Citizen Sensing - Revision history

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[[File:Top_image.jpg|frameless|left|upright|alt= Gears Image]]Citizen Sensing, Social Signals, and Enriching Human Experience
 
Amit Sheth 

A computer which can calculate the Question to the
Ultimate Answer, a computer of such infinite and
subtle complexity that organic life itself shall form
part of its operational matrix. And you yourselves
shall take on new forms and go down into the computer
to navigate its ten-million-year program! Yes!
I shall design this computer for you. And I shall
name it also unto you. And it shall be called ... The
Earth." -Douglas Adams, The Hitchhiker’s Guide to
the Galaxy

Douglas Adams's stupendous vision — the
Earth transformed into a supercomputer
powered by human intelligence — was
fictional but reflects the potential of the most
recent advances in science and technology to
transform our planet into a powerful computing
platform. With 6 billion human inhabitants
acting as processing nodes, Earth could indeed
become the computer that provides the best answers
to life’s most complex and difficult questions
(http://icsc.eecs.uci.edu/abstract_wed1.html). Such a platform's computing power could
even exceed the exponential growth predicted
by the famous Moore's law.
It might seem like science fiction at first
blush, but with the Internet serving as the communication
backbone that connects us all, we
could reach this point sooner than we think.
When Time magazine named "you" as its person
of the year in 2006, it captured the infinite possibilities
brought forth by connecting humans
and providing a platform to harness their collective
intellect, knowledge, and experiences. As
much as we can't question the role technology
has played in fostering this new era of computing,
central to its success has been the participation
of people from all walks of life. Through
each of our small but significant and sustained
contributions, we've created and maintained
vast repositories such as Wikipedia. We're also
helping machines organize the world's online
resources by tagging and sharing various bits
of information. New tools are extracting and
using the knowledge we've embedded into what
we've created to improve searching, browsing,
and decision-making, substantially improving
on software that didn't previously use such a
collective intelligence. In this article, I introduce the exciting paradigm
of citizen sensing enabled by mobile sensors
and human computing-that is, humans as
citizens on the ubiquitous Web, acting as sensors
and sharing their observations and views
using mobile devices and Web 2.0 services. 

 

Citizen-Sensor Networking

By contributing so much online content, many people have become "citizens" of an Internet- or
Web-enabled social community; the use of Internet-or Web-enabled mobile devices to upload
this data gives these devices the ability to act assensors. Thus, the term citizen-sensor network
refers to an interconnected network of people who actively observe, report, collect, analyze,
and disseminate information via text, audio, or video messages.

This combination of human-in-the-loop
sensing, Web 2.0, and mobile computing has led to the emergence of several citizen-sensor
networks. In particular, Web 2.0 fostered the open environment and applications for tagging,
blogging, wikis, and social networking sites that have made information consumption, production,
and sharing so incredibly easy. However, two significant developments in mobile computing
helped enable citizen-sensor networks as we know them today: enhanced features such as
GPS capability and cameras became a standard part of most mobile devices, and large companies created open mobile operating
systems, such as Apple's OS X for the iPhone and Google's Android.

[[File:Fig1 citizenSensing.jpg|frame|center|upright|alt= Figure1.|Figure 1. Model for sensing. The integration of machine-sensing with citizen-sensing provides for an enhancedexperience and situational awareness that’s more complete than either form of sensing could provide alone. ]]

Microblogging - in which users
share short messages (roughly 140 characters) and pictures, typically over the Web - is of particular interest
to citizen-sensors. This relatively new medium emerged on the Web in 2006 and achieved widespread adoption
extremely quickly. Twitter, the most popular microblogging application, has nearly 6 million members
who post almost 2 million messages per day (http://twitterfacts.blogspot.com/2007/06/twitter-number-of-tweets-per-day.html). Applications
such as Twitterific and tweetie enable microblogging on mobile platforms, in which users can directly post photos and other digital captures of the
events they observe onto the Web or social networking sites from their mobile devices.

Such applications have virtually
eliminated the barriers of entry to participation and seem to have actively encouraged the emergence of citizen journalism and science. Other
examples of citizen journalism include Wikinews, a growing number of sites and services such as CNN's IReport, Demotix, and Merinews.
More recently, organizations such as the Boston police department have embraced citizen-sensors to assist in crime prevention (www.cityofboston.gov/police/cristop.asp). Several citizen science projects involve participants with mobile devices capturing observations and reports for environmental data collection, bird and
animal counts, and more. One of the most visible uses of citizen-sensors occurred during the Mumbai terrorist
attacks last November, when tweets (Twitter updates) and Flickr feeds by citizens armed with mobile phones reported observations of events in
real time, often well before traditional media reports could do so (www.informationweek.com/blog/main/archives/2008/11/twitter_in_cont.html).

The interesting twist that citizen-sensor networks bring to reporting a news story or scientific discovery is
that they can record and report an event from multiple angles and perspectives. The messages that citizensensors
send or upload come with a host of additional information, such as the spatiotemporal metadata provided
in the devices used to capture them (www.cnet.com.au/tag/camera-data-iphone-location.htm). Generally, an event has a time, location,
and multiple thematic elements, which in turn become the basis of its semantic description. A collection
of thematically (conceptually), spatially, and temporally related events define a situation; situational awareness,
which represents 'perception of the environmental elements within a volume of time and space, and the comprehension of their meaning"
(http://en.wikipedia.org/wiki/Situational_awareness) then leads to insight and actionable information.

The human-in-the-loop aspect
of citizen or participatory sensing offers several advantages to traditional (machine) sensing. Machines
are good at symbolic processing but poor at perception, which is the act of converting sensory information into
symbols or words that are meaningful to humans. Placing humans in the sensing loop greatly alleviates this
deficiency: sensors or devices can perform continuous, long-term sensing of a limited or fixed set of modalities,
but humans are much better at contextualizing and discriminating (deciding what's interesting or
important) data, filtering (reporting on things of interest and importance) it across multiple modalities,
and capturing the resulting observations for future symbolic processing by machines or collectively with
other humans. Humans are also better at using sensing and perception to adapt to subsequent activities, which in turn affect what they observe and
report. What gives humans this distinct advantage is their ability to deal with semantics and leverage extensive
background knowledge, experience, common sense, and complex reasoning, even with fuzzy data or
inconsistent information. Although traditional sensors merely report encoded observations, humans process
observations from a citizen-sensor network via their intellect and available contextual knowledge. 

[[File:Fig2 citizenSensing.jpg|frame|center|upright|alt= Figure1.|Figure 2. Citizen-sensor data. Semantic annotation integrates raw information from citizen-sensors and leads to
situational awareness. ]]

Situational awareness is the perception of an environment along with its temporal, spatial, and thematic
components, and is critical for making decisions in complex situations. A first step in a systematic
approach to situational awareness is to model sensing as a cycle of operations involving observation, perception,
and communication. Figure 1 shows both citizen- and machinesensing in this general framework. Within the perception and communication
operations, citizen- and machine-sensors can share information that might provide enhanced situational awareness that neither
sensing system could offer alone. Two recent advances are noteworthy in this context: the ability to treat
sensors as services on the Web (via standards such as Sensor Web Enablement) and the emergence of mobile
sensing with humans in the loop (because humans are much better at reacting to observations). 

Moreover, researchers have made
several computational advances in terms of the Semantic Web and its derivatives1 and in the corresponding ability to develop domain models
(ontologies) and knowledge bases, semantically annotate all types of data (specifically, to extract spatial,
temporal, and thematic metadata), and computationally exploit data along these three dimensions.2 As
Semantic Web proponents know, annotation is the key to making data more meaningful, both for human consumption and for machine computation.
Semantically annotated sensor data is more easily integrated, interpreted, and combined with databases, knowledge bases, and
advanced computing capabilities. Although I've discussed semantic annotation of (machine) sensor data as part of this column before, let's
shift the focus here to semantic annotation of — and metadata extraction from — messages submitted in citizen-sensors. Both of these capabilities
share characteristics with the semantic annotation of casual text, such as that used in social networking content.

 

Semantic Annotation
of Citizen-Sensor Data 


The high level of citizen participation in disseminating information during last year’s terrorist attacks in Mumbai, India, demonstrated
the growing power of citizen journalism. Using Flickr and Twitter, ordinary people such as Vinu Ranganathan shared their views
of the events as they unfolded (ht tp://www3.f lickr.com/photos/vinu/sets/72157610144709049). Although user contributions played an invaluable role in disseminating
news, we can realize significant additional value through their integration with semantic analysis, which leads to situational awareness
(http://knoesis.wright.edu/library/resource.php?id=00702).



The example depicted in Figure 2 shows metadata gathered from Twitter updates and Flickr images posted
during the Mumbai attacks. //We?// can use it to extract spatial information about a resource (such as
geo-coordinates for where a picture was taken or from where a message was posted) to determine the closest
street address. From the image information in Figure 2, for example, we can identify the closest street address
as 5, Hormusji Street, Colaba, Mumbai. When given to an "address to location" service, this information
yields prominent locations near this address, including the Nariman House, Vasant Vihar, and the Income
Tax Office. Next, by using temporal information from the image, we can get Twitter messages posted around
the time it was taken; spatial information helps restrict the geography to just where these messages originated.
The location information in conjunction with semantic models that describe a particular domain of
interest (terrorism, in this context) let us connect tweets that describe the event to images found in Flickr.
Such integration provides a richer description of the event and lets us create trails of various events.



The bursty and high-throughput nature of citizen-sensor data, the thematic differences between messages, and the text's unmediated and casual nature pose several interesting research challenges, suchas determining the trustworthiness of information sources (http://news.yahoo.com/s/ap/20090511/ap_on_re_eu/eu_ireland_wikipedia_hoaxer),
creating semantic models for general-purpose domains, and integrating application-specific semantic metadata across information sources.


 

Thematic Analysis and Casual Text 


The problem of semantically integrating citizen-sensor data is nontrivial. On one hand, the social context surrounding
the production of such data offers exciting opportunities, but on the other, this same social context introduces challenges in terms of the
content’s informal nature. Off-topic discussions are common, making it difficult to automatically identify
context. Moreover, the content is often fragmented, doesn’t always follow grammar rules, and relies
heavily on domain- or demographic-specific slang, abbreviations, and entity variations (using skik3 for SideKick 3, for example). Content
from microblogging sites is rather terse by nature, so all these factors combined make the process of automatically identifying what a message
is actually about that much harder.



Semantic annotation of content refers to the process of making data more meaningful through labels (via
marking up, tagging, or annotating) that conform to an agreed-upon reference model, be it a common nomenclature,
dictionary, taxonomy, folksonomy, or ontology that models a specific domain. Annotations with
these vocabularies make Web-based documents and data understandable to machines as well as easier to integrate
and analyze. When applications use ontology rules, whether they range from simple to complex
or are explicitly stated or inferred from the ontology's class properties and relationships, such applications
can realize powerful reasoning over annotated data.



User-generated content (UGC)and other observations from citizensensor networks have unique characteristics
that set them apart from the traditional content found in news or scientific articles. Coupled with the
issues associated with social media content mentioned earlier (such as textual informality), the task of annotation
becomes even more challenging when entities named with English language-words (Stephen King’s novel It, Madonna’s album
Music, or Why, Arizona, one of the state’s smaller cities) must be identified within informal text. This is an
important challenge that Web 3.0 applications will consistently face — the process of automatically creating accurate
markups or annotations from UGC to common referenced models.



The key to semantically annotating content is the process of identifying and disambiguating named entities. In short, semantic annotation
transforms unstructured data into a structured representation that lets applications search, analyze, and aggregate information. When looking
for information about General Motors, for example, semantically annotated content can return analyses on all its variations, such as GM,
GenlMotors, and so on. Clearly, the roles of ontologies and knowledge bases in creating markups will be even more important than they were
before the social Web’s explosive growth — not only can they act as common reference models, but they’ll also play a crucial role in inferring
semantics behind UGC while supplementing well-known statistical and natural language processing (NLP) techniques. Consider this tweet from
the Mumbai terror attacks: "mumbai taj 4th floor left wing fire, live on desitv." Although natural language understanding is hard in itself, the
noncapitalization of key entities such as Mumbai and the Taj Hotel makes for inaccurate natural language parse structures (compare Figures
3a and 3b, generated using the Berkeley Natural Language Parser at http://nlp.cs.berkeley.edu/). In such scenarios, knowing from a domain
model that the Taj Hotel is a landmark in the city of Mumbai can offer meaningful support to the statistical strength of a corpus’s entities.


[[File:Fig3 citizenSensing.jpg|frame|center|upright|alt= Figure1.|Figure 3. Natural language parser. The structure for casual text can lead to inaccuracies without the help of a natural
language parser: (a) "mumbai taj 4th floor left wing fire, live on desitv" should read (b) "Mumbai Taj 4th floor left wing
fire, live on Desitv." ]]


Additional metadata that situational-awareness applications can exploit is the availability of spatial information, typically obtained from
the device generating the content. An important area of investigation for such imminent applications will be how to effectively supplement
existing statistical and NLP-based content-analysis frameworks with available domain knowledge and the spatial and social context surrounding
the generated content.


 

Creating Semantic Models 


Semantic integration of citizensensor data involves integration of spatial, temporal, and thematic information. However, integrating data on the basis of thematic information is a harder problem owing to the rich vocabulary people use when describing a particular situation. As a simple example, think of how to relate two pieces of content that talk about an explosion and a blast in the same spatiotemporal setting.



We can integrate data based on thematic information by using a variety of statistical NLP and knowledge-intensive techniques. An
intriguing possibility is to create and exploit semantic domain models to supplement traditional techniques, but creating a semantic model to
describe thematic information for general-purpose domains, such as disaster management, is a challenging problem in itself. The most
important aspect in the creation of domain models is the agreement required to define the domain. Although domain experts can come to
an agreement in specialized domains (as in biomedical and healthcare domains, for example), the same isn’t true for the Web. Broader and less
specific areas require fewer agreements, and a clique- or committeedriven approach won’t help anyone reach mass consensus for the larger
areas. For dynamic real-world events that rely on very narrow contexts, high-level concept models might be less useful. Instead, we might need
to rely on more community-driven sources of information to generate domain models. One such class of recent efforts used Wikipedia as
a source for extracting an ontology because it’s a community created and maintained source that reflects a degree of agreement.



Wikipedia's all-encompassing scope isn’t only a strength - it can be a weakness when we're only interested
in a specific domain. My work at Kno.esis6 assists the user in carving small and focused domain descriptions out of Wikipedia based
on a seed-category, article, or query. The resulting domain model contains concepts of immediate interest to the task at hand. To use it in classification
tasks or as a starting point for more formal ontology development, users can export it to OWL, RDF-S or XML. Representation of domain
knowledge is another important consideration. Unlike narrower, more constrained domains such as business and science, in which formal domain
modeling can enable powerful reasoning for search, aggregation, and integration purposes, lightweight knowledge representation is
both adequate and desirable when applications don’t need to exploit all of a domain model’s features.



Using domain models to semantically annotate unstructured data is a well-known research area. But to annotate
citizen-sensor observations, we must effectively complement spatial, temporal, and thematic data
processing with available domain knowledge. Consider the example in Figure 3, which mentions the Taj
Mahal Palace in Mumbai but refers to it only as the Taj. A domain model of landmarks in Mumbai along with
other contextual information about hotels that have wings and DesiTV being an Indian TV channel supports
the annotation of Taj with the concept Taj Mahal Palace in the domain model. Disambiguating such casual
mentions by referencing a common domain model facilitates citizensensor data aggregation.


 

Empowering Situation-Aware Applications 
In an ongoing effort, Kno.e.sis researchers have built a system called Twitris (http://twitris.dooduh.com) to
gather real-time citizen-sensor observations from Twitter that support spatial, temporal, and thematic analytics.
The goal is to preserve social signals and present event indicators that lead to situational awareness.
Let’s review some illustrative examples of how Twitris can be used to identify social signals by analyzing
tweets from around the world.

[[File:Fig4 citizenSensing.jpg|frameless|right|700x900px|link=|alt= Figure4|Figure 4 Example shows how Twitris preserves social signals from
the Mumbai terrorist attacks around the world via spatio-temporal-thematic analysis of citizen-sensor observations. ]]


What’s new and interesting? Consider a scenario in which two event descriptors "mumbai attacks" and "hawala funding" appear in citizen-sensor
observations — specifically, the term "mumbai attacks" has occurred every day in the past week whereas "hawala funding" is a new
descriptor for today. In most circumstances, users are more likely to be interested in perspectives and experiences
that differ from yesterday. Looking at spatial contexts, we also find that "hawala funding" doesn’t appear in any other country on the
same day, whereas "mumbai attacks" occurs in almost all of them, which implies that "hawala funding" is unique and a stronger descriptor
local to the US; "mumbai attacks" is a weaker descriptor in terms of its uniqueness to this local region.



Combining the spatial, temporal, and thematic components of event descriptors during their analysis can offer several opportunities for
presenting and using specific observations. Besides being able to find information about known facts and discover new ones, a spatio-temporal-
thematic analysis also allows situational-awareness applications to effectively preserve local and global social signals pertaining to
any real-world event. Consider this year’s G20 financial summit — by using appropriate spatial conditions, we can quickly assess what’s
being said about it in Asia versus in North America. Thus, the meaning and importance of entities found in citizen-sensor observations not
only depends on their distribution in a corpus of related observations, but also on how they’re discussed in other spatial and temporal settings.
Such analysis into isolating spatial and temporal social signals lets us ask a range of questions.



What’s a region paying attention to today? What are people most excited or concerned about? For any
particular event, a spatio-temporal thematic slice of citizen-sensor observations will readily tell us today's
prevalent descriptors or entities. Figure 4, for example, culls keywords and phrases out of citizen-sensor observations
pertaining to the Mumbai terrorist attacks from different parts of the world. Twitris has weighted the words by their distribution
within the country, their local versus global importance as relevant to the event, and the descriptor’s recent popularity. Such summaries are
most helpful for aiding situational awareness applications, rather than simply viewing a list of observations themselves.



How is an entity’s perception changing over time in any region? Allowing temporal aspects into analysis
also lets us observe how the perception of an event or entity changes over time. This ability is critical in understanding how an event progressed,
the key descriptors involved at important timelines, which perceptions originated in what specific regions, and so on.



Looking beyond today's primitive yet compelling capabilities for understanding and analyzing the data reported by citizen-sensors, the
future holds a much bigger promise for addressing more challenging problems and improving the human experience. Specifically, it
will involve using semantics and social computing to exploit what tens of billions of machine sensors and more than 3 billion citizen-sensors
produce on a regular basis. Before long, most of our work will focus on computing for the human experience - perhaps we’ll even witness
the Earth turn into a supercomputer during our lifetimes.


 

Acknowledgements 

This article incorporates research partially funded by the US National Science Foundation
(IIS award #071441) and performed by Kno.e.sis members (aggregation and integration of social data by Karthik Gomadam,
analysis of user-generated content by Meena Nagarajan, extraction of a domain model from Wikipedia by Christopher Thomas, and
Semantic Web sensors by Cory Henson).


 

References 

1. A. Sheth, C. Henson, and S. Sahoo, “Semantic
Sensor Web,” IEEE Internet Computing,
vol. 12, no. 4, 2008, pp. 78–83.

2. A. Sheth and M. Perry, “Traveling the
Semantic Web through Space, Time, and
Theme,” IEEE Internet Computing, vol.
12, no. 2, 2008, pp. 81–86.

3. A. Sheth and M. Nagarajan, “Semantics-
Empowered Social Computing,” IEEE Internet
Computing, vol. 13, no. 1, 2009, pp.
76–80.

4. F. Suchanek, G. Kasneci, and G. Weikum,
“Yago: A Core of Semantic Knowledge,”
Proc. 16th World Wide Web Conf. (WWW
2007), ACM Press, 2007, pp. 697–706.

5. F. Wu and D. Weld, “Automatically Refining
the Wikipedia Infobox Ontology,”
Proc. 17th World Wide Web Conf. (WWW
08), ACM Press, 2008, pp. 635–644.

6. C. Thomas et al., “Growing Fields
of Interest: Using an Expand and
Reduce Strategy for Domain Model
Extraction,” Proc. 2008 IEEE/WIC/
ACM Int’l Conf. Web Intelligence and
Intelligent Agent Technology, Sydney,
ACM Press, 2008, pp. 496–502.

Citation 
A. Sheth, 'Citizen Sensing', Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.[http://knoesis.org/library/resource.php?id=00158]

@@ Line 380: / Line 380: @@
 <span style="font-size:12pt;color:purple">Related Presentation:</span><br />
 A. Sheth,[http://knoesis.wright.edu/library/resource.php?id=00702   Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness] , Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.
 <br/>

← Older revision		Revision as of 16:36, 29 February 2012
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	<span style="font-size:12pt;color:purple">Related Presentation:</span><br />		<span style="font-size:12pt;color:purple">Related Presentation:</span><br />
−	A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.~~[http:~~/~~/knoesis.wright.edu/library/resource.php?id=00702 Kevin]~~	+
		+
		+
		+	A. Sheth,[http://knoesis.wright.edu/library/resource.php?id=00702 Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness] , Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.
		+
		+	<br/>

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	<span style="font-size:12pt;color:purple">Related Presentation:</span><br />		<span style="font-size:12pt;color:purple">Related Presentation:</span><br />
−	A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.[http://knoesis.wright.edu/library/resource.php?id=00702]	+	A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.[http://knoesis.wright.edu/library/resource.php?id=00702 Kevin]

← Older revision		Revision as of 16:33, 29 February 2012
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	<span style="font-size:12pt;color:purple">Citation</span><br />		<span style="font-size:12pt;color:purple">Citation</span><br />
	A. Sheth, 'Citizen Sensing', Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.[http://knoesis.org/library/resource.php?id=00158]		A. Sheth, 'Citizen Sensing', Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.[http://knoesis.org/library/resource.php?id=00158]
		+
		+	<span style="font-size:12pt;color:purple">Related Presentation:</span><br />
		+	A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, Keynote at From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.[http://knoesis.wright.edu/library/resource.php?id=00702]

@@ Line 80: / Line 80: @@
 systems, such as Apple's OS X for the iPhone and Google's Android.</p>
-[[File:Fig1 citizenSensing.jpg|frame|center|upright|alt= Figure1.|<b>Figure 1. Model for sensing. The integration of machine-sensing with citizen-sensing provides for an enhancedexperience and situational awareness that’s more complete than either form of sensing could provide alone.</b> ]]
+[[File:Fig1 citizenSensing.jpg|thumb|left|700x900px|link=|alt= Figure1.|<b>Figure 1. Model for sensing. The integration of machine-sensing with citizen-sensing provides for an enhancedexperience and situational awareness that’s more complete than either form of sensing could provide alone.</b> ]]
-−
 <p>Microblogging - in which users
@@ Line 97: / Line 96: @@
 animal counts, and more. One of the most visible uses of citizen-sensors occurred during the Mumbai terrorist
 attacks last November, when tweets (Twitter updates) and Flickr feeds by citizens armed with mobile phones reported observations of events in
-real time, often well before traditional media reports could do so (www.informationweek.com/blog/main/archives/2008/11/twitter_in_cont.html).</p>
+real time, often well before traditional media reports could do so (http://www.informationweek.com/blog/main/archives/2008/11/twitter_in_cont.html).</p>
 <p>The interesting twist that citizen-sensor networks bring to reporting a news story or scientific discovery is
@@ Line 122: / Line 121: @@
 observations from a citizen-sensor network via their intellect and available contextual knowledge. </p>
-[[File:Fig2 citizenSensing.jpg|frame|center|upright|alt= Figure1.|<b>Figure 2. Citizen-sensor data. Semantic annotation integrates raw information from citizen-sensors and leads to
+[[File:Fig2 citizenSensing.jpg|thumb|left|700x900px|alt= Figure2.|<b>Figure 2. Citizen-sensor data. Semantic annotation integrates raw information from citizen-sensors and leads to
 situational awareness.</b> ]]
 <p>Situational awareness is the perception of an environment along with its temporal, spatial, and thematic
@@ Line 133: / Line 131: @@
 sensing system could offer alone. Two recent advances are noteworthy in this context: the ability to treat
 sensors as services on the Web (via standards such as Sensor Web Enablement) and the emergence of mobile
 sensing with humans in the loop (because humans are much better at reacting to observations). </p>
 <p>Moreover, researchers have made
@@ Line 224: / Line 222: @@
 </p>
-[[File:Fig3 citizenSensing.jpg|frame|center|upright|alt= Figure1.|<b>Figure 3. Natural language parser. The structure for casual text can lead to inaccuracies without the help of a natural
+[[File:Fig3 citizenSensing.jpg|thumb|left|700x900px|link= |alt= Figure3.|<b>Figure 3. Natural language parser. The structure for casual text can lead to inaccuracies without the help of a natural language parser: (a) "mumbai taj 4th floor left wing fire, live on desitv" should read (b) "Mumbai Taj 4th floor left wing
 language parser: (a) "mumbai taj 4th floor left wing fire, live on desitv" should read (b) "Mumbai Taj 4th floor left wing
 fire, live on Desitv."</b> ]]
@@ Line 236: / Line 233: @@
 <br />
 <span style="font-size:12pt;color:purple">Creating Semantic Models</span><br />
@@ Line 285: / Line 281: @@
 tweets from around the world.
-[[File:Fig4 citizenSensing.jpg|frameless|right|700x900px|link=|alt= Figure4|<b>Figure 4 Example shows how Twitris preserves social signals from
+[[File:Fig4 citizenSensing.jpg|thumb|right|700x900px|link=|alt= Figure4|<b>Figure 4 Example shows how Twitris preserves social signals from
 the Mumbai terrorist attacks around the world via spatio-temporal-thematic analysis of citizen-sensor observations.</b> ]]

@@ Line 1: / Line 1: @@
 [[File:Top_image.jpg|frameless|left|upright|alt= Gears Image]]<span style="font-size:26pt;color:purple">Citizen Sensing, Social Signals, and Enriching Human Experience</span>
-<p>A computer which can calculate the Question to the
+<i>Amit Sheth </i> <br />
 Ultimate Answer, a computer of such infinite and
 subtle complexity that organic life itself shall form
-part of its operational matrix. And you yourselves
+part of its operational <br/> matrix. And you yourselves
 shall take on new forms and go down into the computer
 to navigate its ten-million-year program! Yes!
-I shall design this computer for you. And I shall
+I shall design this computer for you. And I <br /> shall
 name it also unto you. And it shall be called ... The
 Earth." -Douglas Adams, <i>The Hitchhiker’s Guide to
-the Galaxy</i></p>
+the Galaxy</i>
 <p><span style = "float: left; font-size: 40pt; line-height: 1; font-weight: bold;

@@ Line 318: / Line 318: @@
 the key descriptors involved at important timelines, which perceptions originated in what specific regions, and so on.
 </p>
 <p>
@@ Line 374: / Line 376: @@
 ACM Press, 2008, pp. 496–502.
 <span style="font-size:12pt;color:purple">Citation</span><br />
 A. Sheth, 'Citizen Sensing', Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.[http://knoesis.org/library/resource.php?id=00158]