social web mining

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c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 0 ( 2 0 1 0 ) 1623

j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b

Social Web mining and exploitation for serious applications:

Technosocial Predictive Analytics and related technologies

for public health, environmental and national security

surveillance

Maged N. Kamel Boulos a,, Antonio P. Sanfilippo b, Courtney D. Corley b, Steve Wheeler c

a Faculty of Health, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, UKb Techno-Social Predictive Analytics Initiative, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352, USAc Faculty of Education, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, UK

a r t i c l e i n f o

Article history:

Received 3 December 2009

Received in revised form

10 February 2010

Accepted 12 February 2010

Keywords:

Social Web

Web 2.0

Disease surveillance

Technosocial Predictive Analytics

a b s t r a c t

This paper explores Technosocial Predictive Analytics (TPA) and related methods for Web

data mining where users posts andqueries aregarneredfrom SocialWeb(Web 2.0) tools

such as blogs, micro-blogging and social networking sites to form coherent representations

of real-time health events. The paper includes a brief introduction to commonly used Social

Web tools such as mashups and aggregators, and maps their exponentialgrowth as an open

architecture of participation for the masses and an emerging way to gain insight about

peoples collective health status of whole populations. Several health related tool examples

are described and demonstrated as practical means through which health professionalsmight create clear location specific pictures of epidemiological data such as flu outbreaks.

2010 Elsevier Ireland Ltd. All rights reserved.

1. Background

The rapid development and growing popularity of the

Social Web, also commonly known as Web 2.0, and its

applications, coupled with the increasing availability of

mobile Internet-enabled devices (such as netbooks and AppleiPhone), has ensured that the Internet is now a truly

integral part of everyday life. The Social Web is a partic-

ularly dynamic medium that captures the pulse of the

society in real time. Blogs, micro-blogs such as Twitter

(http://twitter.com/), and social networking sites such as

Corresponding author at: Room 05, 3 Portland Villas, Faculty of Health, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA,UK. Tel.: +44 0 1752 586530; fax: +44 0 7053 487881.

E-mail addresses: [email protected] (M.N. Kamel Boulos), [email protected] (A.P. Sanfilippo),[email protected] (C.D. Corley), [email protected] (S. Wheeler).

Facebook (http://www.facebook.com/), enable people to pub-

lish their personal stories, opinions, product reviews and

a great deal more in real time. They can also share geo-

tagged alerts and reports about their current location and

allow others to follow their whereabouts, all in real time,

using location-aware social services such as Microsoft Vine

(http://www.vine.net/). Such applications have greater power

when coupled to Social Web mashup services and aggre-

gators. Such tools weave data from disparate sources into a

new, compound data source or service [1,2]. A useful exam-

ple of the latter is HealthMap, the global disease alert map

(http://www.healthmap.org/en).

0169-2607/$ see front matter 2010 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.cmpb.2010.02.007
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://twitter.com/mailto:[email protected]:[email protected]:[email protected]:[email protected]://www.facebook.com/http://www.vine.net/http://www.healthmap.org/enhttp://dx.doi.org/10.1016/j.cmpb.2010.02.007http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://www.healthmap.org/enhttp://www.vine.net/http://www.facebook.com/mailto:[email protected]:[email protected]:[email protected]:[email protected]://twitter.com/http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 0 ( 2 0 1 0 ) 1 6 2 3 17

Fig. 1 How To Use Who Is Sick? (Screenshot from http://whoissick.org/sickness/).

The dynamic nature and continuously updated features of

the Social Web make it a fertile environment for intelligencegathering in a variety of disciplines, enabling users to tap into

the wisdom of the crowds. Further, the Social Web is useful

for gleaning the collective impression of the masses regard-

ing current matters, events and products [3]. In fact, it is not

uncommonthese days for example,to see shoppers(including

those who do not make their purchases through the Internet)

consulting online user reviews and ratings on sites such as

Amazon.com about products they are planning to buy before

making their purchase decisions.

This paper will briefly review a number of emerging

technologiesand tools, includingTechnosocialPredictiveAna-

lytics pioneered at Pacific Northwest National Laboratory,

USA (http://predictiveanalytics.pnl.gov/ ), that can be used toexploit these inherent Social Web features in real or near-real

time and harness them for public health, environmental and

national security surveillance purposes.

2. A brief introduction to TechnosocialPredictive Analytics and some relatedtechnologies and tool examples

2.1. Who Is Sick: the power of user-generated maps

Who Is Sick (http://whoissick.org/sickness/ , Fig. 1) was

launched in 2006 as a location-aware service for tracking andmonitoring sickness, with the aim of providing current and

local sickness information by the public to the public. People

can anonymously post information about their own illness

and use a familiar Google Maps interface to search and fil-

ter illness by symptoms, sex, age, and location. The maps act

as a crude syndromic surveillance tool in real time. A person

feeling unwell can instantly check what sicknesses are circu-

lating within their own locale. Health-conscious individuals

conduct similar scans and take anynecessary preventive mea-

sures in preparation. People traveling to another area of the

country or abroad can use the service to learn if there are any

sicknesses going around that they need to be aware of. One

should be warned however, that information quality is always

dependent upon the accuracy of user-reported details and on

sufficient numbers of people (who are actually sick) using thetool to report their symptoms in a timely manner.

2.2. We Feel Fine: differentiating between informative

and affective text on the Social Web

Created in 2006, We Feel Fine (http://www.wefeelfine.org/)

is a Web-based applet and API (Application Programming

Interfacehttp://wefeelfine.org/api.html ) that can be used to

assess the fluctuating effects of real world events and factors

such as the Stock Index or the weather on peoples feelings

and emotions, and ascertain a crude idea about the general

mood of populations at different dates and locations around

the world [4].At the heart of We Feel Fine is a data crawler

that automatically scours the Web every 10min, har-

vesting data in the form of human feelings from a

large number of blogs and social pages such as those

hosted by MySpace (http://www.myspace.com/) and Blogger

(http://www.blogger.com/). We Feel Fine scans blog posts for

occurrences of the phrases I feel and I am feeling, and

once any of these is found, the system goes backward to the

beginning of the sentence and forward to the end of the sen-

tence, and then saves the full sentence in a database. Saved

sentences are next scanned against a manually compiled list

of about 5000 pre-identified feelings (adjectives and some

adverbs). If a valid feeling is found, the corresponding sen-tence is considered to represent one person who feels that

way. We Feel Fine also attempts to extract the username of

theposts author from the URLof his/her blog post (e.g., steve-

wheeler in http://steve-wheeler.blogspot.com/ ) and uses it

to automatically traverse the blog hosting site to locate that

user profile page. From the profile page, it is often possible to

extract the age, gender, country, state, and city of residence

of the blog owner. We Feel Fine can also retrieve the local

weather conditions for the blog owners city at the time the

post was written. This process results in about 15,000to 20,000

saved feelings (with associated data) per day. For display pur-

poses, the top 200 feelings were manually assigned colours

that loosely correspond to the tone of the feeling, e.g., happy
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://whoissick.org/sickness/http://predictiveanalytics.pnl.gov/http://whoissick.org/sickness/http://www.wefeelfine.org/http://wefeelfine.org/api.htmlhttp://www.myspace.com/http://www.blogger.com/http://steve-wheeler.blogspot.com/http://steve-wheeler.blogspot.com/http://www.blogger.com/http://www.myspace.com/http://wefeelfine.org/api.htmlhttp://www.wefeelfine.org/http://whoissick.org/sickness/http://predictiveanalytics.pnl.gov/http://whoissick.org/sickness/http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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Fig. 2 Screenshot of the available filtering options (feeling, gender, age, weather, location [country city], date) in the We

Feel Fine applet (the applet can be accessed by clicking on Open We Feel Fine link athttp://www.wefeelfine.org/).

positive feelings are graphically represented using bright yel-low and sad negative feelings are depicted in dark blue. The

applet also offers a number of filtering options for querying

the underlying database (Fig. 2).

While We Feel Fine functions as intended with its

pre-coordinated search strategy and list of feelings, it is

rather limited and cannot fully capture all possible affective

expressions and differentiate between them and informative

sentences in Social Web text. One can easily appreciate how

complex this task is by searching for some keywords: such

as flu at BingTweets (http://bingtweets.com/ ) and observing

the nonstop tweets in the resulting real-time Twitter feed

pane: there will almost always be people tweeting about

their own flu sickness (e.g., back to bed to try and sleep offthis flu. My sinuses feel numb!counts as one flu case) and

others just sending informative tweets about flu (sharing

a news item, some related research or information, or ask-

ing a question, e.g., How business travelers can avoid swine

flu: http://bit.ly/zBOuYdoes not count as a flu case). The

challenge here is to devise an algorithm that can reliably dif-

ferentiate between the two types of tweetsautomaticallyand

in real time. Ni et al. [5] describe one solution using a machine

learning method for classifying informative and affective blog

posts.

We Feel Fine also attempts to perform some geographical

classification of the feelings it continually gathers from the

blogosphere, but again it does so in a rather limited fashion.

The limitations extend beyond looking at users profiles forlocation details (which are not always available), because there

may also be issues to individual posts. A post by, for example

a user from the USA, might relate to that users experience at

a totally different location to the one they are normally resid-

ing in, e.g., the Bloggers holidays in Italy. Fortunately, more

sophisticated technologies exist today, such as MetaCarta

(http://www.metacarta.com/), which can be applied to sift

through Social Web text and extract much more compre-

hensive geographical references, while intelligently handling

any geographical name ambiguities or inconsistencies in the

scanned text [6].

2.3. Google Flu Trends: an example of infodemiologyand infoveillance in action

Eysenbach [7,8] defines infodemiology as the science of

distribution and determinants of information in an elec-

tronic medium, specifically the Internet, or in a population,

with the ultimate aim to inform public health and pub-

lic policy. He further goes on to define a related concept,

infoveillance, as a similar science whose primary aim is

surveillance. Infodemiology and infoveillance rely on auto-

mated and continuous analysis of unstructured, free text

information available on the Internet. Thisincludes analysisof

search engine queries (or what Eysenbach calls the demand

side), as well as of what is being published on Web sites, blogs,
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://www.wefeelfine.org/http://bingtweets.com/http://bit.ly/zBOuYhttp://www.metacarta.com/http://www.metacarta.com/http://bit.ly/zBOuYhttp://bingtweets.com/http://www.wefeelfine.org/http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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Twitter, etc. (or the supply side, to use again Eysenbachs

terminology).

Building on the same infodemiology and infoveillance

conceptsdescribed by Eysenbach, researchers at Google found

that certain search terms are good indicators of flu activity

[9]. Google Flu Trends (http://www.google.org/flutrends/) uses

aggregated Google search data to estimate flu activity up to

two weeks quicker than traditional systems. Such an earlydetection of disease activity (in close to real time), when fol-

lowed by an appropriate rapid response, has the potential of

reducing the impact of both seasonaland pandemic influenza.

Trends (continuously updated) are currently available from

Google for Australia, New Zealand, the United States (Fig. 3),

Mexico and sixteen more countries. A related project, Infovigil

(http://www.infovigil.com/ ), has recently been launched by

Eysenbach and colleagues at the Centre for Global eHealth

Innovation, Toronto, Canada, and is introduced in [8]. (Seealso

the Technosocial Predictive Analytics approach to monitoring

of seasonal influenza later in this paper.)

2.4. Emergency and public health virtual situationrooms using 3D serious gaming technology: getting the

big picture in real time

Conventional situation rooms are routinely used to oversee

public health emergencies and disaster management opera-

tions in realtime. Nowadays, large amounts of emergency data

are increasingly coming from a wide range of sources in real

or near-real time and need to be cross-linked and visualized

where they spatially belong on maps of the affected regions.

Also, emergencies are usually managed by multi-professional

teams who, not uncommonly, are distributed in multiple geo-

graphic locations. Because of these reasons, we have started

to see physical situation rooms gradually being replaced (orcombined) with virtual situation rooms that use onlinecollab-

orative (and mostly 2Dtwo-dimensional) platforms such as

Depiction (http://www.depiction.com/), in addition to conven-

tional Web conferencing. These platforms, although usable

and helpful, leave much to be desired, as they are lacking the

third dimension, which is needed to create a proper percep-

tion of the emergency space, as well as a sense of co-presence

of other virtual team members [1,10,11].

To overcome this limitation, Kamel Boulos [11,12] pro-

posed the development of novel emergency and public health

virtual situation rooms in a suitable 3D (three-dimensional)

virtual world (sometimes also called 3D serious gaming

platform), where animated 3D graphical self-representations(avatars) of experts and professionals can collaborate together

and discuss incident data in real time in a simulated 3D

space representing the physical location where the emer-

gency/public health incident of interest is unfolding and

reflecting all changes taking place there (again in real time).

The real-time link between the virtual world and the physical

world incident would be two-way and multimodal involving

geo-tagged physical/environmental sensor data feeds, citizen-

contributed data (as found in Microsoft Vine and Who Is

Sick), data gleaned via automatic analysis of Social Web

content, textual and 3D spatialized audio/voice exchanges

between virtual team members, video feeds, 3D simulations

and animations, various Web mashups, and shared desk-

top applications, among other possibilities. Various what-if

scenarios can also be explored and tested in the virtual

environment, making the proposed rooms much suited for

real-time emergency and disaster management, e.g., for man-

aging an influenza pandemic and planning and coordinating

actions at global, regional and local levels.

To achieve the above vision, the proposed collabo-

rative and interactive situation room platform wouldmarry virtual globes or 3D mirror worlds (such as

Google EarthTMhttp://earth.google.com/) and 3D virtual

worlds (such as Second Lifehttp://secondlife.com/ and

OpenSimhttp://opensimulator.org/ ) [1,11,12], and comple-

ment and tightly integrate them with other key technologies,

e.g., real time, geo-tagged RSSReally Simple Syndication

data feeds and geo-mashups (using Web services such as

Yahoo! Pipeshttp://pipes.yahoo.com/ ). The platform would

weave data and services in real time from different sources

into a new rich datascape that better reflects the current

situation (the big picture) in novel visual ways that are easier

to understand and manage. The platform has to be secure,

enabling multiple distributed persons to see each other,visualize relevant data together in unique ways, conduct

3D simulation and training scenarios, and collaborate in

real time, each according to their assigned role and access

privileges. True stereoscopic vision can be added, if needed,

using readily available technologies such as NVIDIA 3D Vision

(http://www.nvidia.com/object/3D Vision Overview.html) for

more realistic 3D visualization, with a bettersense of 3D depth

and relief. It is noteworthy that it is now possible to stream a

3D virtual world to a suitable mobile phone (see, for example,

http://www.youtube.com/watch?v=XwRnjbkljnc ) or other

Internet-enabled, small form factor mobile devices, making

this vision end-user device and platform-independent and

thus suitable for those members of the emergency operationsteam who are on the move.

2.5. Technosocial Predictive Analytics: a systems

science approach to proactive anticipatory reasoning

Events occur daily that challenge the health, security and

sustainable growth of our society, and often find us unpre-

pared for the catastrophic outcomes. These events involve

the interaction of complex processes such as climate change,

emerging infectious diseases, energy reliability, terrorism,

nuclear proliferation, natural and man-made disasters, and

geopolitical, social and economic vulnerabilities. If we are to

prevent the adversities and leverage the opportunities thatemerge from these events, integrated anticipatory reasoning

has to become an everyday activity. Technosocial Predictive

Analytics (TPA [13]) supports anticipatory reasoning through a

multidisciplinary approach to strategic analysis and response

that enables a concerted decision-making effort by analysts

and policymakers.

There is increased awareness among subject-matter

experts, analysts, and decision makers that a combined

understanding of interacting physical and human factors is

essential in addressing strategic decision making proactively.

For example, multilevel studies that consider a broad range

of biological, family, community, socio-cultural, environmen-

tal, policy, and macro-level economic factors are necessary to
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://www.google.org/flutrends/http://www.infovigil.com/http://www.depiction.com/http://earth.google.com/http://secondlife.com/http://opensimulator.org/http://pipes.yahoo.com/http://www.nvidia.com/object/3D_Vision_Overview.htmlhttp://www.youtube.com/watch%3Fv=XwRnjbkljnchttp://www.youtube.com/watch%3Fv=XwRnjbkljnchttp://www.nvidia.com/object/3D_Vision_Overview.htmlhttp://pipes.yahoo.com/http://opensimulator.org/http://secondlife.com/http://earth.google.com/http://www.depiction.com/http://www.infovigil.com/http://www.google.org/flutrends/http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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Fig. 3 Google Flu Trends for the United States (screenshot ofhttp://www.google.org/flutrends/intl/en us/). The site was

displaying a data current through: October 04, 2009 on the day this screenshot was taken (on October 05, 2009). Users canalso view flu activity by individual state.

prevent and mitigate the emergence of health threats such

as childhood obesity [14] and drug addiction [15]. Integrated

understanding of the infrastructural, social and ideational

context in which contentious social movements operate is

an essential analytical step in framing the emergence of

violent behaviour [16]. Combined understanding of anthro-

pogenic effects (e.g., chemical waste) and natural processes

(e.g., solar variation) is needed to predict the impact of global

warming [17]. Insights on human cognitive and emotional

biases improve our understanding of economic decisions andtheir effect on market prices, returns, and the allocation

of resources [18]. TPA provides theoretical and operational

advancements of these insights.

The human brain provides a basic framework for mem-

ory and prediction in which decision making emerges as a

process of pattern storage, recognition and projection rooted

in our experience of the world and driven by perception

and creativity [19]. Qualities such as the ability to focus on

what is perceived to be most important in the moment

and the capacity to make quick decisions by insight and

intuition make human judgment uniquely effective [20,21].

However, the same qualities can also be responsible for fal-

lacious reasoning when judgment lacks sufficient knowledge

and expertise [22], and is biased by factors such as group-

think [23,24], limitations in memory and focus attention

[25,26], positive framing [27] and increased confidence in

extreme judgments and highly correlated observables [28].

TPA addresses these gaps by using knowledge management

andmodeling to informhumanjudgment andanalytical gam-

ing to neutralize biased reasoning. Morespecifically, TPA helps

analysts and policymakers provide better proactive analysis

and response by enabling naturalistic decision making while

countering adverse influences on human judgment through acombined set of capabilities that (a) provide multidisciplinary

knowledge reach-back to inform analysis and response dur-

ing decision making; (b) supplement the expertise of the

analyst and policymaker with simulated scenarios generated

by computational models that integrate human and physi-

cal factors, and (c) engage analysts and policymakers within

a gaming environment that stimulates creative critical rea-

soning through visual analysis and collaborative/competitive

work, as described in Fig. 4a.

TPA offers a collaborative visual analytic environment

with user-friendly access to predictive models in the areas

of security, energy and the environment that are informed

by knowledge management processes, as shown in Fig. 4b.
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://www.google.org/flutrends/intl/en_us/http://www.google.org/flutrends/intl/en_us/http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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Fig. 4 Top diagram (a) describes TPAs capability components. Bottom diagram (b) illustrates TPAs operational

environment.

Such an environment promotes model integration through

innovative algorithms that enable the combination of diverse

modeling paradigms such as Bayesian networks and system

dynamics. Integrated modeling is informed by a Knowl-

edge Encapsulation Framework that facilitates the distillation

and vetting of relevant knowledge from heterogeneous

data streams, including social media. The TPA collaborative

working environment enables analysts and policymakers to

stress-test the validity of their analysis and policy plans using

techniques such as role-playing and serious gaming. Gaming

data are stored over time and analyzed to make inferences

about strategic decision making in context from social inter-

action during game-play.

TPA provides an ideal systems science approach for pub-

lic health informatics with reference to challenges such as

emerging infectious diseases. Current epidemic models char-

acterize effects of transportation, demographic traits, schools,

healthcare access, and host-to-hostdynamics. TPA can extend
http://dx.doi.org/10.1016/j.cmpb.2010.02.007http://dx.doi.org/10.1016/j.cmpb.2010.02.007


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Fig. 5 CDC ILINet vs. normalized flu-keywords: containing ILI-based blog post frequency per week.

these existing modeling paradigms through the integration

of socio-cultural models to provide a more accurate char-acterization of how public health responses reflect factors

such as social order, individual and community-based health

knowledge, and behaviour change due to public health com-

munications in mass media and emerging Web technologies.

One specific application of the TPA approach to public

health informatics is the monitoring of seasonal influenza

through Web and social media. As described earlier in this

paper, many Internet users formulate search queries and post

entries in blogs and discussion forums using terms related to

influenza-like-illness (ILI) to identify sites and otherusers that

offer medical diagnosisand advice [9,29]. There is a clear sense

in which an increase/decrease in the number of ILI-based

searches and posts in blogs and discussion forums reflects ahigher/lower public concern for the spreading of influenzaand

can therefore be used to monitor seasonal influenza. Such an

insight is corroborated by observed correlations of ILI-based

blog posts and surveillance reports from sentinel health-

care providers. For example, Fig. 5 plots ILI-based blog posts

(source: http://www.spinn3r.com) with outpatient ILI Surveil-

lance Network (ILINet) by the US Centers for Disease Control

and Prevention on a weekly basis for the period from Octo-

ber 05, 2008 to January 25, 2009. The evaluation of Pearsons

correlation coefficient for the two data series yields a strong

(r = 0.545) and statistically significant (95% confidence) correla-

tion value. Such results and analyses can greatly improve the

quality of existing models of seasonal influenza.

3. Conclusions

Technosocial Predictive Analytics is still in its infancy, but

nevertheless provides health professionals with a powerful

method of exploiting vast, continuous streams of user-

generated content on a rapidly proliferating worldwide social

network. As the technology develops it will provide more

sophisticated and reliable tools for public health use. It must

be emphasized however, that such techniques are only appli-

cable where large proportions of a society regularly use Social

Web services. There are still many people and sometimes

entirecommunities whodo notuse theWeb on a regular basis.

For such communities there may be no easy way to gain gen-eral health data, and it is inadvisable to extrapolate findings

from other communities in an attempt to predict their health

state. However, as Social Web use increases exponentially due

to technology and connectivity becoming more widely avail-

able andaffordable, this situation will rapidly change. There is

therefore a need to monitor the continuously shifting demo-

graphic characteristics of a variety of Social Web services that

populations are using. Each social network service and its

prevailing user characteristics such as age, gender and preva-

lent user geographic locations should be considered as these

data can help in better interpreting any intelligence or results

derived from these services.

r e f e r e n c e s

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