as color, texture, shapes, and struct
ures. Statistical

theories are used to associate words with image regions that are then compared

with human manual annotations of similar images (
Smeulders
et al., 2000). Attempts are
made to describe images using “vocabulary of blobs” as proposed by Duygu
lu
et al
. (2002).
Jeon
et al.
(2007)
proposed a method for using “training set of annotated images for … cross
media relevance model for images.”

CBIR researchers are developing mathematical descriptions of images defined as ‘signatures.’
The signatures de
scribe an image in mathematical formulations to let researchers measure
content similarities between image frames. Statistical methods such as clustering and
classification form image signatures that will allow automatic similarity measurements by
machines
. Images are segmented by features such as color, texture differences, shapes and
other salient points.


11

 
3.2 Video Information Retrieval


In video retrieval, researchers have attempted to develop automatic retrieval

methods that do not rely on subjective
human analysis. This required developing

techniques that identify thresholds between color histograms corresponding to

consecutive video frames (
Flickner
et al. 2003). A search engine, ‘ImageSpace,’ was
developed where users could direct queries for multi
ple visual objects, such as sky, trees,
water etc. These tools were used for several video searches including automatic detection of
pornographic content (ibid).


3.3 Human Centered Content Analysis


It has been long recognized that human satisfaction wit
h search for knowledge and

information in multimedia content involves several dimensions: a mixture of

rational as well as emotional dimensions. The consumer search takes place in a

certain context and emotional state and identical search results may be vi
ewed

differently by the same person based on his or her emotional state at the time of the

search. A person’s background, education and values affect his or her satisfaction

with the search results.


An important dimension is to study the emotions that a c
ertain piece of content

evokes in people. Datta et al refer to this dimension as aesthetics; “Aesthetics is the

kind of emotion a picture arouses in people. Emotions are subjective reactions and

should be measured as such. This is referred to at times as a
ffective computing…

which focuses on understanding the user’s emotional state that affects his

satisfaction with the information retrieval.”
 
(
Barnard

et al. 2003).


CBIR researchers realize that integrating human feedback and involvement in the

automatic
multi
-
model content analysis is crucial in reducing errors and increasing

user satisfaction. This new direction in research is called ‘human
-
centered

computing.’ Some researchers attempt to define images according to emotional

categories. Salway et al deve
loped a way to extract character emotions from films

based on a model that links character emotions to events in their environment
 
(
Salway, and
Graham, 2003
)


12

 
3.4 The DNA of Literature


Before the invention of computers (but after Boolian Logic and Bayes’
Theorems

laid the mathematical foundations of modern computers and algorithms), in the

mid 19
th
century, the French writer George Polti, (b.1868) analyzed the elements of

successful literature, its ‘DNA.’ Polti listed thirty
-
six dramatic situations in good

drama, including prayer to the supernatural, crime pursued by vengeance, loss or

recovery of a lost one, disaster, remorse, revolt against a tyrant, enigma and others.

Polti’s list remains popular and writers often use it in developing stories. Terry

Rusi
o, the ‘Shrek’ scriptwriter, said he referred to Polti’s list to resolve a situation

in the film’s plot. To create his list, Polti analyzed classical Greek texts and French

literature. His analysis of the DNA of drama was followed by other writers.


These
attempts to discover good story elements and persuasive drama were not

written from an information
-
retrieval perspective but provide the literary ‘blobs’

that will let researchers dissect, in our case, a journalism story along its content

elements.


These
content elements, found in texts, should receive mathematical formulations

that will allow computer
-
based analysis. One should be able to retrieve content

based, for example, on Polti’s thirty six situations or preferably other sets of

situations, which ma
y be determined by modern data analysis of journalism stories,

for comparative analysis or for marketing news stories to consumers based on their

digital identities.


4. Journalism Content and Consumer

Engagement

4.1. The Concept of Media Engagement

The ec
onomic engine driving journalism in the non
-
public service media has until

now been advertising
-
based. Journalism companies, regardless of media platform

(paper, video, audio) have sold consumer attention to advertisers. Though

inaccurate, rating was the k
ey measuring tool until the Internet. No pre
-
Internet

rating technique can measure real attention by individual consumers to specific

content. New media platforms, with the advance of interactive new media, make

13

 
the competition for consumer attention fierc
e and complex. The journalism

industry now needs to develop new ways to measure consumer attention in

multiple parameters, including the consumer cognitive and behavioral profiles and

context parameters. The interactive nature of the new media platforms be
gins to

allow for scientific measurement of consumer attention along personal dimensions.


In this new battle for consumer attention the concept of ‘engagement,’ a relatively

new term, is being used to describe the new relations between consumers and

journ
alistic content. The Advertising Research Council (ARC) has devised the

following definition of media engagement: “Engagement is turning on a prospect

to a brand idea enhanced by the surrounding context… the working definition

proposed by ARC encapsulates
the ultimate objective of linking positive effects

towards a brand with brand advertising within the environment of the program

content" (
Kilger, and Romer, 2007)
.


Context within which content is delivered is becoming of prime importance. Kilger

and Rome
r
identified three mechanisms that enhance consumer engagement in a

journalistic content:


“Cognitive (relevance of the program and advertisement to the consumer)

Emotional (the extent to which one likes the content and advertising)

Behavioral (paying atte
ntion to the program and advertising content)”

(ibid)
.


The main hypothesis is that the more engaged consumers are the more they will

spend on the advertised product. This recognition by the advertising world, that

engagement in journalistic content involv
es consumer cognition, emotional profile

and behavior, provides relevance to computer
-
based information retrieval as

applied to content analysis.


Research by Kilger
and Romer
(ibid) about the relationship between media engagement and
product
-
purchase lik
elihood reveals that as engagement measures increased so did the mean
likelihood of products advertised in the media to be purchased. Three media platforms were
studied

television, Internet and printed magazines. All three exhibited similar findings.
“Inte
rnet and magazines exhibited very close response curves, while TV followed a similar
path but slightly lower mean of purchase likelihood." (ibid).


14

 
The personal parameters Kilger
and Romer
examined were those traditionally used in social
-
science research:
gender, age, education, income, race and marital status.

Age, income and race mattered. In the TV and Internet, people with lower

education expressed higher levels of trust in the media and older people reported

lower engagement. The finding that personal
attributes affect media engagement is,

as can be expected, of great relevance regarding digital identities. Digital identities

are valuable to advertisers who will not hesitate to take advantage of them once

available on a large scale and accessible automa
tically. The road to influence

journalistic content in the direction of higher consumer engagement is short. Kilger

and Romer
considered a limited number of personal parameters, as a larger number was “too
many to fill within the space constraints of this
article." (ibid).


The Internet offers ways to measure and broker not only consumer attention and

engagement but also consumer interaction. A pay
-
per
-
click model does this, as

advertisers will pay not for being visible but for consumer clicks, an action.
This

can be taken further. For example, a click on an ad will usually lead to a sales site

and may result in further interaction between the consumer and the vendor,

including a purchase. So ads, thus journalistic content, could in principle be paid

by fin
ders’ fees. This could, however, introduce business incentives for journalists

that might jeopardize journalistic principles.


To convert the content
-
engagement/product
-
purchase relations into a science

requires the analysis of many variables including con
textual ones, and requires

automation and the introduction of artificial intelligence: “Excelling during an era

of frugality in high expectations requires digital marketers to be accountable for

every dollar…The ROI focus will force agencies… to improve ef
fectiveness and

we see increased dependence on automation… recent shifts [in the liberal

direction] in user privacy perceptions have created a window for marketers to use

AI to run efficient campaigns."
 
(ibid).



The ultimate goal of engagement as perceiv
ed by the advertising industry is to

target advertisements to consumers based on contextual and personal parameters as listed by
the Kilger group: cognition, emotions and behavior. This today is being

done and researched in the new media channels, termed ‘
Behavioral Targeting' by

academic researchers, journalists and the advertising industry.


15

 
4.2 Behavioral Targeting and Journalistic Content


In the late 90s a new marketing field gained academic and industry attention:

Behavioral
Targeting. Recent advance
s in Internet and Web 2.0 interactivity,

characterized by consumers becoming content creators and providers, have opened

new frontiers for targeting ads to consumers based on interactive behavior.

Behavioral Targeting is “the ability to deliver ads to cons
umers based on their

behavior while viewing web pages, shopping on
-
line for products and services,

typing keywords into search engines or combinations of all three…”
 
(
Aho Williamson, 2005)
.


Many Internet companies are involved in behavioral targeting, inc
luding Google,

Microsoft and Yahoo. M. Kassner (2009)

surveyed Google’s extensive use of behavioral
targeting. Google confirms this in its official website. Google uses two separate systems,
Adwords and AdSense. Adwords targets ads based on the search sub
ject matter by identifying
search keywords. AdSense targets ads based on website content the consumer views “for
example if you visit a gardening site, ads on that site may be related to gardening."
 
(ibid).

AdSense was extended to searching annotated imag
es and videos in YouTube. According to
Kassner, “Google is also trying to present relevant advertisements in the Gmail
application…by scanning every Gmail message for spam… and sending ads based on the
keywords… the whole process is automated and involves
no human matching ads to the
Gmail content." (ibid).


Google’s rationale is that by making ads more relevant to customers it brings them

more value. So far, AdSense and Adwords, in all their applications, are still based

on text analysis. Once image and v
ideo content are analyzed and annotated

automatically, behavioral targeting will likely be applied to all journalistic content.


4.3 Behavioral Targeting in Social Networks

Social networks characterized by voluntary profiling by members uploading

personal
data in texts, pictures and videos are ripe for behavioral targeting. Social

network members’ profiles include lists of friends, hobbies, demographics and

other interests. Behavioral targeting is growing rapidly in social networks. Startups

are devising be
havioral
-
targeting technologies developed for social networks.

Stefanie Olson (2008)

describes one example: 33Across.com. The New York
-
based

company’s algorithms can follow consumer behavior patterns in social networks,

16

 
identify ‘sociograms’ among members
and identify for advertisers the more

influential members and the ‘viral propagators’ by studying message dynamics.

Universal Pictures is using 33Across to study how people share studio trailers or

content with their friends (ibid).

Other companies that st
arted to use behavioral

targeting on social networks for marketing advertisements include Reverence

Science and Tacoda Systems (bought by AOL, now a full subsidiary). Yahoo

launched SmartAds, to combine behavioral information with demographic data for

targ
eting ads. Behavioral targeting ad spending is projected at $1B in 2010,

growing to $3.8B by 2011 (
Mills
, 2007).


Behavioral targeting raises serious privacy issues discussed extensively in

academic literature and political circles. The issue of privacy vi
s
-
a
-
vis consumer

profiling is beyond the scope of this paper. Tim Berners
-
Lee, credited with

inventing the World Wide Web, spoke before the U.K. parliament on privacy and

the Internet. He said that he came to “raise awareness to the technical, legal and

et
hical implications of the interception and profiling by ISPs in collaboration with

behavioral targeting companies.”
 
(
Watson, 2009)
.
He continued: “It is very important that
when you click, you click without a thought that a third party knows what we are

cl
icking on… I have come here to defend the Internet as a medium.” (ibid).



But surveys by TRUSTe (a ‘privacy’ company) shows that the public “show a

willingness… to submit to monitoring and enhanced content delivery.”
 
(
Olsen, 2008)
.

This is
a remarkable fi
nding that should be followed.


4.4 Project ‘Smart Push’

Davitz of SRI applies machine
-
learning techniques to study communications in

social networks as part of a multimillion dollar project funded by the Defense

Advance Research Project Agency (DARPA) of
the U.S. Department of Defense.

Davitz’s objective was to “automatically monitor people’s interest and influence in

military communities… to identify the influencers… then to ensure that they see

relevant information in news feed to that topic." (Oslen, 20
08)

Davitz calls this targeting of
news according to members’ interest profiles ‘Smart Push.’ According to Olsen, “SRI is
looking at commercial applications for it not related to advertising… you can already learn
more about people from MySpace and Faceboo
k." (ibid).


17

 
When a powerful research institute like SRI promotes concepts like ‘Smart Push,’

news media, when ‘rating is king,’ will adjust journalistic content to fit consumers’

digital profiles. This may be done by using an AI engine to filter or ‘webli
ne’

services based on digital identities.


5. AI: Digital Identities and Behavioral

Targeting Engine


5.1. Managing Digital Identities

Developing a

Universal Standard

The consumer’s digital identity is a vital component in this process and will

directly
affect the type of services and information he or she will receive.


Today, the global knowledge industry invests great resources in developing and

improving management techniques of digital identities. Digital
-
identity

management is developing rapidly and
is called ‘federated identity management.’

The term ‘federated identity’ refers to various components of users’ profiles

gathered while they surf on different sites and consolidated into uniform profiles

according to a global standard. The term is also us
ed for adoption of standards for

the consumer
-
identification process on the various platforms. Currently, the most

acclaimed standard for constructing digital identity is called SAML2, ‘Security

Assertions Markup Language 2.0;’
ix

it enables consolidation of
digital identities of

surfers on various platforms and management of those identities; and it allows

mobilizing various parts of the surfer’s identity definition, defined on different

social networks, and merging them into one virtual profile. The standar
d was

successfully assimilated in financial organizations, academic institutions, the

American electronic government and more.


Adoption of international standards for defining digital identities is significant. It

will enable researchers to follow surfers
in any site in cyberspace and carry out

widespread studies on the connection between the users’ digital identities and their

personalities, fields of interest and cognitive abilities. Every surfer has a uniquely

dynamic way of surfing

derived from the per
son's ability to make decisions,

18

 
memory and additional cognitive factors

rendered to automatic cognitive

diagnosis through AI algorithms.


Soon AI algorithms will be able to construct a personal digital identity for every

person performing actions on the I
nternet. Data
-
mining ‘robots’ will be able to

analyze texts, video and audio contents and transform them into sociological DNA

(SDNA) that will describe the individual personality (
Lemelshtrich Latar, 2004).

Constructing the digital identity is a dynamic
process updated as long as the person is active
on the Web.


5.2 Digital Identities and Social Networks


One of the main Internet uses is activity in social networks. Today, millions of

people belong to social networks that answer many needs, social, econo
mical and

political. A social network is a group that maintains connection to exchange

information in text, video, photos or voice or for social purposes. Every network

member must give personal details about themselves, and these are exposed to the

other
network members or part of them, according to the user’s choice(
Boyd
and
 
Ellison
,
2007).

Some major networks, originally constructed as reservoirs for content to serve the
surfers, see their purpose today in providing services, information and products ad
apted to
members’ digital identities. In September 2007 the network Myspace informed its
shareholders that it intended to undertake data mining, using the profiles and blogs of
approximately one hundred million of its members, to direct advertisements and
services to
them. Thus, this is the start of a screening system that will provide services and information to
members according to their digital identity
 
(
Abramovitch, 2007).
The declared objective is to
improve the membership experience on the network, “t
o add value to the user experience”
(almost a paraphrase of Aldous Huxley in ‘Brave New World’).


Social networks create a substantial and dangerous expansion of the digital
-
identity

notion to include complete mapping of surfers’ social and professional

co
nnections. This mapping will accompany the surfers in all human activities and

may become a powerful filter that will limit the information and possibilities

presented to them, without them being aware of it.



5.3 Socio
-
Genetics and Digital Identity

19

 
The m
ind and the body hang together, and science is constantly improving the

knowledge about it. We know today that social behavior is linked to genetics.

Understanding these connections, and how they work in a social context, is

powerful for constructing digit
al identities and can be valuable for analyzing the

body, mind and ecosystem surrounding them: society. So information about

people’s genetic codes may be as rewarding for constructing digital identities as

the information from social networks.

Research an
d instrumentation for mapping man’s genetic code, ‘gene sequencing,’

are developing rapidly at leading research institutes and large commercial

companies worldwide. Their main objective is to identify genes associated with

hereditary diseases and to develo
p medication based on genetic treatment. Since

the completion of the Human Genome Project in 2001, commercial competition

has arisen between companies for producing machines that map the genetic code of

man. The main research project in this field is the P
ersonal Genome Project.
x


The connection between genes and human traits, and the entry of information
-
age

giants such as Google and leading research centers such as Harvard and Cornell

into the field of genetic research, should close the knowledge research
gaps much

faster. The large volume of participants in these studies, the vast databases holding

participants’ digital identities and data mining peoples’ social behavior on the

Internet, together with the use of smart algorithms is helping science to begi
n to

predict social behavior, both pro
-
social and anti
-
social, according to the genetic

mapping of humanity.


20

 
5.4 Behavioral Targeting AI Engine Based on

Journalistic Content and Consumer Digital Identity



The behavioral
-
targeting AI engine above outlin
es the basic information
-
flow

elements that will automatically analyze journalistic content in all platforms and

transmit relevant content and advertisements to consumers per their digital Identities.

21

 

The model shows a dynamic learning model constantly up
dated as it ‘learns’ the

consumer profile and content preferences. Unknown factors are expressed by

probabilities constantly updated in the ‘learning’ process. Journalistic content will

be monitored constantly as consumers interact and make choices. The AI
engine

will also monitor context parameters and consumers’ emotional state during

interaction by analyzing verbal or other reactions. A brief description of the

information flow:


Step One: All journalistic content is analyzed by AI smart algorithms and r
eceive

automatic annotations (tags);


Step Two: Consumers’ digital identities and annotated content are fed to the

Assessment Rule Engine for initial content determination; proper ads are sent to

consumers based on their profiles;


Step Three: Consumers in
teract with the content and advertisements; this

interactivity is monitored constantly and consumer attention measured;


Step Four: The Learning Engine analyzes consumer feedback and automatically

adjusts the probabilities to better describe consumer behav
ior; new content is sent

to consumers;


Step Five: The Learning Engine transmits updated information to a Personal

Memory database where a consumer media profile is created and constantly

updated;


Steps four and five continue indefinitely to allow the AI
engine to accurately

predict consumer content and product interests/choices in varying contexts

the

‘Learning Process’ section.


6. Digital Identities and Weblining

Filtering journalistic content vs. consumer profiles could lead to serious social

inequalit
y. Marcia Stepanek coined ‘weblining’ to describe this phenomenon:

“Call it weblining, an information
-
age version of that nasty practice of red lining,

22

 
where lenders and other businesses mark neighborhoods off limits. Cyber space

doesn’t have geography but
that’s no impediment to weblining […] weblining may

permanently close doors to you or your business."
 
(Sterpanek,
2000).


New York University sociologist Marshall Blonsky adds to the meaning of

‘weblining:’ “If I am weblined and judged to be of minimum va
lue, I will never

have the product and services channeled to me or the economic opportunities that

flow to others over the net." (ibid).



Digital identity is at the core of weblining. Though the emphasis of Stepanek and

Blonsky is on economic aspects of c
ommercial organizations, the described

phenomenon is also true in spreading journalistic content based on profiling. The

economic forces

advertisers and the journalism organizations

cannot be

expected to show altruism and create mechanisms to protect our r
ight to equal

accessibility to content. More seriously, no one can protect us from the effects of

the need to target content per consumer profiles on the quality of journalistic

content.


7. Digital Identities and the Practice of

Journalism

From the point
of view of journalism practice, the emergence of digital identities

suggests that publishers and journalists will be able to simulate and measure what

their news stories will do for audiences and the other stakeholders in their

storytelling, while they are
developing the story. They would be able to ‘test run’

stories before publication, much as advertisers now do with new product tests. This

will introduce interesting opportunities and challenges for journalism.


Simple on
-
textual advertising need not thre
aten journalistic principles of separation

between content production and selling audience attention to advertisers (who may

have stakes in the stories). But as contextual advertising starts to understand

content, context and audience better, ads will be p
laced precisely. Present pay
-
perclick
business models will create an incentive for publishers to focus on stories that match ads. If
so, it will threaten journalistic freedom. The classic ‘separation of

Church and State,’ the metaphor used by publishers to
distinguish between news

23

 
stories and paid advertising, will blur.


For example, consider a situation where readers use their digital identities,

combined with a series of filters, to select news stories they want to be brought to

their attention. Let’s sa
y the quality of filters and digital identities is good enough

to estimate both the chance that a story will catch the reader attention and the

chance it will lead to action by the reader. Now consider a set of contextual

advertisers (these can also be dig
ital identities) that will pay for attention and

interaction with readers. Consider a journalist with access to these digital identities

and filters, as well as access to the contextual advertisers, when writing a story.

The journalist can test the story o
n digital identities representing both audience and

advertisers as the story is written. The journalist can adjust the writing to receive

the ‘best’ results, a combination of what the journalist, the audience wants and the

advertisers want.


Consider, fina
lly, that the journalist’s own digital identity will be included in the

interaction, The journalist’s digital identity is combined with a set of filters for

selecting themes that the journalist wishes to cover, connected to readers’ and

advertisers’ digita
l identities, and exposed to a ‘news ticker’ type flow of events,

e.g. all the twitter feeds, the blogosphere and all the other news feeds on the

Internet. It can be data flow from stock markets, sensors measuring weather or

earthquakes etc. The journalist
can then be tipped off about events that will

produce suitable matching between his/her own interests, and the interests of the

audience and advertisers.


Thus producing a successful story is equal to solving a dynamic equation involving

the journalist, t
he audience and the business model, e.g. the advertiser. Producing a

journalistic story while guided by the interaction between the digital identities and

the filters can be seen as an iterative, heuristic solution of the equation, identifying

overlapping
interests and optimizing the combined actions into a result maximizing

value for each party. In each interaction, real
-
life users behind the digital identities

give feedback, reinforcing or modifying digital identities’ and filters’ actions, to

improve the
outcome in the next round.


24

 
8. Principles of Journalism and Digital
Identities

The interaction between digital identities, as discussed above, may improve the

outcome for all parties involved. But it is a hazardous scenario. It needs to be

discussed among
the actors who care about journalism and its role in society.

Looking at existing journalistic principles, at least the following can be strongly

affected by the above scenario:

Journalism’s first loyalty is to the citizens:
Journalists can be pressured t
o

show loyalty to citizens’ digital identities rather than to the citizens themselves.

If each story is coupled directly to the business model, and if the business

model builds on selling audience attention/interaction to advertisers, this can

be a problem
. It will be difficult to maintain a loyalty to the audience of

citizens if the journalist will earn more money by adapting to the [digital

identities of the] advertisers.

Its practitioners must maintain independence from those they cover:
It

may be possib
le to involve behavioral models of those covered in the stories in

the ‘equation.’ This will improve the journalists’ chances to plan a series of

stories, knowing how the outcome of one story opens for the next. It will give

journalists a tool for projecti
ng the effects the story will have on stakeholders.

Those covered in the story may also be advertisers or have strong, shared

interests with advertisers. This makes the web of co
-
dependencies more visible

to the journalist. In some cases this can help a jo
urnalist to be independent but

in many other cases it will make it difficult to maintain independence.

Its practitioners must be allowed to practice their personal
conscience:
If

the business model and the system of digital identities and filters permits

p
rojecting how much profit a story can produce as it is written, or if it will

offer predictions of how the story will influence stakeholders in the journalism

organization, probability increases that the journalists’ personal consciences

may conflict with
businesses’ or other stakeholders’ interests. In short: ‘if I

write the story the way I want, my publisher will know that I chose to earn less

money’. Or: ‘If I write the story the way I want, my publisher will know that I

chose to increase the risk of us
getting in conflict with the advertisers.

25

 

These are only quick, simple examples of types of issues that need to be considered

while developing systems of digital identities and filters for journalism.


8.1 Principles for Using Digital Identities for

Journ
alism


We suggest the need for principles for using digital identities in journalism. Some

such may be:

1.

People’s needs are more important than the needs of digital
identities.

Digital Identities can never be identical to a person’s whole being. Some

mea
sure of error should always be considered. People are more important

than digital identities. Digital identities should adapt to people, not viceversa;

2. Using digital identities in journalism should not compromise

journalism’s loyalty to the audience or
its independence from
sources;

3. Using digital identities in journalism should not compromise the

journalists’ freedom to practice his/her personal conscience.


8.2 Need for Further Discussion Between

Stakeholders in Society


A group of computer scientist
s, AI researchers and roboticists met in Asilomar

Conference Grounds on Monterey Bay in California to debate “whether there

should be limits on research that might lead to the loss of human control over

computer
-
based systems that carry a growing share of
society’s workload…their

concern is that further advances could create profound social disruptions and even

have dangerous consequences…and force humans to learn to live with machines

that increasingly copy human behaviors"
 
(
Markoff, 2009).


The scientists
were concerned about job loss or criminals accessing these tools. No

reference was made to the possible devastating effects that using AI tools may have

on journalistic content. The conference was organized by the Association for the

26

 
Advancement of Artifi
cial Intelligence (AAAI). Dr Horvitz of Microsoft, who

organized the meeting, said “he believed computer scientists must respond to the

notions of superintelligent machines and artificial intelligence run amok …the

panel was seeking ways to guide research
so that technology improved society

rather than move it toward technological catastrophe" (ibid).


It is time to organize a similar conference with computer scientists, AI experts,

academic researchers in the area of multimedia information retrieval, jour
nalism

professionals and experts, social communication experts and economists who

specialize in media business models, to explore the potential effects of AI

algorithms on the journalism profession and its role in a democratic society. Some

of the question
s to be explored:

1. Will people control or be controlled by their digital identities?

2. How will the definition of journalism be influenced by digital identities?

3. With the Internet, journalism is no longer only broadcasting but also

interacting with r
eaderships and facilitating public discussions. What is the

role of journalism in society?

4. How will journalistic principles be affected by interaction between digital

identities?

5. Which business models are enabled by digital identities? To what extent

will journalists be attention workers, paid by brokering the readership

attention to advertisers; to what extent will they be knowledge workers,

paid by brokering knowledge?

6. What are suitable principles for journalism, in a situation where interaction

with and between digital identities guides the production of journalism, the

ways it generates value for people, and the ways it creates profits for the

journalism industry?

7. What is the match between journalism and journalistic business models?

8. How w
ill journalistic principles and matching business models be updated?

9. How are journalistic principles, and the process for updating them, be

implemented in an environment of digital identities?

27

 
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30

 
About the authors

Noam Lemelshtrich Latar
is the Founding Dean of the Sammy Ofer School of

Communications at IDC Herzliya (the first private academic institution in Israel),

and serves since 2009 as the Chairperson of the Israel Communications

Associati
on, which groups all media researchers in the Israeli Universities and

Colleges. Lemelshtrich Latar received a Ph.D. in communications from MIT in

1974 and MSc. in engineering systems at Stanford in 1971. He was among the

founders of the Community Dialog P
roject at MIT, experimenting with interactive

TV programs involving communities through electronic means. From 1975 to 2005

Lemelshtrich Latar pioneered the teaching and research of new media at the

Hebrew and Tel Aviv Universities. From 1999 to 2005 he wa
s involved in the

Israeli high
-
tech industry as a venture
-
capital chairman, helping to establish several

communications start ups in cognitive enhancement, data mining of consumer

choices and home networking. In 2005 he joined IDC Herzliya Israel as foundi
ng

Dean of a new school of communications, emphasizing new media. His current

research interest is in digital identities and the effect of AI on journalism.


David Nordfors
is co
-
founding Executive Director of the Center for Innovation and
Communication a
t Stanford University. He coined

‘Innovation Journalism’ and ‘Attention Work’ and started the first innovation

journalism initiatives, in Sweden and at Stanford. He is a member of the World

Economic Forum Global Agenda Council on the Future of Journalism.
Nordfors is

adjunct professor at IDC Herzliya and visiting professor at the Monterrey Institute

of Technology and Higher Education (Tech Monterrey). Dr. Nordfors has a Ph.D.

in molecular quantum physics from the Uppsala University, and did his postdoctoral

research in theoretical chemistry at the University of Heidelberg. He was

the initial Director of Research Funding of the Knowledge Foundation in Sweden

(KK
-
stiftelsen). He was the first Science Editor of Datateknik, a Swedish IT

magazine, from where he i
nitiated and headed the first hearing about the Internet to

be held by the Swedish Parliament.
 
                                                           
                                                           
 
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The expression ‘augmented intelligence’ is attributed to Engelbart, D.C. (Oct 1962). "
Augmenting

Human Intellect: A C
onceptual Framework
", Summary Report AFOSR
-
3233, Stanford Research

Institute, Menlo Park, CA. Related concepts: 1) IA or ‘Intelligence Amplification‘ by Ashby, W.R.

(1956),
An Introduction to Cybernetics
, Chapman and Hall, London, UK. Reprinted, Methuen an
d

Company, London, UK, 1964. 2) ‘Man
-
Computer
-
Symbiosis’ Licklider, J.C.R. (1960). "Man
-

31

 
                                                           
                                                           
                                                           
                                                           
                                                           
                                             
 
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ii

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single page.

Techmeme works by scraping news websites and blogs, and then compiles a list of links to the most

popular technology
-
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all chosen by an automated process. http://en
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iii

Nordfors, D. (2008). “Separating Journalism and the Media”, EJC Magazine, 4 Dec 2008, European

Journalism Centre http://www.ejc.net/magazine/article/separating_journalism_and_the_media/


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(2008) http://siteresources.worldbank.org/DATASTATISTICS/Resources/GDP.pdf


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Lemelshtrich Latar, N. & Nordfors, D. (2009). "Digital Identities and Journalism Content",
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Innovation Journalism Publication Se
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(7), Nov. 11. VINNOVA
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Innovation Journalism, Wallenberg Hall, Stanford University.


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ix

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