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increase the number of participating
individuals is tapping into the Internet.
However, saddled with a freewheeling
Wild West style, the Internet cannot
easily provide pure well-controlled
study populations. But the vast potential
for touching enormous numbers of
people could negate the noise of the
Web. Experts use “meta-analysis” to
increase the size of their experimental
sample. Wiki knowledge derived from
a social network offers a fluid, open
source, ongoing meta-analysis—a virtual
collection of experiences that can
be constantly updated as users enter
more individual data.
Benefits and Challenges of
Collective Information
Social networks empower the “expert,”
be it a doctor or a journalist, because
access to this community-generated
knowledge is shared by all. For example,
illness and a significant story intersected
at Love Canal, where 21,000
tons of chemical waste lay buried
beneath the community unbeknownst
to the residents. Back in 1978, a time
long before social media existed, Lois
Gibbs, a local mother and president
of the Love Canal Homeowners’ Association,
first associated exposure to
the leaking chemical waste with the
epilepsy, asthma and urinary tract
infections that were recurring in her
children. Although flagrant and clear
cut, Love Canal is not unique. Now, the
ability of Web-based medical networks
to cluster data geographically has the
potential to reveal other dangerous living
conditions. Similarly, occupational
risks for disease are well recognized,
and organizing medical data in this way
will likely serve as an early warning
system for on-the-job risks—and for
investigative stories that can be done
about them.
Figuring out what constitutes a
healthy lifestyle is something that
consumes the time of both doctors
and journalists, whose job it is to
report reliably on the barrage of evidence
emerging from many different
studies—much of it contradictory or,
at least, confusing. New information
surfaces almost daily about dietary
measures or fitness programs that
will increase or decrease our risk
for cancer, heart attacks, Alzheimer’s
disease, and more, and some of it is
potentially contaminated by the bias
of financial involvement.
How can one possibly capture all
these simultaneous variables when
computing risk? Did a study that found
something new about coffee drinkers
control for the number of hours those
people spent in the gym? When a new
drug is tested, the control group may
not be identical to the experimental
group in caloric intake, number of
portions of vegetables eaten, or their
amount of daily exercise. At best,
the study is controlled for age and
gender. But if those taking the drug
are eating poorly and under stress
and those on placebo are dining on
salads and jogging on the beach, the
wrong conclusion could be reached.
And if one adds in genetic variation
found in human populations—certain
types of genes can increase or diminish
the risk for disease—the variables
mount further. Controlled studies are
just not powered to capture multiple
variables, and medical conditions are
brimming with variables. The only way
to increase the statistical power of a
conclusion is to increase the sample
size, exactly what social networks are
designed to do.
Because wiki knowledge in the social
network arena is obtained in an
unconventional manner, it might not
provide conclusive evidence. Therefore,
a preferable way of thinking about
wiki knowledge is as a guidepost for
the design of hypotheses (for scientists
to test) or generating story ideas (for
journalists to report). For each of us,
the pitfalls are evident, and a few of
them are highlighted below:
• Selection bias is a problem. Those on
a social network tend to be younger
and not economically disadvantaged.
When groups of people are excluded
due to entry barriers, the information
generated from the community
will be biased, and other knowledge
will be lost or skewed. In time, the
increasing penetration of the Internet
to all segments of the society will
New Venues
resolve this issue as has happened
for telephones and TV.
• The privacy question. No network
is totally secure—and medical
information is not immune to the
problem. This summer, staff at a
hospital near Los Angeles was discovered
snooping through records of
Hollywood celebrities. And this case
is not unique. Beyond the security
of servers, networks allow levels of
access; therefore, on a site where
people share medical information,
they can limit the information that
others can see. Some individuals
may want to remain completely
anonymous. Others may be willing
to share all their information within
a small subnetwork of people they
know well and keep anonymous their
data to the larger network.
• Entry of false data is a potentially
serious issue—for doctors and
journalists alike. For example, take
reporting on the performance of
surgeons, an area in which data are
sorely needed. Suppose a disgruntled
patient wants to smear a surgeon
and fabricates multiple entries with
bad outcomes. Tools are needed for
verification. Suppose a person is part
of social network related to weight
control or hypertension and enters
false data. If just a few people are
guilty of false entries the overall
conclusions will not vary much. But
large numbers of people may have
a tendency to lie or distort their
personnel information even if their
identity is concealed.
Neither doctors nor journalists will
have been the first to venture into the
realm of figuring out how to utilize
wiki knowledge. In “Wikinomics,” by
Don Tapscott [see his article on page
18] and Anthony Williams, many positive
examples are presented that bring
collective Web-based knowledge to the
business model. Yet there are critics
of this approach, too. Andrew Keen,
author of “The Cult of the Amateur,”
argues that Web-based knowledge is
superficial and lacks deep and considered
judgment. Indeed, Web content
can be boisterous, unfiltered and
amateur. Yet if conventional knowl-
Nieman Reports | Winter 2008 43