<article>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#article10_02_24_2343219</id>
	<title>Cell Phone Data Predicts Movement Patterns</title>
	<author>samzenpus</author>
	<datestamp>1267035960000</datestamp>
	<htmltext>azoblue writes <i>"In a study published in Science, researchers examined customer location data culled from cellular service providers. By looking at how customers moved around, the authors of the study found that it may be possible to <a href="http://arstechnica.com/science/news/2010/02/cell-phones-show-human-movement-predictable-93-of-the-time.ars">predict human movement patterns and location up to 93 percent of the time</a>."</i></htmltext>
<tokenext>azoblue writes " In a study published in Science , researchers examined customer location data culled from cellular service providers .
By looking at how customers moved around , the authors of the study found that it may be possible to predict human movement patterns and location up to 93 percent of the time .
"</tokentext>
<sentencetext>azoblue writes "In a study published in Science, researchers examined customer location data culled from cellular service providers.
By looking at how customers moved around, the authors of the study found that it may be possible to predict human movement patterns and location up to 93 percent of the time.
"</sentencetext>
</article>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269494</id>
	<title>Re:Uh huh...</title>
	<author>dgatwood</author>
	<datestamp>1267130400000</datestamp>
	<modclass>Funny</modclass>
	<modscore>2</modscore>
	<htmltext><p>Well, 85\% of the time, it will be red.  The rest of the time, it's sort of a rust color as we pull it out of the river because you were talking on the cell phone and missed the "bridge out" sign.</p><p>Am I close?</p></htmltext>
<tokenext>Well , 85 \ % of the time , it will be red .
The rest of the time , it 's sort of a rust color as we pull it out of the river because you were talking on the cell phone and missed the " bridge out " sign.Am I close ?</tokentext>
<sentencetext>Well, 85\% of the time, it will be red.
The rest of the time, it's sort of a rust color as we pull it out of the river because you were talking on the cell phone and missed the "bridge out" sign.Am I close?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269270</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31272958</id>
	<title>What's the big deal? Just watch TV...</title>
	<author>joedoc</author>
	<datestamp>1267118100000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>I learned this four years ago on <em>Law and Order: Special Victims Unit</em>.
<br> <br>
Those <a href="http://lawandorder.wikia.com/wiki/Technical\_Assistance\_Response\_Unit" title="wikia.com">T.A.R.U. guys</a> [wikia.com] are some smart coppers.
<br> <br>
Benson and Stabler know where every perv in New York is located.</htmltext>
<tokenext>I learned this four years ago on Law and Order : Special Victims Unit .
Those T.A.R.U .
guys [ wikia.com ] are some smart coppers .
Benson and Stabler know where every perv in New York is located .</tokentext>
<sentencetext>I learned this four years ago on Law and Order: Special Victims Unit.
Those T.A.R.U.
guys [wikia.com] are some smart coppers.
Benson and Stabler know where every perv in New York is located.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269270</id>
	<title>Uh huh...</title>
	<author>spiffmastercow</author>
	<datestamp>1265140140000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>5</modscore>
	<htmltext>So you're saying that analyzing movement patterns allows you to predict movement patterns.  Would you like to guess the color of my red car?</htmltext>
<tokenext>So you 're saying that analyzing movement patterns allows you to predict movement patterns .
Would you like to guess the color of my red car ?</tokentext>
<sentencetext>So you're saying that analyzing movement patterns allows you to predict movement patterns.
Would you like to guess the color of my red car?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269844</id>
	<title>Re:Sleep and Work?</title>
	<author>noidentity</author>
	<datestamp>1267091880000</datestamp>
	<modclass>Troll</modclass>
	<modscore>0</modscore>
	<htmltext><blockquote><div><p>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.</p></div>
</blockquote><p>I predict you're in a non-mathematical field, perhaps banking.</p></div>
	</htmltext>
<tokenext>Seeing how 66.67 \ % of the time I am either sleeping at home or at work it should n't be too hard to fill the other 27 \ % with commute/grocery shopping .
I predict you 're in a non-mathematical field , perhaps banking .</tokentext>
<sentencetext>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.
I predict you're in a non-mathematical field, perhaps banking.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269538</id>
	<title>The science of stalking!</title>
	<author>mykos</author>
	<datestamp>1267130820000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext>BRB...this girl I used to date will be at Subway in five minutes.  Time to casually bump into her.</htmltext>
<tokenext>BRB...this girl I used to date will be at Subway in five minutes .
Time to casually bump into her .</tokentext>
<sentencetext>BRB...this girl I used to date will be at Subway in five minutes.
Time to casually bump into her.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271384</id>
	<title>Re:Sleep and Work?</title>
	<author>Idbar</author>
	<datestamp>1267109340000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>In more devastating news, they will find that predictability increases with your "nerd" status. Most likely they will find I'm in front of a computer... at my parent's basement... 90\% of the time (sort of like being in a desk drawer, but with the fun of the Internet)<br> <br>
Like I need some research study to remind me of my sedentary life.</htmltext>
<tokenext>In more devastating news , they will find that predictability increases with your " nerd " status .
Most likely they will find I 'm in front of a computer... at my parent 's basement... 90 \ % of the time ( sort of like being in a desk drawer , but with the fun of the Internet ) Like I need some research study to remind me of my sedentary life .</tokentext>
<sentencetext>In more devastating news, they will find that predictability increases with your "nerd" status.
Most likely they will find I'm in front of a computer... at my parent's basement... 90\% of the time (sort of like being in a desk drawer, but with the fun of the Internet) 
Like I need some research study to remind me of my sedentary life.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269186</id>
	<title>Wow!</title>
	<author>Beelzebud</author>
	<datestamp>1265139360000</datestamp>
	<modclass>Funny</modclass>
	<modscore>4</modscore>
	<htmltext>Imagine that.  If you study someone's daily routine you can "predict" where they will go.  Call me shocked.</htmltext>
<tokenext>Imagine that .
If you study someone 's daily routine you can " predict " where they will go .
Call me shocked .</tokentext>
<sentencetext>Imagine that.
If you study someone's daily routine you can "predict" where they will go.
Call me shocked.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271648</id>
	<title>Re:Wow!</title>
	<author>Anonymous</author>
	<datestamp>1267110720000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Yup<nobr> <wbr></nobr>... if those terrorist new when every person went for their morning visit to the john just think of the stink bombs they could build...</p></htmltext>
<tokenext>Yup ... if those terrorist new when every person went for their morning visit to the john just think of the stink bombs they could build.. .</tokentext>
<sentencetext>Yup ... if those terrorist new when every person went for their morning visit to the john just think of the stink bombs they could build...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269376</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271702</id>
	<title>Re:Sleep and Work?</title>
	<author>vxice</author>
	<datestamp>1267111080000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>This just in, people lead regular boring lives that are easy to predict with little concern for being tracked since most of us are not spies being constantly hunted.  More at 11.</htmltext>
<tokenext>This just in , people lead regular boring lives that are easy to predict with little concern for being tracked since most of us are not spies being constantly hunted .
More at 11 .</tokentext>
<sentencetext>This just in, people lead regular boring lives that are easy to predict with little concern for being tracked since most of us are not spies being constantly hunted.
More at 11.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269162</id>
	<title>duh... you don't say...</title>
	<author>Anonymous</author>
	<datestamp>1265139120000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>this is why we don't want them knowing were we are... unless we want them to know were we are by say making an emergency call...</htmltext>
<tokenext>this is why we do n't want them knowing were we are... unless we want them to know were we are by say making an emergency call.. .</tokentext>
<sentencetext>this is why we don't want them knowing were we are... unless we want them to know were we are by say making an emergency call...</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31278430</id>
	<title>Re:Sleep and Work?</title>
	<author>Anonymous</author>
	<datestamp>1267095540000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><div class="quote"><p>If they track my wife's phone, they'll notice she spends 93\% of the time in her desk drawer. Why the heck do I pay for her phone when she never has it on her!?</p></div><p>So she can call her boyfriends and go out while telling you she wasn't ignoring your calls; she must have forgotten her phone in her desk drawer.</p></div>
	</htmltext>
<tokenext>If they track my wife 's phone , they 'll notice she spends 93 \ % of the time in her desk drawer .
Why the heck do I pay for her phone when she never has it on her !
? So she can call her boyfriends and go out while telling you she was n't ignoring your calls ; she must have forgotten her phone in her desk drawer .</tokentext>
<sentencetext>If they track my wife's phone, they'll notice she spends 93\% of the time in her desk drawer.
Why the heck do I pay for her phone when she never has it on her!
?So she can call her boyfriends and go out while telling you she wasn't ignoring your calls; she must have forgotten her phone in her desk drawer.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269376</id>
	<title>Re:Wow!</title>
	<author>Anonymous</author>
	<datestamp>1267128960000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>My friend, it goes far, FAR beyond daily routine.  For instance, from cell phone data, you can tell when a person's plane just landed, because evidently there's a law that says once you land you must immediately call someone on your cell phone and announce that you just landed and then repeat that several times.  You know, in case they were picking you up and refused to read the screens indicating when your plane was going to land and wouldn't stand at the gate until you actually walked out.</p><p>But I digress.  From that cell phone data, you'd be able to predict someone's non-routine movements.  Scary huh.  And just think about if terrorists got that information.</p></htmltext>
<tokenext>My friend , it goes far , FAR beyond daily routine .
For instance , from cell phone data , you can tell when a person 's plane just landed , because evidently there 's a law that says once you land you must immediately call someone on your cell phone and announce that you just landed and then repeat that several times .
You know , in case they were picking you up and refused to read the screens indicating when your plane was going to land and would n't stand at the gate until you actually walked out.But I digress .
From that cell phone data , you 'd be able to predict someone 's non-routine movements .
Scary huh .
And just think about if terrorists got that information .</tokentext>
<sentencetext>My friend, it goes far, FAR beyond daily routine.
For instance, from cell phone data, you can tell when a person's plane just landed, because evidently there's a law that says once you land you must immediately call someone on your cell phone and announce that you just landed and then repeat that several times.
You know, in case they were picking you up and refused to read the screens indicating when your plane was going to land and wouldn't stand at the gate until you actually walked out.But I digress.
From that cell phone data, you'd be able to predict someone's non-routine movements.
Scary huh.
And just think about if terrorists got that information.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269186</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31273380</id>
	<title>Re:This was done last year</title>
	<author>piltdownman84</author>
	<datestamp>1267119780000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>I would guess the difference in that stat is the difference between European and American Culture.  In North America its easy to predict when people will be at work and sleeping, or about 66\% of their day.  In Europe, in my experience,  you can also include three hours every night out at the local pub, or 80\%.</htmltext>
<tokenext>I would guess the difference in that stat is the difference between European and American Culture .
In North America its easy to predict when people will be at work and sleeping , or about 66 \ % of their day .
In Europe , in my experience , you can also include three hours every night out at the local pub , or 80 \ % .</tokentext>
<sentencetext>I would guess the difference in that stat is the difference between European and American Culture.
In North America its easy to predict when people will be at work and sleeping, or about 66\% of their day.
In Europe, in my experience,  you can also include three hours every night out at the local pub, or 80\%.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269200</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269668</id>
	<title>coverup getting desperat.</title>
	<author>Cr0vv</author>
	<datestamp>1267089660000</datestamp>
	<modclass>Funny</modclass>
	<modscore>2</modscore>
	<htmltext>More and more we see stories like this, if you can connect the dots.  this is a NASA coverup for Planet X, pure and simple.  Fireballs almost every day or at least reported on every week, fallin' out of the sky.  it'll be happening more and more, then NASA will fall silent.  They do not tell the truth of what's out there, if you don't believe this:  your programmed.
Crow.</htmltext>
<tokenext>More and more we see stories like this , if you can connect the dots .
this is a NASA coverup for Planet X , pure and simple .
Fireballs almost every day or at least reported on every week , fallin ' out of the sky .
it 'll be happening more and more , then NASA will fall silent .
They do not tell the truth of what 's out there , if you do n't believe this : your programmed .
Crow .</tokentext>
<sentencetext>More and more we see stories like this, if you can connect the dots.
this is a NASA coverup for Planet X, pure and simple.
Fireballs almost every day or at least reported on every week, fallin' out of the sky.
it'll be happening more and more, then NASA will fall silent.
They do not tell the truth of what's out there, if you don't believe this:  your programmed.
Crow.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269152</id>
	<title>JUST 93\%?</title>
	<author>Anonymous</author>
	<datestamp>1265139060000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>JUST 93\%?</p></htmltext>
<tokenext>JUST 93 \ % ?</tokentext>
<sentencetext>JUST 93\%?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269448</id>
	<title>Like all marketing claims</title>
	<author>WinstonWolfIT</author>
	<datestamp>1267129860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>'Never' falls within 'up to 93\% of the time' n'est ce-pas?</htmltext>
<tokenext>'Never ' falls within 'up to 93 \ % of the time ' n'est ce-pas ?</tokentext>
<sentencetext>'Never' falls within 'up to 93\% of the time' n'est ce-pas?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</id>
	<title>Sleep and Work?</title>
	<author>Dayze!Confused</author>
	<datestamp>1265139000000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>5</modscore>
	<htmltext><p>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.</p></htmltext>
<tokenext>Seeing how 66.67 \ % of the time I am either sleeping at home or at work it should n't be too hard to fill the other 27 \ % with commute/grocery shopping .</tokentext>
<sentencetext>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31279908</id>
	<title>old news... ie very old!</title>
	<author>ThinkOfaNumber</author>
	<datestamp>1267103640000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I don't mean old as in "last week", this came up months ago, if not even pre 2009!</p><p>Not with cell phones, but some study was claiming to predict human location based on a study of your previous location, to a high degree of accuracy.</p><p>Now if I could just find the original link... I'm sure it was on slashdot...</p></htmltext>
<tokenext>I do n't mean old as in " last week " , this came up months ago , if not even pre 2009 ! Not with cell phones , but some study was claiming to predict human location based on a study of your previous location , to a high degree of accuracy.Now if I could just find the original link... I 'm sure it was on slashdot.. .</tokentext>
<sentencetext>I don't mean old as in "last week", this came up months ago, if not even pre 2009!Not with cell phones, but some study was claiming to predict human location based on a study of your previous location, to a high degree of accuracy.Now if I could just find the original link... I'm sure it was on slashdot...</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31272376</id>
	<title>Re:Sleep and Work?</title>
	<author>MonsterTrimble</author>
	<datestamp>1267115220000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>I was that way, except in reverse. I begrudgingly took on a pay as you go cell phone and it did OK for about a year. However it very quickly got to the point where it was a bigger pain in the butt to keep putting minutes on so I wouldn't lose the minutes I had plus the cost itself and I owned the phone itself. I switched to a plan when they put out a $10/month plan. In the end it was a 3 year contract, I got a nice new phone and it ended up about $3/month then pay as you go. Easily worth the $3 for the lack of headaches.</htmltext>
<tokenext>I was that way , except in reverse .
I begrudgingly took on a pay as you go cell phone and it did OK for about a year .
However it very quickly got to the point where it was a bigger pain in the butt to keep putting minutes on so I would n't lose the minutes I had plus the cost itself and I owned the phone itself .
I switched to a plan when they put out a $ 10/month plan .
In the end it was a 3 year contract , I got a nice new phone and it ended up about $ 3/month then pay as you go .
Easily worth the $ 3 for the lack of headaches .</tokentext>
<sentencetext>I was that way, except in reverse.
I begrudgingly took on a pay as you go cell phone and it did OK for about a year.
However it very quickly got to the point where it was a bigger pain in the butt to keep putting minutes on so I wouldn't lose the minutes I had plus the cost itself and I owned the phone itself.
I switched to a plan when they put out a $10/month plan.
In the end it was a 3 year contract, I got a nice new phone and it ended up about $3/month then pay as you go.
Easily worth the $3 for the lack of headaches.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269502</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269734</id>
	<title>Re:Sleep and Work?</title>
	<author>Anonymous</author>
	<datestamp>1267090500000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Huh? Where did the other time go?</p></htmltext>
<tokenext>Huh ?
Where did the other time go ?</tokentext>
<sentencetext>Huh?
Where did the other time go?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271976</id>
	<title>Great info for my Theft Ring</title>
	<author>SirLanse</author>
	<datestamp>1267112820000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>All this Cell phone data should be in the public domain.  There should be no privacy. Then my theft ring can predict when all your family are going to be out of the house.  Cross reference this with your Twitters and annual vacation travel and we can really clean you out.  Or perhaps the police just need to see if you have tags on all your pets. Or a quick look at your TVs to make sure you are paying that California Plasma tax.  After they see what they need, then they can get the warrant for a REAL search.
Maybe you have a cute daughter, I could show up at her favorite shop.  Cross reference with her Rx and know when she is on the peak of her cycle.  Look at credit card charges, give her some of her favorite treats.
Perhaps know when she rides her bicycle home from school and goes through that dark patch of woods.
Its a brave new world and Uncle Sam wants all your info available to anyone with hacker skills.</htmltext>
<tokenext>All this Cell phone data should be in the public domain .
There should be no privacy .
Then my theft ring can predict when all your family are going to be out of the house .
Cross reference this with your Twitters and annual vacation travel and we can really clean you out .
Or perhaps the police just need to see if you have tags on all your pets .
Or a quick look at your TVs to make sure you are paying that California Plasma tax .
After they see what they need , then they can get the warrant for a REAL search .
Maybe you have a cute daughter , I could show up at her favorite shop .
Cross reference with her Rx and know when she is on the peak of her cycle .
Look at credit card charges , give her some of her favorite treats .
Perhaps know when she rides her bicycle home from school and goes through that dark patch of woods .
Its a brave new world and Uncle Sam wants all your info available to anyone with hacker skills .</tokentext>
<sentencetext>All this Cell phone data should be in the public domain.
There should be no privacy.
Then my theft ring can predict when all your family are going to be out of the house.
Cross reference this with your Twitters and annual vacation travel and we can really clean you out.
Or perhaps the police just need to see if you have tags on all your pets.
Or a quick look at your TVs to make sure you are paying that California Plasma tax.
After they see what they need, then they can get the warrant for a REAL search.
Maybe you have a cute daughter, I could show up at her favorite shop.
Cross reference with her Rx and know when she is on the peak of her cycle.
Look at credit card charges, give her some of her favorite treats.
Perhaps know when she rides her bicycle home from school and goes through that dark patch of woods.
Its a brave new world and Uncle Sam wants all your info available to anyone with hacker skills.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270934</id>
	<title>Re:Traffic lights</title>
	<author>FlyingBishop</author>
	<datestamp>1267105860000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>They aren't programmed to get you where you're going quickly. They're programmed to slow you down so you don't run into someone (and no one runs into you.)</p></htmltext>
<tokenext>They are n't programmed to get you where you 're going quickly .
They 're programmed to slow you down so you do n't run into someone ( and no one runs into you .
)</tokentext>
<sentencetext>They aren't programmed to get you where you're going quickly.
They're programmed to slow you down so you don't run into someone (and no one runs into you.
)</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269126</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31276468</id>
	<title>Yeah. But not very exact.</title>
	<author>Hurricane78</author>
	<datestamp>1267130640000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>You&rsquo;re lucky when you get it down to 500 m.</p><p>Then again, when you&rsquo;re a A-GPS server provider...<nobr> <wbr></nobr>;)</p></htmltext>
<tokenext>You    re lucky when you get it down to 500 m.Then again , when you    re a A-GPS server provider... ; )</tokentext>
<sentencetext>You’re lucky when you get it down to 500 m.Then again, when you’re a A-GPS server provider... ;)</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269558</id>
	<title>Re:This was done last year</title>
	<author>Anonymous</author>
	<datestamp>1267131120000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Might want to check if there is a non-zero intersection between the groups of scientists you mention...</p><p><div class="quote"><p>Difference here was that the percentage was 85\%. Not the 93\% declared now.</p></div><p>The 93\% is an <b>upper bound</b> on the success of a <b>theoretical</b> prediction algorithm.  Makes sense that Eagle, et al,  implementing an actual prediction method, achieve 85\%.  RTFA?</p><p>Of course, it makes sense that people are very predictable.  The surprising result is that even people who travel hundreds of miles, all the time, are <b>just as predictable</b> as those that barely move around</p></div>
	</htmltext>
<tokenext>Might want to check if there is a non-zero intersection between the groups of scientists you mention...Difference here was that the percentage was 85 \ % .
Not the 93 \ % declared now.The 93 \ % is an upper bound on the success of a theoretical prediction algorithm .
Makes sense that Eagle , et al , implementing an actual prediction method , achieve 85 \ % .
RTFA ? Of course , it makes sense that people are very predictable .
The surprising result is that even people who travel hundreds of miles , all the time , are just as predictable as those that barely move around</tokentext>
<sentencetext>Might want to check if there is a non-zero intersection between the groups of scientists you mention...Difference here was that the percentage was 85\%.
Not the 93\% declared now.The 93\% is an upper bound on the success of a theoretical prediction algorithm.
Makes sense that Eagle, et al,  implementing an actual prediction method, achieve 85\%.
RTFA?Of course, it makes sense that people are very predictable.
The surprising result is that even people who travel hundreds of miles, all the time, are just as predictable as those that barely move around
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269200</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31273730</id>
	<title>No kidding</title>
	<author>nurb432</author>
	<datestamp>1267121040000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>People are creatures of habit thus predictable most of the time.</p><p>So where is my cut of the grant money?</p></htmltext>
<tokenext>People are creatures of habit thus predictable most of the time.So where is my cut of the grant money ?</tokentext>
<sentencetext>People are creatures of habit thus predictable most of the time.So where is my cut of the grant money?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269510</id>
	<title>Re:Uh huh...</title>
	<author>Anonymous</author>
	<datestamp>1267130580000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><div class="quote"><p>So you're saying that analyzing movement patterns allows you to predict movement patterns.  Would you like to guess the color of my red car?</p></div><p>Blue?</p></div>
	</htmltext>
<tokenext>So you 're saying that analyzing movement patterns allows you to predict movement patterns .
Would you like to guess the color of my red car ? Blue ?</tokentext>
<sentencetext>So you're saying that analyzing movement patterns allows you to predict movement patterns.
Would you like to guess the color of my red car?Blue?
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269270</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271134</id>
	<title>Re:Traffic lights</title>
	<author>Anonymous</author>
	<datestamp>1267107660000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Nope, they'll just use it to find who's speeding where and when and set speedtraps accordingly.</p></htmltext>
<tokenext>Nope , they 'll just use it to find who 's speeding where and when and set speedtraps accordingly .</tokentext>
<sentencetext>Nope, they'll just use it to find who's speeding where and when and set speedtraps accordingly.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269126</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269200</id>
	<title>This was done last year</title>
	<author>Kitkoan</author>
	<datestamp>1265139420000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>5</modscore>
	<htmltext><p>While not to the exactness of this study, this has been done before in May 2009 ( <a href="http://www.pbs.org/newshour/updates/science/jan-june09/celldata\_05-15.html" title="pbs.org" rel="nofollow">http://www.pbs.org/newshour/updates/science/jan-june09/celldata\_05-15.html</a> [pbs.org] ). From the article:</p><p><div class="quote"><p>analyzed six months of anonymous cell phone records from more than 100,000 people in a European country, obtained from a European cell phone provider. Those cell phone records gave an approximation of each person's location at the time of each call, because cell phone calls are routed through the nearest cell tower.

He and his colleagues found that people tend not to stray far -- almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months, and nearly half the people rarely strayed outside a six-mile circle. They also tended to go back and forth regularly between only a few locations, such as home and work.</p></div><p>And another attempt on the same idea was done by MIT in July 2005 ( <a href="http://yro.slashdot.org/article.pl?sid=05/07/25/1751234" title="slashdot.org" rel="nofollow">http://yro.slashdot.org/article.pl?sid=05/07/25/1751234</a> [slashdot.org] ). Difference here was that the percentage was 85\%. Not the 93\% declared now. From the Wired article:</p><p><div class="quote"><p>Eagle's Reality Mining project logged 350,000 hours of data over nine months about the location, proximity, activity and communication of volunteers, and was quickly able to guess whether two people were friends or just co-workers.... Given enough data, Eagle's algorithms were able to predict what people -- especially professors and Media Lab employees -- would do next and be right up to 85 percent of the time.... Eagle used Bluetooth-enabled Nokia 6600 smartphones running custom programs that logged cell-tower information to record the phones' locations. Every five minutes, the phones also scanned the immediate vicinity for other participating phones. Using data gleaned from cell-phone towers and calling information, the system is able to predict, for example, whether someone will go out for the evening based on the volume of calls they made to friends.</p></div></div>
	</htmltext>
<tokenext>While not to the exactness of this study , this has been done before in May 2009 ( http : //www.pbs.org/newshour/updates/science/jan-june09/celldata \ _05-15.html [ pbs.org ] ) .
From the article : analyzed six months of anonymous cell phone records from more than 100,000 people in a European country , obtained from a European cell phone provider .
Those cell phone records gave an approximation of each person 's location at the time of each call , because cell phone calls are routed through the nearest cell tower .
He and his colleagues found that people tend not to stray far -- almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months , and nearly half the people rarely strayed outside a six-mile circle .
They also tended to go back and forth regularly between only a few locations , such as home and work.And another attempt on the same idea was done by MIT in July 2005 ( http : //yro.slashdot.org/article.pl ? sid = 05/07/25/1751234 [ slashdot.org ] ) .
Difference here was that the percentage was 85 \ % .
Not the 93 \ % declared now .
From the Wired article : Eagle 's Reality Mining project logged 350,000 hours of data over nine months about the location , proximity , activity and communication of volunteers , and was quickly able to guess whether two people were friends or just co-workers.... Given enough data , Eagle 's algorithms were able to predict what people -- especially professors and Media Lab employees -- would do next and be right up to 85 percent of the time.... Eagle used Bluetooth-enabled Nokia 6600 smartphones running custom programs that logged cell-tower information to record the phones ' locations .
Every five minutes , the phones also scanned the immediate vicinity for other participating phones .
Using data gleaned from cell-phone towers and calling information , the system is able to predict , for example , whether someone will go out for the evening based on the volume of calls they made to friends .</tokentext>
<sentencetext>While not to the exactness of this study, this has been done before in May 2009 ( http://www.pbs.org/newshour/updates/science/jan-june09/celldata\_05-15.html [pbs.org] ).
From the article:analyzed six months of anonymous cell phone records from more than 100,000 people in a European country, obtained from a European cell phone provider.
Those cell phone records gave an approximation of each person's location at the time of each call, because cell phone calls are routed through the nearest cell tower.
He and his colleagues found that people tend not to stray far -- almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months, and nearly half the people rarely strayed outside a six-mile circle.
They also tended to go back and forth regularly between only a few locations, such as home and work.And another attempt on the same idea was done by MIT in July 2005 ( http://yro.slashdot.org/article.pl?sid=05/07/25/1751234 [slashdot.org] ).
Difference here was that the percentage was 85\%.
Not the 93\% declared now.
From the Wired article:Eagle's Reality Mining project logged 350,000 hours of data over nine months about the location, proximity, activity and communication of volunteers, and was quickly able to guess whether two people were friends or just co-workers.... Given enough data, Eagle's algorithms were able to predict what people -- especially professors and Media Lab employees -- would do next and be right up to 85 percent of the time.... Eagle used Bluetooth-enabled Nokia 6600 smartphones running custom programs that logged cell-tower information to record the phones' locations.
Every five minutes, the phones also scanned the immediate vicinity for other participating phones.
Using data gleaned from cell-phone towers and calling information, the system is able to predict, for example, whether someone will go out for the evening based on the volume of calls they made to friends.
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271810</id>
	<title>Well seeing as how....</title>
	<author>g0bshiTe</author>
	<datestamp>1267111740000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>"predict human movement patterns and location up to 93 percent of the time."<br> <br>
I am either at work or at home, thank you very much for the study Dr. Obvious.</htmltext>
<tokenext>" predict human movement patterns and location up to 93 percent of the time .
" I am either at work or at home , thank you very much for the study Dr. Obvious .</tokentext>
<sentencetext>"predict human movement patterns and location up to 93 percent of the time.
" 
I am either at work or at home, thank you very much for the study Dr. Obvious.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269126</id>
	<title>Traffic lights</title>
	<author>Anonymous</author>
	<datestamp>1265138880000</datestamp>
	<modclass>Funny</modclass>
	<modscore>4</modscore>
	<htmltext><p>Hopefully they find a way to program those (!@#) traffic lights a little better with this!</p></htmltext>
<tokenext>Hopefully they find a way to program those ( !
@ # ) traffic lights a little better with this !</tokentext>
<sentencetext>Hopefully they find a way to program those (!
@#) traffic lights a little better with this!</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269612</id>
	<title>upper bound</title>
	<author>Anonymous</author>
	<datestamp>1267088760000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>3</modscore>
	<htmltext>This seems to be the upper bound of predictability by computers ; in other domains of artificial intelligence, such as automatic translation or speech recognition, automated statistical analysis from corpuses seems to perform better than manual encoding of rules, but ends up at maybe 90\% efficiency. The rest is too random to be predicted, and it could be the part of poetry, art  or intelligence in our lives.</htmltext>
<tokenext>This seems to be the upper bound of predictability by computers ; in other domains of artificial intelligence , such as automatic translation or speech recognition , automated statistical analysis from corpuses seems to perform better than manual encoding of rules , but ends up at maybe 90 \ % efficiency .
The rest is too random to be predicted , and it could be the part of poetry , art or intelligence in our lives .</tokentext>
<sentencetext>This seems to be the upper bound of predictability by computers ; in other domains of artificial intelligence, such as automatic translation or speech recognition, automated statistical analysis from corpuses seems to perform better than manual encoding of rules, but ends up at maybe 90\% efficiency.
The rest is too random to be predicted, and it could be the part of poetry, art  or intelligence in our lives.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31280380</id>
	<title>These Slides Are More Interesting</title>
	<author>Anonymous</author>
	<datestamp>1267107540000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Download them while you can and spread the word</p><p> <a href="http://www.public.asu.edu/~huanliu/sbp08/program.html" title="asu.edu" rel="nofollow">http://www.public.asu.edu/~huanliu/sbp08/program.html</a> [asu.edu] </p><p>Papers:</p><p>
    Integrating Multi-Agent Technology with Cognitive Modeling to Develop an Insurgency Information Framework (IIF)<br>
    LeeRoy Bronner and Akeila Richards (Slides)</p><p>
    Stochastic Opponent Modeling  Agents: A Case Study with Hezbollah<br>
    Aaron Mannes, Mary Michael, Amy Pate, Amy Sliva, V.S. Subrahmanian and Jonathan Wilkenfeld (Slides)</p><p>
    An approach to modeling group behaviors and beliefs in conflict situations<br>
    Norman Geddes and Michele Atkinson (Slides)</p><p>
    Computational Models of Multi-national Organizations<br>
    Alexander Levis, Smriti K. Kansal, A. Erkin Olmez and Ashraf M. AbuSharekh (Slides)</p><p>
    Clustering of Trajectory Data obtained from Soccer Game Record -A First Step to Behavioral Modeling<br>
    Shoji Hirano and Shusaku Tsumoto (Slides)</p><p>
    Inferring Social Network Structure using Mobile Phone Data<br>
    Nathan Eagle, Alex (Sandy) Pentland, and David Lazer (Slides)</p><p>
    Human Behavioral Modeling Using Fuzzy and Dempster-Shafer Theory<br>
    Ronald Yager (Slides)</p><p>
    Behavior Profiling for Computer Security Applications<br>
    David Robinson, George Cybenko, and Vincent Berk (Slides)</p><p>
    Mining for Social Process Signatures in Intelligence Data Streams<br>
    Robert Savell and George Cybenko (Slides)</p><p>
    An Ant Colony Optimization Approach to Expert Identification in Social Networks<br>
    Muhammad Ahmad and Jaideep Srivastava (Slides)</p><p>
    Modeling and Supporting Common Ground in Geo-Collaboration<br>
    Gregorio Convertino, Anna Wu, Craig H. Ganoe, Luke (Xiaolong) Zhang, and John M. Carroll (Slides)</p><p>
    Modeling Malaysian Public Opinion by Mining the Malaysian Blogosphere<br>
    Brian Ulicny (Slides)</p><p>
    Reading between the Lines: Human-centered Classification of Communication Patterns and Intentions<br>
    Daniela Stokar von Neuforn and Katrin Franke (Slides)</p><p>
    Automating Frame Analysis<br>
    Antonio Sanfilippo, Lyndsey Franklin, Stephen Tratz, Gary Danielson, Nicholas Mileson, Roderick Riensche and Liam McGrath (Slides)</p><p>
    Metagame Strategies of Nation-States, with Application to Cross-Strait Relations<br>
    Alex Chavez and Jun Zhang (Slides)</p><p>
    Using Topic Analysis to Compute Identity Group Attributes<br>
    Lashon Booker and Gary Strong</p><p>Posters:</p><p>
    Conceptualizing Trustworthiness Mechanisms for Countering Insider Threats<br>
    Shuyuan Mary Ho (Slides)</p><p>
    Particle Swarm Social Model for Group Social Learning in Adaptive Environment<br>
    Xiaohui Cui, Laura Pullum, Jim Treadwell, Robert Patton and Thomas Potok (Slides)</p><p>
    Social Network Analysis: Tasks and Tools<br>
    Steven Loscalzo and Lei Yu (Slides)</p><p>
    Behavioral Entropy of a Cellular Phone User<br>
    Ram Dantu, Santi Phithakkitnukoon, and Husain Husna (Slides)</p><p>
    Community Detection in a Large Real</p></htmltext>
<tokenext>Download them while you can and spread the word http : //www.public.asu.edu/ ~ huanliu/sbp08/program.html [ asu.edu ] Papers : Integrating Multi-Agent Technology with Cognitive Modeling to Develop an Insurgency Information Framework ( IIF ) LeeRoy Bronner and Akeila Richards ( Slides ) Stochastic Opponent Modeling Agents : A Case Study with Hezbollah Aaron Mannes , Mary Michael , Amy Pate , Amy Sliva , V.S .
Subrahmanian and Jonathan Wilkenfeld ( Slides ) An approach to modeling group behaviors and beliefs in conflict situations Norman Geddes and Michele Atkinson ( Slides ) Computational Models of Multi-national Organizations Alexander Levis , Smriti K. Kansal , A. Erkin Olmez and Ashraf M. AbuSharekh ( Slides ) Clustering of Trajectory Data obtained from Soccer Game Record -A First Step to Behavioral Modeling Shoji Hirano and Shusaku Tsumoto ( Slides ) Inferring Social Network Structure using Mobile Phone Data Nathan Eagle , Alex ( Sandy ) Pentland , and David Lazer ( Slides ) Human Behavioral Modeling Using Fuzzy and Dempster-Shafer Theory Ronald Yager ( Slides ) Behavior Profiling for Computer Security Applications David Robinson , George Cybenko , and Vincent Berk ( Slides ) Mining for Social Process Signatures in Intelligence Data Streams Robert Savell and George Cybenko ( Slides ) An Ant Colony Optimization Approach to Expert Identification in Social Networks Muhammad Ahmad and Jaideep Srivastava ( Slides ) Modeling and Supporting Common Ground in Geo-Collaboration Gregorio Convertino , Anna Wu , Craig H. Ganoe , Luke ( Xiaolong ) Zhang , and John M. Carroll ( Slides ) Modeling Malaysian Public Opinion by Mining the Malaysian Blogosphere Brian Ulicny ( Slides ) Reading between the Lines : Human-centered Classification of Communication Patterns and Intentions Daniela Stokar von Neuforn and Katrin Franke ( Slides ) Automating Frame Analysis Antonio Sanfilippo , Lyndsey Franklin , Stephen Tratz , Gary Danielson , Nicholas Mileson , Roderick Riensche and Liam McGrath ( Slides ) Metagame Strategies of Nation-States , with Application to Cross-Strait Relations Alex Chavez and Jun Zhang ( Slides ) Using Topic Analysis to Compute Identity Group Attributes Lashon Booker and Gary StrongPosters : Conceptualizing Trustworthiness Mechanisms for Countering Insider Threats Shuyuan Mary Ho ( Slides ) Particle Swarm Social Model for Group Social Learning in Adaptive Environment Xiaohui Cui , Laura Pullum , Jim Treadwell , Robert Patton and Thomas Potok ( Slides ) Social Network Analysis : Tasks and Tools Steven Loscalzo and Lei Yu ( Slides ) Behavioral Entropy of a Cellular Phone User Ram Dantu , Santi Phithakkitnukoon , and Husain Husna ( Slides ) Community Detection in a Large Real</tokentext>
<sentencetext>Download them while you can and spread the word http://www.public.asu.edu/~huanliu/sbp08/program.html [asu.edu] Papers:
    Integrating Multi-Agent Technology with Cognitive Modeling to Develop an Insurgency Information Framework (IIF)
    LeeRoy Bronner and Akeila Richards (Slides)
    Stochastic Opponent Modeling  Agents: A Case Study with Hezbollah
    Aaron Mannes, Mary Michael, Amy Pate, Amy Sliva, V.S.
Subrahmanian and Jonathan Wilkenfeld (Slides)
    An approach to modeling group behaviors and beliefs in conflict situations
    Norman Geddes and Michele Atkinson (Slides)
    Computational Models of Multi-national Organizations
    Alexander Levis, Smriti K. Kansal, A. Erkin Olmez and Ashraf M. AbuSharekh (Slides)
    Clustering of Trajectory Data obtained from Soccer Game Record -A First Step to Behavioral Modeling
    Shoji Hirano and Shusaku Tsumoto (Slides)
    Inferring Social Network Structure using Mobile Phone Data
    Nathan Eagle, Alex (Sandy) Pentland, and David Lazer (Slides)
    Human Behavioral Modeling Using Fuzzy and Dempster-Shafer Theory
    Ronald Yager (Slides)
    Behavior Profiling for Computer Security Applications
    David Robinson, George Cybenko, and Vincent Berk (Slides)
    Mining for Social Process Signatures in Intelligence Data Streams
    Robert Savell and George Cybenko (Slides)
    An Ant Colony Optimization Approach to Expert Identification in Social Networks
    Muhammad Ahmad and Jaideep Srivastava (Slides)
    Modeling and Supporting Common Ground in Geo-Collaboration
    Gregorio Convertino, Anna Wu, Craig H. Ganoe, Luke (Xiaolong) Zhang, and John M. Carroll (Slides)
    Modeling Malaysian Public Opinion by Mining the Malaysian Blogosphere
    Brian Ulicny (Slides)
    Reading between the Lines: Human-centered Classification of Communication Patterns and Intentions
    Daniela Stokar von Neuforn and Katrin Franke (Slides)
    Automating Frame Analysis
    Antonio Sanfilippo, Lyndsey Franklin, Stephen Tratz, Gary Danielson, Nicholas Mileson, Roderick Riensche and Liam McGrath (Slides)
    Metagame Strategies of Nation-States, with Application to Cross-Strait Relations
    Alex Chavez and Jun Zhang (Slides)
    Using Topic Analysis to Compute Identity Group Attributes
    Lashon Booker and Gary StrongPosters:
    Conceptualizing Trustworthiness Mechanisms for Countering Insider Threats
    Shuyuan Mary Ho (Slides)
    Particle Swarm Social Model for Group Social Learning in Adaptive Environment
    Xiaohui Cui, Laura Pullum, Jim Treadwell, Robert Patton and Thomas Potok (Slides)
    Social Network Analysis: Tasks and Tools
    Steven Loscalzo and Lei Yu (Slides)
    Behavioral Entropy of a Cellular Phone User
    Ram Dantu, Santi Phithakkitnukoon, and Husain Husna (Slides)
    Community Detection in a Large Real</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269802</id>
	<title>Wow slashdot...</title>
	<author>aceofspades1217</author>
	<datestamp>1267091280000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>This is a really old arstechnica article. Wasn't this article from last week?</p><p>Arstechnica is a great site, although they do tend to get carried away with their stories. I really read arstechnica when I'm in a boring class and need to pass time because their articles are so ridiculously long.</p></htmltext>
<tokenext>This is a really old arstechnica article .
Was n't this article from last week ? Arstechnica is a great site , although they do tend to get carried away with their stories .
I really read arstechnica when I 'm in a boring class and need to pass time because their articles are so ridiculously long .</tokentext>
<sentencetext>This is a really old arstechnica article.
Wasn't this article from last week?Arstechnica is a great site, although they do tend to get carried away with their stories.
I really read arstechnica when I'm in a boring class and need to pass time because their articles are so ridiculously long.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270952</id>
	<title>Re:Uh huh...</title>
	<author>KlaymenDK</author>
	<datestamp>1267106100000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>"If I can guess the real color of your car, can I have my dog back?"</p></htmltext>
<tokenext>" If I can guess the real color of your car , can I have my dog back ?
"</tokentext>
<sentencetext>"If I can guess the real color of your car, can I have my dog back?
"</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269270</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270788</id>
	<title>it's not 1984 yet</title>
	<author>CiderJack</author>
	<datestamp>1267104300000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>What about the user who carries the phone from home to work and back, and never carries it anywhere else? Can they predict that movement? How about those of us who don't use a cellphone to begin with? I'm a web designer and a landline serves my needs just fine, thankyou. Track me, I wish you luck<nobr> <wbr></nobr>;)

Sheesh what a useless study,</htmltext>
<tokenext>What about the user who carries the phone from home to work and back , and never carries it anywhere else ?
Can they predict that movement ?
How about those of us who do n't use a cellphone to begin with ?
I 'm a web designer and a landline serves my needs just fine , thankyou .
Track me , I wish you luck ; ) Sheesh what a useless study,</tokentext>
<sentencetext>What about the user who carries the phone from home to work and back, and never carries it anywhere else?
Can they predict that movement?
How about those of us who don't use a cellphone to begin with?
I'm a web designer and a landline serves my needs just fine, thankyou.
Track me, I wish you luck ;)

Sheesh what a useless study,</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269194</id>
	<title>Really a surprise?</title>
	<author>Xgamer4</author>
	<datestamp>1265139420000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext>Is this really a surprise to anyone? I'd wager the day for the vast majority of people goes something like "wake up, work/school, home, sleep", with the removal of work on Saturdays and Sundays and the possible addition of church or something on Sundays. It's not really that hard to predict something that consistent.</htmltext>
<tokenext>Is this really a surprise to anyone ?
I 'd wager the day for the vast majority of people goes something like " wake up , work/school , home , sleep " , with the removal of work on Saturdays and Sundays and the possible addition of church or something on Sundays .
It 's not really that hard to predict something that consistent .</tokentext>
<sentencetext>Is this really a surprise to anyone?
I'd wager the day for the vast majority of people goes something like "wake up, work/school, home, sleep", with the removal of work on Saturdays and Sundays and the possible addition of church or something on Sundays.
It's not really that hard to predict something that consistent.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269370</id>
	<title>Re:Traffic lights</title>
	<author>Anonymous</author>
	<datestamp>1267128780000</datestamp>
	<modclass>Funny</modclass>
	<modscore>1</modscore>
	<htmltext><p>I'm <a href="http://xkcd.com/277/" title="xkcd.com" rel="nofollow">a civil engineer</a> [xkcd.com], you insensitive clod!</p></htmltext>
<tokenext>I 'm a civil engineer [ xkcd.com ] , you insensitive clod !</tokentext>
<sentencetext>I'm a civil engineer [xkcd.com], you insensitive clod!</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269126</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269406</id>
	<title>Re:Uh huh...</title>
	<author>uolamer</author>
	<datestamp>1267129260000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><i>Would you like to guess the color of my red car?</i> <br>
My algorithm says<br>
<br>
94.1\% red<br>
2.6\% white<br>
1.7\% blue<br>
1.2\% black<br>
0.4\% Nader</htmltext>
<tokenext>Would you like to guess the color of my red car ?
My algorithm says 94.1 \ % red 2.6 \ % white 1.7 \ % blue 1.2 \ % black 0.4 \ % Nader</tokentext>
<sentencetext>Would you like to guess the color of my red car?
My algorithm says

94.1\% red
2.6\% white
1.7\% blue
1.2\% black
0.4\% Nader</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269270</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269564</id>
	<title>Great idea for an app.</title>
	<author>ipquickly</author>
	<datestamp>1267131180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>It seems like you haven't used the WC for any extended period in two days.<br>Your local grocery store now has wholebran cereal on sale.<br>Just mention this ad and we will knock an extra 2 dollars of the already low price.<br>Coupon code: knst1patd</p></htmltext>
<tokenext>It seems like you have n't used the WC for any extended period in two days.Your local grocery store now has wholebran cereal on sale.Just mention this ad and we will knock an extra 2 dollars of the already low price.Coupon code : knst1patd</tokentext>
<sentencetext>It seems like you haven't used the WC for any extended period in two days.Your local grocery store now has wholebran cereal on sale.Just mention this ad and we will knock an extra 2 dollars of the already low price.Coupon code: knst1patd</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269488</id>
	<title>Published in Science!!?????</title>
	<author>viraltus</author>
	<datestamp>1267130340000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Sounds like a High School science project from a lame student; how are these results relevant or interesting at all?</p></htmltext>
<tokenext>Sounds like a High School science project from a lame student ; how are these results relevant or interesting at all ?</tokentext>
<sentencetext>Sounds like a High School science project from a lame student; how are these results relevant or interesting at all?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31273278</id>
	<title>Duuuh</title>
	<author>strangeattraction</author>
	<datestamp>1267119540000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>I'm at work or at home at least \%93 percent of the time.</htmltext>
<tokenext>I 'm at work or at home at least \ % 93 percent of the time .</tokentext>
<sentencetext>I'm at work or at home at least \%93 percent of the time.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269946</id>
	<title>Re:Sleep and Work?</title>
	<author>caffeinemessiah</author>
	<datestamp>1267093620000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>2</modscore>
	<htmltext><p><div class="quote"><p>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.</p></div><p>
You're not too far off. I worked at the research wing of a phone company, and I can tell you that "tracking" a person using a cell tower is pretty coarse, even in urban areas. Given that most people go to work on weekdays, I'd say that a lot of your "movement" could be predicted on this level by just predicting your average movement. Add in a weekday/weekend variation, and 93\% is hardly surprising.
</p><p>
This isn't even one of those "well duh, in RETROSPECT everything is obvious" studies -- anyone who has ever worked with CDR (mobile phone) data knows that this is pretty obvious even before running the experiments.
</p><p>
And for the people who bring up the MIT Reality Mining experiment, keep in mind that they tracked about 100 *individuals*, all of whom were MIT students with pretty regular routines.</p></div>
	</htmltext>
<tokenext>Seeing how 66.67 \ % of the time I am either sleeping at home or at work it should n't be too hard to fill the other 27 \ % with commute/grocery shopping .
You 're not too far off .
I worked at the research wing of a phone company , and I can tell you that " tracking " a person using a cell tower is pretty coarse , even in urban areas .
Given that most people go to work on weekdays , I 'd say that a lot of your " movement " could be predicted on this level by just predicting your average movement .
Add in a weekday/weekend variation , and 93 \ % is hardly surprising .
This is n't even one of those " well duh , in RETROSPECT everything is obvious " studies -- anyone who has ever worked with CDR ( mobile phone ) data knows that this is pretty obvious even before running the experiments .
And for the people who bring up the MIT Reality Mining experiment , keep in mind that they tracked about 100 * individuals * , all of whom were MIT students with pretty regular routines .</tokentext>
<sentencetext>Seeing how 66.67\% of the time I am either sleeping at home or at work it shouldn't be too hard to fill the other 27\% with commute/grocery shopping.
You're not too far off.
I worked at the research wing of a phone company, and I can tell you that "tracking" a person using a cell tower is pretty coarse, even in urban areas.
Given that most people go to work on weekdays, I'd say that a lot of your "movement" could be predicted on this level by just predicting your average movement.
Add in a weekday/weekend variation, and 93\% is hardly surprising.
This isn't even one of those "well duh, in RETROSPECT everything is obvious" studies -- anyone who has ever worked with CDR (mobile phone) data knows that this is pretty obvious even before running the experiments.
And for the people who bring up the MIT Reality Mining experiment, keep in mind that they tracked about 100 *individuals*, all of whom were MIT students with pretty regular routines.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269308</id>
	<title>Re:Wow!</title>
	<author>wealthychef</author>
	<datestamp>1265140740000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Exactly.  By definition, a pattern is 100\% predictable by looking at its history.  Why is this news?  Hell no I'm not going to RTFA.</htmltext>
<tokenext>Exactly .
By definition , a pattern is 100 \ % predictable by looking at its history .
Why is this news ?
Hell no I 'm not going to RTFA .</tokentext>
<sentencetext>Exactly.
By definition, a pattern is 100\% predictable by looking at its history.
Why is this news?
Hell no I'm not going to RTFA.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269186</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270718</id>
	<title>it's not 1984 yet</title>
	<author>Anonymous</author>
	<datestamp>1267103640000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>I don't have a cellphone. How predictable are humans without cellphones? I might do something unexpected, I hope I don't get arrested for being unable to be profiled! Oh wait, it's not 1984 yet, that's just the future phobia...<br>(btw, my captcha was 'escapes')</p></htmltext>
<tokenext>I do n't have a cellphone .
How predictable are humans without cellphones ?
I might do something unexpected , I hope I do n't get arrested for being unable to be profiled !
Oh wait , it 's not 1984 yet , that 's just the future phobia... ( btw , my captcha was 'escapes ' )</tokentext>
<sentencetext>I don't have a cellphone.
How predictable are humans without cellphones?
I might do something unexpected, I hope I don't get arrested for being unable to be profiled!
Oh wait, it's not 1984 yet, that's just the future phobia...(btw, my captcha was 'escapes')</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269702</id>
	<title>Re:This was done last year</title>
	<author>MichaelSmith</author>
	<datestamp>1267090080000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months, and nearly half the people rarely strayed outside a six-mile circle.</p></div><p>Reading that I had this eerie recollection of radio tagging studies on animals, and how they seem to follow patterns similar to this.</p></div>
	</htmltext>
<tokenext>almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months , and nearly half the people rarely strayed outside a six-mile circle.Reading that I had this eerie recollection of radio tagging studies on animals , and how they seem to follow patterns similar to this .</tokentext>
<sentencetext>almost three quarters of the people stayed mainly within about a 20-mile circle for the entire six months, and nearly half the people rarely strayed outside a six-mile circle.Reading that I had this eerie recollection of radio tagging studies on animals, and how they seem to follow patterns similar to this.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269200</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269866</id>
	<title>Re:Wow!</title>
	<author>pipingguy</author>
	<datestamp>1267092240000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Yeah, I thought that was amusing also.</htmltext>
<tokenext>Yeah , I thought that was amusing also .</tokentext>
<sentencetext>Yeah, I thought that was amusing also.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269186</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269332</id>
	<title>Re:Wow!</title>
	<author>Anonymous</author>
	<datestamp>1267128120000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>I once predicted the movements of an ant colony by drawing thousands of random paths on a paper, it was pretty accurate, the same thing could probably be done with human colonies.</p></htmltext>
<tokenext>I once predicted the movements of an ant colony by drawing thousands of random paths on a paper , it was pretty accurate , the same thing could probably be done with human colonies .</tokentext>
<sentencetext>I once predicted the movements of an ant colony by drawing thousands of random paths on a paper, it was pretty accurate, the same thing could probably be done with human colonies.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269186</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270402</id>
	<title>Re:Uh huh...</title>
	<author>Mjec</author>
	<datestamp>1267099560000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p><div class="quote"><p>So you're saying that analyzing movement patterns allows you to predict movement patterns.  Would you like to guess the color of my red car?</p></div><p>Blue?</p></div><p>You should <a href="http://en.wikipedia.org/wiki/Doppler\_effect" title="wikipedia.org" rel="nofollow">slow down</a> [wikipedia.org].</p></div>
	</htmltext>
<tokenext>So you 're saying that analyzing movement patterns allows you to predict movement patterns .
Would you like to guess the color of my red car ? Blue ? You should slow down [ wikipedia.org ] .</tokentext>
<sentencetext>So you're saying that analyzing movement patterns allows you to predict movement patterns.
Would you like to guess the color of my red car?Blue?You should slow down [wikipedia.org].
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269510</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270224</id>
	<title>How about the other ones ?</title>
	<author>BESTouff</author>
	<datestamp>1267096980000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>it may be possible to predict human movement patterns and location up to 93 percent of the time.</p></div><p>The remaining 7\% are deviant, they're probably onto some terrorist task of some sort and should be Guantanamoed.</p></div>
	</htmltext>
<tokenext>it may be possible to predict human movement patterns and location up to 93 percent of the time.The remaining 7 \ % are deviant , they 're probably onto some terrorist task of some sort and should be Guantanamoed .</tokentext>
<sentencetext>it may be possible to predict human movement patterns and location up to 93 percent of the time.The remaining 7\% are deviant, they're probably onto some terrorist task of some sort and should be Guantanamoed.
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269502</id>
	<title>Re:Sleep and Work?</title>
	<author>kerrbear</author>
	<datestamp>1267130520000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext><p>If they track my wife's phone, they'll notice she spends 93\% of the time in her desk drawer. Why the heck do I pay for her phone when she never has it on her!?</p></htmltext>
<tokenext>If they track my wife 's phone , they 'll notice she spends 93 \ % of the time in her desk drawer .
Why the heck do I pay for her phone when she never has it on her !
?</tokentext>
<sentencetext>If they track my wife's phone, they'll notice she spends 93\% of the time in her desk drawer.
Why the heck do I pay for her phone when she never has it on her!
?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31270982</id>
	<title>Not really impressive</title>
	<author>Anonymous</author>
	<datestamp>1267106340000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>98\% of people will go to work in the morning and come home from work in the afternoon. Assuming there are no detours they will take the same route every day. When they aren't working 72\% of people will go to the grocery store once per week, and 27\% of people will make up statistics to seem important.</p></htmltext>
<tokenext>98 \ % of people will go to work in the morning and come home from work in the afternoon .
Assuming there are no detours they will take the same route every day .
When they are n't working 72 \ % of people will go to the grocery store once per week , and 27 \ % of people will make up statistics to seem important .</tokentext>
<sentencetext>98\% of people will go to work in the morning and come home from work in the afternoon.
Assuming there are no detours they will take the same route every day.
When they aren't working 72\% of people will go to the grocery store once per week, and 27\% of people will make up statistics to seem important.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269576</id>
	<title>Re:Privacy, anyone?</title>
	<author>polar red</author>
	<datestamp>1267131480000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Carrying a device that shouts(electromechanically) very loud your position, would destroy any notion of you caring for privacy I could guess. On the other hand : would those researchers REALLY care about your activities ?</p></htmltext>
<tokenext>Carrying a device that shouts ( electromechanically ) very loud your position , would destroy any notion of you caring for privacy I could guess .
On the other hand : would those researchers REALLY care about your activities ?</tokentext>
<sentencetext>Carrying a device that shouts(electromechanically) very loud your position, would destroy any notion of you caring for privacy I could guess.
On the other hand : would those researchers REALLY care about your activities ?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269364</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31272680</id>
	<title>metaman.tumblr.com</title>
	<author>Anonymous</author>
	<datestamp>1267116600000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>There is a new science evolving around understanding objectively humans and their behaviour.</p><p>As the century of the Self was driven by Freud this century will be driven by quantifying the self and even the culture.<br>Albert-Laszlo Barabasi the leader of this domain says we should get used with the idea that we are "dreaming robots on autopilot" nothing more.</p></htmltext>
<tokenext>There is a new science evolving around understanding objectively humans and their behaviour.As the century of the Self was driven by Freud this century will be driven by quantifying the self and even the culture.Albert-Laszlo Barabasi the leader of this domain says we should get used with the idea that we are " dreaming robots on autopilot " nothing more .</tokentext>
<sentencetext>There is a new science evolving around understanding objectively humans and their behaviour.As the century of the Self was driven by Freud this century will be driven by quantifying the self and even the culture.Albert-Laszlo Barabasi the leader of this domain says we should get used with the idea that we are "dreaming robots on autopilot" nothing more.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269822</id>
	<title>Re:Sleep and Work?</title>
	<author>Jedi Alec</author>
	<datestamp>1267091460000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Any tips for the rest of us on how you get away with sleeping at work? I'd love to get 8 more hours a day to spend for myself...</p><p>And 66\%...you work 7*8?</p></htmltext>
<tokenext>Any tips for the rest of us on how you get away with sleeping at work ?
I 'd love to get 8 more hours a day to spend for myself...And 66 \ % ...you work 7 * 8 ?</tokentext>
<sentencetext>Any tips for the rest of us on how you get away with sleeping at work?
I'd love to get 8 more hours a day to spend for myself...And 66\%...you work 7*8?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269252</id>
	<title>Re:Sleep and Work?</title>
	<author>Jeff321</author>
	<datestamp>1265140020000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I think 93\% of the time I'm at home or work.</p></htmltext>
<tokenext>I think 93 \ % of the time I 'm at home or work .</tokentext>
<sentencetext>I think 93\% of the time I'm at home or work.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269148</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31269364</id>
	<title>Privacy, anyone?</title>
	<author>Bjecas</author>
	<datestamp>1267128660000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Am I the only one concerned at random researchers keeping track of where I am, where I went and where I'll probably go? I'm not ok with some people *I know* knowing my schedule, let alone random people.
</p><p>
I see no valid reasoning for this study to intrude in privacy like this, since from the get-go it didn't aspire to answer any meaningful question: proving that you're able to ascertain someone's schedule from their phone calls seems like a very sordid thing to prove.</p></htmltext>
<tokenext>Am I the only one concerned at random researchers keeping track of where I am , where I went and where I 'll probably go ?
I 'm not ok with some people * I know * knowing my schedule , let alone random people .
I see no valid reasoning for this study to intrude in privacy like this , since from the get-go it did n't aspire to answer any meaningful question : proving that you 're able to ascertain someone 's schedule from their phone calls seems like a very sordid thing to prove .</tokentext>
<sentencetext>Am I the only one concerned at random researchers keeping track of where I am, where I went and where I'll probably go?
I'm not ok with some people *I know* knowing my schedule, let alone random people.
I see no valid reasoning for this study to intrude in privacy like this, since from the get-go it didn't aspire to answer any meaningful question: proving that you're able to ascertain someone's schedule from their phone calls seems like a very sordid thing to prove.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_02_24_2343219.31271286</id>
	<title>Re:Wow!</title>
	<author>Idbar</author>
	<datestamp>1267108740000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>Imagine that. If you study someone's daily routine you can "predict" where they will go. Call me shocked.</p></div><p>Hey Shocked!
<br>Oh.. research... Spending so much time predicting where people is going, when you can just <a href="http://yro.slashdot.org/story/10/02/18/1340202/I-Use-Twitter-Please-Rob-Me" title="slashdot.org">check on-line</a> [slashdot.org]<br> <br>
What worries me the most is knowing that they DO keep track of the people. I don't care if they are able to predict where I'm going, I may change that. But they studying my patterns seems scary.</p></div>
	</htmltext>
<tokenext>Imagine that .
If you study someone 's daily routine you can " predict " where they will go .
Call me shocked.Hey Shocked !
Oh.. research... Spending so much time predicting where people is going , when you can just check on-line [ slashdot.org ] What worries me the most is knowing that they DO keep track of the people .
I do n't care if they are able to predict where I 'm going , I may change that .
But they studying my patterns seems scary .</tokentext>
<sentencetext>Imagine that.
If you study someone's daily routine you can "predict" where they will go.
Call me shocked.Hey Shocked!
Oh.. research... Spending so much time predicting where people is going, when you can just check on-line [slashdot.org] 
What worries me the most is knowing that they DO keep track of the people.
I don't care if they are able to predict where I'm going, I may change that.
But they studying my patterns seems scary.
	</sentencetext>
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