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The Future of Journalism:
Artificial Intelligence
And Digital Identities
Noam Lemelshtrich Latar
Sammy Ofer School of CommunicationsIDC Herzliya
Israel
David Nordfors
Stanford Center for Innovation and Communication
Stanford University
Feb 2011
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Table of Contents1 INTRODUCTION
32 DEFINING JOURNALISM IN THE DIGITAL AGE 73 ESTABLISHING THE DNA OF JOURNALISTIC CONTENT 93.1 CONTENT BASED IMAGE RETRIEVAL (CBIR) .................................................... 10
3.2 VIDEO INFORMATION RETRIEVAL ...................................................................... 11
3.3 HUMAN CENTERED CONTENT ANALYSIS ......................................................... 11
3.4 THE DNA OF LITERATURE ............................................................................ 12
4 JOURNALISM CONTENT AND CONSUMER ENGAGEMENT 124.1 THE CONCEPT OF MEDIA ENGAGEMENT .......................................................... 12
4.2 BEHAVIORAL TARGETING AND JOURNALISTIC CONTENT .................................... 15
4.3 BEHAVIORAL TARGETING IN SOCIALNETWORKS ............................................... 15
4.4 PROJECT SMART PUSH ................................................................................ 16
5 AI: DIGITAL IDENTITIES AND BEHAVIORAL TARGETING ENGINE17
5.1 MANAGING DIGITAL IDENTITIES DEVELOPING A UNIVERSAL STANDARD .......... 17
5.2 DIGITAL IDENTITIES AND SOCIALNETWORKS .................................................... 18
5.3 SOCIO-GENETICS AND DIGITAL IDENTITY ....................................................... 19
5.4 BEHAVIORAL TARGETING AI ENGINE BASED ON JOURNALISTIC CONTENT AND
CONSUMER DIGITAL IDENTITY ................................................................................ 20
6 DIGITAL IDENTITIES AND WEBLINING217 DIGITAL IDENTITIES AND THE PRACTICE OF JOURNALISM228 PRINCIPLES OF JOURNALISM AND DIGITAL IDENTITIES248.1 PRINCIPLES FOR USING DIGITAL IDENTITIES FOR JOURNALISM .......................... 25
8.2 NEED FOR FURTHER DISCUSSION BETWEEN STAKEHOLDERS IN SOCIETY ......... 25
REFERENCES................... 27ABOUT THE AUTHORS 30END NOTES ........................ 30
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The Future of Journalism:
Artificial Intelligence and
Digital IdentitiesInteraction 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
describe 'Digital Identities' and their future interaction with journalism. Wesummarize 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 current
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 journalism 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 intelligenceiof
individuals using personal computers and collective intelligence of groups whennetworking. 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
intelligence, 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 Internet in the 90s, and we are now seeing the explosion
of social interaction enter journalism, ranging from reader comments to crowdsourcing.
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The interaction between journalism, the Internet and social interaction is familiar
and intensely discussed, helping 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 smart 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 peoples lives represents a complex issue. Even 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 algorithms rating journalism,
such as TechMemeii, 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 journalism.
Journalisms 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, societys collective
intelligence and machine AI? Ideally, journalism raises intelligenceempowering
the audienceas 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 penetrating journalisms 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 computings growing power are the
change agents. Interactive new media is permitting, for the first time, accurate
measurement of the attention each user gives to journalistic content. Advertiserswill demand full validation of consumer ratings. Existing measuring methods will
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vanish. Fierce competition will arise in selling consumer attention to advertisers,
whose ROI (Return On Investment) will determine 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 generation 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 latest 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 probabilities 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 reader 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 theworlds 26th largest economyv.
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Targeting content per consumer digital identity will require AI engines to analyze
multi-dimensional content vs. attributes of the engaging experience and a
consumers total beingrelate human DNA, content DNA and context DNA
(attempts to identify successful music 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 contenttext, video, audio and still imagesto
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 journalistic 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 formulations
(signatures) to understand content and context. We probe the popular concept of
media engagement and its derivativesbehavioral targeting, contextual targeting,
and how AI is used in social networks to target content and ads. An AI engine
that can filter and target journalistic content based on the consumers 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 current 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 effectsof AI algorithms on the journalism profession and journalism's role in a democratic
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hold on to dated one-to-many media technologies. Their business 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 practice of journalism.
Journalisms essence is described in principles of journalism, as suggested by the
Pew Research Centers Project for Excellence in Journalism (PEJ) and the
Committee of Concerned Journalistsviii
:
1 Journalisms 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 public 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 to 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 the 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, such as selling ads. Therefore, much journalism is attention work
performed with a mandate from the audience. When attention work is done with a mandatefrom the sources, it is public relations and publicity, not
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content automatically searches 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 the 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 structures. 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 Duygulu 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 describe 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.
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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 19th 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.
Poltis list remains popular and writers often use it in developing stories. Terry
Rusio, the Shrek scriptwriter, said he referred to Poltis list to resolve a situation
in the films 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 Poltis thirty six situations or preferably other sets of
situations, which may 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 economic 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 key measuring tool until the Internet. No pre-Internet
rating technique can measure real attention by individual consumers to specificcontent. New media platforms, with the advance of interactive new media, make
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the competition for consumer attention fierce 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 begins 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
journalistic 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 Romer 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 attention 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 involves 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 likelihood reveals that as engagement measures increased so did the mean
likelihood of products advertised in the media to be purchased. Three media platforms were
studiedtelevision, Internet and printed magazines. All three exhibited similar findings.
Internet and magazines exhibited very close response curves, while TV followed a similar
path but slightly lower mean of purchase likelihood." (ibid).
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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 automatically. 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 finders 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 contextual 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 dollarThe ROI focus will force agencies to improve effectiveness 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 perceived 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.
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4.2 Behavioral Targeting and Journalistic Content
In the late 90s a new marketing field gained academic and industry attention:
Behavioral Targeting. Recent advances 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 consumers 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, including Google,Microsoft and Yahoo. M. Kassner (2009) surveyed Googles 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 subject 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 images and videos in YouTube. According to
Kassner, Google is also trying to present relevant advertisements in the Gmail
applicationby 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).
Googles 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 video 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 behavioral-targeting technologies developed for social networks.
Stefanie Olson (2008)describes one example: 33Across.com. The New York-basedcompanys algorithms can follow consumer behavior patterns in social networks,
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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 started to use behavioraltargeting 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
targeting 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 vis-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
ethical 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
clicking 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 finding 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.
Davitzs objective was to automatically monitor peoples 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, 2008)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 Facebook." (ibid).
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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 webline
services based on digital identities.
5. AI: Digital Identities and Behavioral
Targeting Engine
5.1. Managing Digital Identities Developing aUniversal Standard
The consumers 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 used 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;ixit enables consolidation of digital identities of
surfers on various platforms and management of those identities; and it allows
mobilizing various parts of the surfers identity definition, defined on different
social networks, and merging them into one virtual profile. The standard 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 uniquelydynamic way of surfingderived from the person's ability to make decisions,
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memory and additional cognitive factorsrendered 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 Internet. 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, economical 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 users choice(Boyd andEllison,
2007). Some major networks, originally constructed as reservoirs for content to serve the
surfers, see their purpose today in providing services, information and products adapted 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, to 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
connections. 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
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The mind 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 digital identities and can be valuable for analyzing the
body, mind and ecosystem surrounding them: society. So information about
peoples genetic codes may be as rewarding for constructing digital identities as
the information from social networks.
Research and instrumentation for mapping mans 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 develop 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 Personal 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 begin to
predict social behavior, both pro-social and anti-social, according to the genetic
mapping of humanity.
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The model shows a dynamic learning model constantly updated 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 receive
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 interact 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 behavior; 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 contextsthe
Learning Process section.
6. Digital Identities and Weblining
Filtering journalistic content vs. consumer profiles could lead to serious social
inequality. Marcia Stepanek coined weblining to describe this phenomenon:
Call it weblining, an information-age version of that nasty practice of red lining,
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where lenders and other businesses mark neighborhoods off limits. Cyber space
doesnt have geography but thats 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 value, 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 commercial organizations, the described
phenomenon is also true in spreading journalistic content based on profiling. The
economic forcesadvertisers and the journalism organizationscannot be
expected to show altruism and create mechanisms to protect our right 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 ofJournalism
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 threaten 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 placed 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
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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. Lets say 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 digital 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 on 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, finally, that the journalists own digital identity will be included in the
interaction, The journalists digital identity is combined with a set of filters for
selecting themes that the journalist wishes to cover, connected to readers and
advertisers digital 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, the 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.
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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:
Journalisms first loyalty is to the citizens: Journalists can be pressured toshow loyalty to citizens digital identities rather than to the citizens themselves.
If each story is coupled directly to the business model, and if the businessmodel 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 co ver:It
may be possible 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 projecting 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 journalist to be independent but
in many other cases it will make it difficult to maintain independence.
Its practitioners must be allowed to practice their personalconscience: If
the business model and the system of digital identities and filters permits
projecting 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.
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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
Journalism
We suggest the need for principles for using digital identities in journalism. Some
such may be:
1.Peoples needs are more important than the needs of digitalidentities
Digital Identities can never be identical to a persons whole being. Some
measure 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 compromisejournalisms loyalty to the audience o r its independence fromsources;
3 Using digital identities in journalism should not compromise thejournalists freedom to practice his/her personal conscience
8.2 Need for Further Discussion Between
Stakeholders in Society
A group of computer scientists, 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 societys workloadtheir
concern is that further advances could create profound social disruptions and even
have dangerous consequencesand 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
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Advancement of Artificial 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, journalism
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 questions 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 readerships 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 will 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?
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About the authors
Noam Lemelshtrich Latar is the Founding Dean of the Sammy Ofer School ofCommunications at IDC Herzliya (the first private academic institution in Israel),and serves since 2009 as the Chairperson of the Israel Communications
Association, 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 Project 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 was 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 founding
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 andCommunication at 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 initiated and headed the first hearing about the Internet to
be held by the Swedish Parliament.
"The expression augmented intelligence is attributed to Engelbart, D.C. (Oct 1962). "AugmentingHuman Intellect: A Conceptual Framework", Summary Report AFOSR-3233, Stanford Research
Institute, Menlo Park, CA. Related concepts: 1) IA or Intelligence Amplification by Ashby, W.R.
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Computer Symbiosis",IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11.
""Techmeme, http://techmeme.com, arranges tech journalism story links into a single page.Techmeme works by scraping news websites and blogs, and then compiles a list of links to the most
popular technology-related news of the day, which is continuously updated. The stories selected are
all chosen by an automated process. http://en.wikipedia.org/wiki/Techmeme (11 Jan 2010)
"""Nordfors, D. (2008). Separating Journalism and the Media, EJC Magazine, 4 Dec 2008, EuropeanJournalism Centre http://www.ejc.net/magazine/article/separating_journalism_and_the_media/
"#Wikipedia. (Aug 29 2009). http://en.wikipedia.org/wiki/Advertising#cite_note-2 "GlobalEntertainment and Media Outlook: 20062010, a report issued by global accounting firm
PricewaterhouseCoopers". Pwc.com. Retrieved 2009-04-20.
#"The World Bank: World Development Indicators database, 1 July 2009. Gross domestic product(2008) http://siteresources.worldbank.org/DATASTATISTICS/Resources/GDP.pdf
#"Lemelshtrich Latar, N. & Nordfors, D. (2009). "Digital Identities and Journalism Content", TheInnovation Journalism Publication Series, 6(7), Nov. 11. VINNOVA-Stanford Research Center of
Innovation Journalism, Wallenberg Hall, Stanford University.
#""Compact Oxford English Dictionary, published on-line by AskOxford.com. Retrieved Sep 6 2009.#"""PEJ and CCJ Principles of Journalism, published 1997. Available athttp://www.journalism.org/resources/principles Retrieved 6 Sep 2009.
"$Madsen, Paul, SAML2: The building blocks of federated identity, Jan 2005, xml.com.$www.personalgenomes.org.
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