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- Home
- Glass Cloth (2-Pack)
Product DescriptionWe Also Recommend
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Dr. Beasley's Glass Cloth is designed with you in mind. It's made to quickly and effectively assist you in removing smudges, oils, and other obstructions from the glass that you depend on while driving. While other cloths may leave lint behind, Dr. Beasley's Glass Cloth guarantees a clear and streak-free view through your windows. The Glass Cloth isn't detail specific by any means; it's a great tool to keep around while your driving to make sure you've got a good range of vision whenever you're behind the wheel.
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Wetland Protections Worry Farmers In New Jersey
"The work in progress Highlands Preservation acts 'Master Plan' is designed to protect nearly a million acres in New Jersey from development."
"2 years ago the New Jersey government passed the act in large part to curb urban sprawl and protect the 115 billion gallons of clean drinking water the highlands provide, used by more than 15 million people in the region."
"Aside from severely limiting development opportunities Farmer's are upset that the plan can limit what they're able to do on their land, such as timber and cut fire wood."
"The big fear for farmers is that their land is worthless, but the Council says there is a preservation fund that will pay them the assessed value of their land before the study was done."

subset: commoncrawl


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Hey. I was just taking a shower and then this question popped in my head. I guess I'm a little like Spencer. I get ideas when I'm wet. Ha-ha. You probably didn't need to know that. Anyway. I think I read a comment saying that when Sam was getting replaced by Missy from iReunite with Missy
Carly didn't believe that Sam said Missy is out to get Sam. Well I think Carly and Sam was friends longer than Carly and Missy I think. Sam went to Freddie for hope and he did believe her, after it probably took a while.
Also I want to point out that in iHate Sam's boyfriend Carly didn't want to tell Sam that her boyfriend tried to cheat on her when Freddie wanted to tell Sam. Best friends tell each other everything. Even when their boyfriends or girlfriends are lying cheating stub rags. Comment me.


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I had sex about a year ago about a year ago we only did it twice and once without protection. I was in class and they were talking about stds. I got me curious and when i looked at my vagina i have pimples. There small and they dont hurt or itch.I do shaveone in a while but i have never noticed those pimples before. Im freaked what could it be ?READ MORE
If you have pimples inside your vigina does it mean that its herpes or some other some type of std?
This question has not been answered by one of our experts yet.
Hi there
Hmmmm, i dont think you have a std necessarily. I think the best thing, is to go get it checked out, if you nervous...i am not convinced you have a std....
I have herpes, and when i have an outbreak, i get itchy, painful blisters that then kind of pop...and takes a wk or so to heal, and i feel awful...but thats me, many people experience it differently, what you describe doesnt really sound like herpes, but as i said, go to you doc, if you have one, and dont worry, dont be shy, they deal with this kind of thing everyday, thats what they there for!!!
But it could also be....depends what you mean by pimples....just dont worry too much, go get it checked...and always use protection till you been with someone for long and you trust each other, and can decide together what you want...
Good luck!!!READ MOREYou should know Answers to your question are meant to provide general health information but should not replace medical advice you receive from a doctor. No answers should be viewed as a diagnosis or recommended treatment for a condition. Content posted by community members does not necessarily reflect the views of Remedy Health Media, which also reserves the right to remove material deemed inappropriate.DISPLAY TEXTLINK URL


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Long Lasting Memory
Memory and Storage
Solid State Drives
Take PC performance to the next level with Samsung SSDs*
Designed to perform with utmost speed and reliability under tough demands, Samsung SSDs boast leading-edge technology. It is designed to help your PC boost, load and multi-task quickly.*EXPLORE
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Transfer fast, and secure your data
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Professional enough for the office desktop, and very portable, Samsung USB 3.0 Flash Drives can be at home wherever you take them.SEE ALL PRODUCTS


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Secure Important Files With Two-Step Verification for Dropbox
Photo of the author, Jennifer Beese by Jennifer Beese on August 27, 2012
In an effort to improve the security of its file-hosting service, Dropbox has introduced two-step verification.
This optional process requires members to submit a six-digit security code in addition to passwords upon signing in, or linking to a new computer, phone, or tablet.
The security code will be sent to your mobile phone via text message, or generated using a mobile authenticator app — ensuring that you're the only person who's able to access your account.
To enable two-step verification, visit the Dropbox security page (while you're logged in) and look for the feature toward the bottom of the page. If you've already turned on two-step verification for Gmail, then this process will seem very familiar.
Last month, the company found that usernames and passwords stolen from other websites were used to sign in to a number of Dropbox accounts. Given the nature of the files being hosted, extra security is crucial.
Dropbox stated that it will be adding two-step verification for all accounts in the next few days. If you can't wait, you can install the latest beta version and enable the feature now.
[Image credit: Marcin Kargol]
Jennifer Beese: Jennifer Beese has worked as a community manager and social media strategist. When she's not writing, you can find her studying anatomy and physiology—she literally has a skeleton in her closet—or under the stars with her telescope.


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Summer Camp hours are 8AM-4:30PM, extended hours can be arranged.
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" Papad is good appetizer and digestive.
" Roasted or grilled papad helps to absorb the fatty material from mouth and throat when consumed at the end of the meal.Papads are made of lentils and thus are free from gluten, rich in protein and dietary fiber. minimum order qty is 200gms.


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Just about someone can accustom what they know, or demonstrate how to do a aptitude that they've scholarly. And some of these can be, at times, effectual way of education. But these are not the just way to tutor. And for umpteen students, these are not the furthermost decisive distance to acquire.
Certified trainers know how to edward teach the same subject several polar distance so that their point gets crosstown to all of the students, not of late a few. They know how to endorse when students are having effort beside the topic, and they cognise how to assist the students that condition left-over support. They are besides amazingly right at artful interest group effort and hands-on projects, as resourcefully as functional problem-solving situations. These are all completely efficient methods of tuition that are oft disregarded by trainers who have not accompanied a Train the Trainer habituation work.
Other skills that certified trainers have which uncertified trainers by and large don't:Post ads:
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o scheduling breaks after proper basic cognitive process "chunks"
o getting pied groups of students to trade equally hand in glove and productively
o scheduling in time for exploit off the message a little, as frequently happens in a classPost ads:
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o keeping the students on track, when necessary
o ensuring that students immoderation respectively remaining beside respect
o ensuring that habitually unobserved and unnoted students get close study opportunities


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Your answer is one click away!
I am getting an object that looks like this
discount = {salesman: 0, supervisor: 0.02, manager: 0.05};
group_name = 'salesman';
I need to get the value of discount.group_name so if the group_name = 'salesman' I should get 0, if the group_name = manager I should get 0.05 and so on.
I tried to get it by doing
but it didn't work.
Please help me.
You need to use
console.log( discount[group_name] );
Look Bracket notation.


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Have a Tax Question? Ask a Tax Expert
Hello. My specialty is focusing on YOUR Financial needs. Financial Planner/Business Owner for 20 years. CPA,PFS,QFP,GMMA.
Yes these are deductible. I realize my answer is "short" but it's really that simple.
If this does not answer your question please let me know. Also let me know if you have additional questions.
This is my full time job and I only get paid if you accept my answer. We work on the trust system at JA.
If you understand my answer and have no more questions, please accept and please leave positive feedback (if so inclined). Thank you.


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❝Books are a uniquely portable magic.❞
cigarettesouls Jun 03, 2015
Hey I just wanted to know how you got your inverted commas to look like that in the description of this book?
lukesloveablepenguin Jan 05, 2015
@thebreakfastclvb check out my new book called 'The New Kid'. It has the quote in it. The quote is in the Finally chapter.
awkwardanadotcom Dec 07, 2014
slightly scary that I've read all of your other books and now look up book ideas and find you how did I not see this coming


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Can The Record IPhone 6S Sales Drive Apple Stock Higher?
- Apple announced that it has sold more than 13 million iPhone 6S/iPhone 6S Plus handsets just three days after their sales launch.
- New features such as Live Photos and 3D Touch are acting as a big draw.
- This marks a new record for the company and helps to alleviate investor fears about a possible slow down in iPhone sales.
Apple (NASDAQ:AAPL) reported that it has sold more than 13 million iPhone 6S/iPhone 6S plus handsets just three days after the sales launch of the new models, a new record for the company. It appears as if iPhone 6S' bells and whistles such as Multi-Touch interface with 3D touch and Live Photos are acting as a big draw for the phone, and dispelling the notion by some analysts that they are not substantive enough to make people want to upgrade. Live Photos brings still images to life. Meanwhile, the pressure-sensitive screen actually opens up a whole new vista of things that Apple developers can do with the smartphone. Live Photos, 3D Touch and the 12 Megapixel iSight camera are all powered by A9 chips, the most advanced chips in a smartphone.
But what does all this mean for Apple stock? As highlighted in an earlier post, Strong iPhone 6S sales could have a material impact on Apple stock price.
Good gains on the horizon
The investing world reacts quite strangely to new Apple products. Whereas stocks of most companies usually make good gains when they announce new products, Apple stock usually sells off by quite a margin whenever the company unveils new products. This probably has something to do with Apple's secrecy about new products, and super-high expectations surrounding the company. Apple shares have followed the old script and have slipped after the two phone models were unveiled.
Apple 5-Day Share Return
Source: CNN Money
Apple shares, however, tend to perform better 6-12 months after the launch of new products.
Apple Share Performance After Launch of New iPhones
Apple relies heavily on iPhone sales to drive its top line. The investing world watches hawk-eyed for any signs of trouble in this segment, which can sometimes lead to high short interest in Apple stock. With the good piece of news out, Apple investors can now rest easy.
What about Apple Watch?
One of the major reasons why Apple shares sold off badly post Q3 earnings call was because the company failed to break out Apple Watch sales numbers leading many investors to fear the devices were not selling as well as anticipated. There are a number of reports that keep surfacing on the Internet that suggest that Apple Watch sales are not great. A recent story about an Apple Watch parts supplier suggests that sales are still sluggish. But it's hard to arrive at conclusions based on just one supplier since Apple sources its parts from many different suppliers.
Apple recently announced that the Apple Watch will be available at 100 Best Buy stores in the U.S. starting August and another 300 locations before the holiday season. This marks the first time that the product will be available outside Apple retail stores. Apple retail stores typically have huge amounts of traffic which can discourage a lot of potential buyers of Apple products from shopping there. Meanwhile, while many investors have for long regarded Best Buy as the place where new products go to die, this seems to be changing. DJI, maker of the highly popular Phantom 3 consumer drone, has lately been seeing its drones selling out in nearly all Best Buy stores, which suggests that Apple picked the right retailer for Apple Watch.
Many analysts estimate that the product sold between 2-3 million units during last quarter, which puts it a shade below Fitbit Inc (NYSE:FIT) sales of 3.5 million units. That's quite remarkable for a newly launched product. Making the product available outside Apple retail stores might have a big impact on sales and allow Apple to finally report its sales as a separate category and thus help quell investor fears.


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Jessica Ivy – Jessica Ivy Left Gaping Wide
Released: November 18, 2016
Little Jessica Ivy is playing with her favorite toys – the ones that go right deep down her butt. With this dildo action, she is slowly stretching that anus to get ready to be pounded by the mighty Yura, and who knows how wide she will be after that. His anal jackhammer excavation leaves her asshole spread almost as wide as her mouth was for his dick. Deepthroat sucking, and now deep anal fucking. Watch as Ivy begs for more as Yura spanks and fucks her into ecstasy. Eventually he explodes over her perfect face. She might neat a special chair to sit down after this session…
Size: 485 MB | Duration: 34 min | Video: 960×544, 2012 kbits/s | Audio: 44100 Hz, 192 kbits/s


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Fillan of Pittenweem
Saint Fillan was a Scottish Benedictine monk from the Isle of May Priory, founded in 1153 by King David I of Scotland.
Fillan left the Isle of May for Pittenweem in Fife and converted the local populace to Christianity. The priory on May subsequently founded a priory there, which by 1318 had replaced the founding priory and which had been given to the canons regular of the cathedral priory in St. Andrews.
He is supposed to have lived in what is now called St Fillan's Cave, situated in Cove Wynd, Pittenweem, which is open to the public. The cave was rediscovered about 1900 when a horse ploughing in the Priory garden fell down a hole into it. The cave has flat rocks that are presumed to have been used as beds and a small spring of "holy water" at its rear. It was rededicated as a place of worship by the Bishop of St. Andrews in 1935.[1] It has since been refurbished and opened to visitors as of October 2000, and is owned by the Bishop Low Trust. It is entrusted to St John's Scottish Episcopal Church in Pittenweem.[2]
See also[edit]
|This article about a saint from the predecessor states to the United Kingdom is a stub. You can help Wikipedia by expanding it.
|This biography of a Scottish religious figure is a stub. You can help Wikipedia by expanding it.
|This article about a member of the Christian clergy is a stub. You can help Wikipedia by expanding it.


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How to Make a Ping-Pong smoke bomb
This little kid explains how to use aluminum foil and a Ping-Pong ball to make a small smoke bomb. This tutorial for a mini smoke bomb is easy to follow and precise. Just be careful when playing with fire! This smoke bomb only lasts a few seconds, but it's easy to make, so have some smoking fun.


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Sanding Belt, 3.5 In Wx15.5 In L, CA, 120G
NORTON| Zoro #: G0886611| Mfr #: 78072718753
Zoro #: G0886611Mfr. Part #: 78072718753
$9.91 /ea
Orders over $50.00 ship free. DetailsStandard ground shipping
Technical Specifications
|Zoro #: G0886611 Mfr #: 78072718753
|Backing Material: Cloth
|Abrasive Type: Coated
|Item: Sanding Belt
|Backing Weight: Y
|Series: R981 SG
|Abrasive Material: Ceramic
|Length: 15-1/2"
|Color: Red
|Grit: 120
|Grade: Fine
|Width: 3-1/2"
|Country of Origin (subject to change): United States
Product Description
Sanding Belt, Width 3-1/2 In., Length 15-1/2 In., Abrasive Type Coated, Abrasive Material Ceramic, Grit 120, Grade Fine, Series R981 SG, Color Red, Backing Weight Y, Backing Material Cloth


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Baltimore 44, San Francisco 6
BALTIMORE (Ticker) -- Ray Lewis and the rest of the Baltimore
Ravens had no problem spoiling Jeff Garcia's return to the
Lewis returned one of Garcia's four interceptions 29 yards for a
touchdown as the Ravens rolled to a 44-6 rout of the San
Francisco 49ers.
After missing the last three games with an ankle injury, Garcia
regained his starting job from Tim Rattay, who had played well
to fuel a quarterback controversy. Garcia added more fuel to
the fire with a miserable day.
"I didn't necessarily feel rusty," said Garcia, who was 14-of-29
for 112 yards before being pulled in the fourth quarter. "I
went out there and tried to make decisions. They played
differently, defensively in doubling outside and singling up on
the inside receivers. There weren't many windows."
Lewis, Baltimore's perennial Pro Bowl inside linebacker,
highlighted a 17-point second quarter with his return that gave
the Ravens a 24-6 lead with under a minute remaining in the
first half.
Anthony Wright added a pair of touchdown passes and Jamal Lewis
ran for a score for Baltimore (7-5), which posted its most
lopsided win in franchise history and remained tied with
Cincinnati for first place in the AFC North. The Ravens host
the Bengals next Sunday.
"With 24 points off turnovers, even I cannot screw that up,"
Ravens coach Brian Billick said. "This is a complete game for
us. It's the kind of game we're capable of having."
Todd Peterson kicked a pair of field goals for the Niners (5-7),
who fell to 0-6 on the road. It also was San Francisco's worst
loss since a 41-3 drubbing at Jacksonville in September 1999.
"Embarrassing game," Niners coach Dennis Erickson said. "Didn't
play well, basically, in all areas. I'm at the top of the food
chain, so it starts here. One of the tougher games I've ever
been around in my career."
Holding a 7-6 lead, the Ravens completely turned the game in
their favor late in the second quarter when they scored 18
points in a span of 1:41. Wright found Marcus Robinson with a
38-yard touchdown pass, extending the lead to 17-6. Robinson
had four TD receptions in last week's 44-41 overtime win over
"I think our biggest challenge was to come out and prove that
last week wasn't a fluke," said Wright, who completed 14-of-25
passes for 177 yards and one interception.
Ray Lewis capped the barrage with 32 seconds left in the half
with his first career touchdown in the regular season, making it
During the celebration of Lewis' return, Ravens linebacker Ed
Hartwell leveled line judge Carl Johnson, who was forced to
leave the game with a concussion.
Garcia threw an interception on the game's fourth play from
scrimmage, setting up Jamal Lewis' six-yard TD. Lewis rushed for
78 yards, bringing his league-leading total to 1,443 yards.
Niners star receiver Terrell Owens, who was matched against
cornerback Chris McAlister, managed just three catches for 23


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The families of several victims of the deadly rail crash in Lac-Mégantic, Que., are opposing a bid by a committee involving the Quebec government, municipal authorities and other victims for official standing at a railway's bankruptcy proceedings in the United States.
The families of 18 of the 47 victims of the July 6 derailment and explosion told a U.S. court in Maine that they object to the Quebec group's efforts to be appointed as an official creditors' committee representing the province, the municipality and some wrongful death and personal injury and property damage claimants.
"The families who lost loved ones believe they have specific interests that should be defended by their own committee," their lawyer, Daniel Cohn, said in an interview."They welcome help from the government, but they understand that [the government] could [use compensation] for cleaning environmental damage. They want their fair share."
Other parties have also objected, including the court-appointed trustee overseeing Montreal, Maine & Atlantic's bankruptcy. A hearing is set for Oct. 1 in Bangor, Me.
Last month, the Quebec government appointed Luc Despins, a bankruptcy specialist, to make representations on behalf of the committee. In his motion, he urged the judge to recognize the committee that represents creditors with the "largest claims" against the railway and the "entire spectrum" of victims.
"I want to be very clear: There is no animosity among the Quebec government, the City of Lac-Mégantic and the victims," Mr. Despins said Tuesday in an interview. "Quebec has already said that victim compensation was the priority."
Robert Keach, the court-appointed trustee in the proceedings, said inclusion of the creditors' committee was unnecessary because he represents the interests of all claimants. Recognizing the Quebec committee would increase administrative costs of the case and "sap what limited resources of the debtor exist," Mr. Keach wrote.Report Typo/Error


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|Message view
|« Date » · « Thread »
|« Date » · « Thread »
|Nascif Abousalh-Neto <Nascif.AbousalhN...@sas.com>
|RE: Reinforce dependency control
|Thu, 09 Oct 2008 01:13:45 GMT
We implemented that functionality with a wrapper around a static analysis tool called JarJar: http://code.google.com/p/jarjar/ This tool will give a list of dependencies on a jar to jar level for a given classpath. We parse the output and use it to generate an ivy.xml with the compile time dependencies. If you are interested I can send you our BeanShell wrapper - it is not worth posting as it is not very generic, since it has to deal with our own organization and module naming conventions. Developers were expected to do a second pass fixing organizations and module names and adding runtime dependencies. I think you might be able to generate test and runtime "correct" dependencies as well using a profiler or aspects - assuming you trust your test coverage to be good enough. Good luck, Nascif ________________________________________ From: Maarten Coene [email@example.com] Sent: Wednesday, October 08, 2008 3:37 PM To: email@example.com Subject: Re: Reinforce dependency control There is a very basic (and undocumented) task in Ivy that generates an ivy.xml from the import statements found in your sources. Just take a look at the IvyExtractFromSources task. Not exactly what you were looking for though Maarten ----- Original Message ---- From: Gilles Scokart <email@example.com> To: email@example.com; Ant Users List <email@example.com> Sent: Wednesday, October 8, 2008 3:40:30 PM Subject: Reinforce dependency control When working on some big project with heavy history and when looking at the quality of some meta data in the maven repository I'm always shoked by the amount of useless dependencies used. To counter that, I would like to use (and promote) a tool that help the developpers to limit their path to the strict minimum. I would like to start with the easiest : the compile path. (Next I would try to do the same for test classpath, and maybe for runtime classpath) I would like to push my build to an extreme solution. I want to make a build fail if some jars of the compile path are not used. Did you know a task that do that? Or did you know any tools that list the jars that are required by a sources (or a compiled jars) that I could adapt to make such a tasks? -- Gilles Scokart
|View raw message


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This paper considers two primary issues about big
- Stephany Peters
- 1 years ago
- Views:
1 SYMPOSIUM Drawing Inferences and Testing Theories with Big Data Jonathan Nagler, New York University Joshua A. Tucker, New York University This paper considers two primary issues about big data: (1) big data as a sample, and (2) a particular type of big data that is, social media data. We argue that if we can learn things from little data, then we certainly can learn from big data. We think that big data offers a tremendous opportunity that little data does not. We argue that having more data is an opportunity for, not a constraint on, testing theories of political behavior. We address three aspects of big data as a sample. Regardless of how big big data is, it is a sample drawn from a population. First, this means that we have to consider how representative the sample is of the population that we claim to be studying. 1 Second, we have to consider whether the population from which the sample was drawn is a population of interest and/or whether the population it was drawn from generalizes to other populations. Third, because big data is big, we have to adopt more caution than typically is the case when interpreting our measures of statistical precision. Big data drives sampling error toward zero but does not reduce other errors associated with inferences drawn from a sample. That big data drives sampling error toward zero makes big data different from little data in that we must be careful not to be fooled by our own estimated precision. Although there are many types of big data available, we consider what we can learn from social media data. Big data comes in many forms, including individual-level campaign contributions (Bonica 2013 ), large corpora of text from traditional print media, records of legislative debate, and judicial opinions. Big data also comes from new activities, such as counts of online searches. We think that social media data is particularly valuable to political scientists for at least three reasons. First, it allows us to observe a rich set of data on those networks in which people choose to embed themselves. Second, social media data allows us to observe the comments that people freely make about politics. Thus, if we are interested in knowing what people think, we do not need to ask they volunteer this information on social media. Third, because social media data also is big data, it offers advantages over traditional types of political science survey data. Because so many people are on social media, we can obtain large samples from subpopulations of interest, whether they are defined by geographic characteristics (e.g., residents of a particular state), demographic characteristics (e.g., young black men), or political characteristics (e.g., Democrats). Fourth, because people use social media to communicate about a variety of political activities, including protest activities and political mobilization, we can use it to learn about these strategies more generally. Thus, social media data comprises a source of big data by providing information about peoples opinions regarding politics, their choices of information sources, the nature of their associations, and their strategic choices in certain political situations. As political scientists, we think we can learn something from this. BIG DATA AS A SAMPLE First, we want to make a few simple points. In some ways, big data is not different than other data. If we can learn things from little data, we ought to be able to learn things from big data. So we still can learn useful things from a 2x2 table using big data. And we still can learn useful things and make causal inferences from a well-specified model testing clearly defined hypotheses using big data. We even can run experiments with big data: randomizing treatment across respondents and observing outcomes. We also can make bad mistakes with big data, just as we can with little data. One of the things that has happened with big data is that computer scientists have discovered big datasets related to politics. This is both a good thing and a bad thing. As social scientists, we are interested in stating and testing hypotheses. We typically test our hypotheses by trying to draw statistical inferences. We find a sample, analyze data, and draw a statistical inference about the parameters of the population from which the sample was drawn. So far, the computer scientists who have analyzed big data about politics have not worried so much about this. They are very good at worrying about whether their estimates are applicable to their sample: they are much more thorough than the typical political scientists at out-of-sample testing. However, in this context, out of sample means split the sample into many parts, and determine whether analyzing one part of the sample can be applied to the other parts of the sample. But of course, since the sample is split at random, this is a means of establishing how well we can forecast, assuming our sample is representative of the population. However, this is not a way to check whether the sample is representative of the population. However, as political scientists, we typically are concerned about the properties of the sample. Specifically, is it representative of the population? This is a major problem in traditional survey data a problem that has waxed and waned over time. We have come a long way from the Literary Digest days. Standardized techniques have been developed for drawing random samples. Although we have struggled with the banal 84 PS January 2015 American Political Science Association, 2015 doi: /s
2 problem of who is more likely to answer the telephone, until relatively recently, telephone surveys seemed to be well understood. Cell phones have made surveys more difficult to conduct, and response rates have plummeted across both types of telephones. Furthermore, Internet surveys have ushered in the world of bigger (but not big) data for survey research. Serious survey firms (e.g., Knowledge Networks and Polimetrix) have pioneered methods to survey people over the Internet, resulting in representative samples of populations of interest (e.g., registered voters, or the adult population). Of course, the Internet also has introduced the age of the convenience sample, which is anything but representative. from the same population as our sample, and we estimated the same quantity, we would obtain the same result. None of this suggests that the quantity we estimated was meaningful. And this is the danger of inference with big data. The error that we observe most easily is our sampling error. However, big data drives the sampling error toward zero while having no effect relative to small data on other causes of total survey error. Total survey error would include (1) error induced by the failure of the sampling design to generate a sample that was truly randomly drawn from the population, (2) failure to handle unit nonresponse, and (3) measurement error induced by any survey instrument. 3 None of this suggests that the quantity we estimated was meaningful. And this is the danger of inference with big data. The error that we observe most easily is our sampling error. With big data comes the illusion of big precision. In one often-repeated paradigm, scholars estimate a model of vote choice for a given election using the National Election Study and they publish a result with a t-statistic more than 2. We then conclude that we are 95% certain that our result would hold (to the extent that would hold means the sign would not change) if we had data on the entire set of voters for that election. Through sleight of hand or carelessness, we can pretend that our result would hold for other elections and that we have found a universal truth because our result is statistically significant. However, we have not found a universal truth; rather, we have found something that was probably true in the one election that we observed. Does this result generalize to other elections? We have no idea. We must take it on faith that the election we observed was randomly drawn from all possible elections. Or, we must believe that the model we specified applied to all elections. Perhaps it does, or perhaps we omitted a variable that changes from election to election. For example, we could estimate a model of voter turnout in the 2008 election using the 90,000 or so observations of the Current Population Survey. Based on our estimates, would we be certain that older people, ceteris paribus, are more likely to vote than younger people? Of course not. We would be very certain of this only for the 2008 election. We could state this because we have a representative sample of all potential voters in that election. But perhaps there was something specific to the election that caused older people to vote more than younger people? If we want to know if this were generally true, we would need to observe many more elections. 2 Or, we would have to assume and assuming is not knowing that our correctly specified model accurately describes the data-generating process in every election, not only the one on which we estimated it. With big data, if we estimate a quantity and compute a standard error, it will appear as if we are very certain of something. This is the beauty of dividing by N. However, all that we are very certain of is that if we had even more data drawn Luckily, the discipline (or parts of it) has recognized the fallacy of asterisks and the worshipping of statistical significance just in time for the deluge of big data and big t-statistics. Part of the credit for this goes to the Bayesians, but part of it is simply common sense. Therefore, if we report standard errors computed from big data, while we are still computing a value associated only with sampling error, at least we may be less likely to conclude that our findings are validated by strings of asterisks indicating statistical significance at traditional levels. However, big data itself comes in many flavors, and sometimes it can appear that we know the population. We may know the entire number of Google searches on Google for a particular term or set of terms. But presumably, that is only a proxy for some facts of political interest. If we consider Google searches to indicate interest in something, then if we see the number of searches for impeach Obama increases, for example, we might think that interest in impeaching the president is increasing. It might be increasing among the people likely to use Google to search for political facts. If 95% of the voting-age population is likely to use Google to search for political facts in this way, and if the number of searches for impeach Obama doubles, then using the method of bounds, we can be quite certain that interest in impeaching the president has risen among the voting-age population, if we believe that those searches express interest. However, most questions in big data are not so clear cut. This simple example suggests something to which we return in the following discussion: Why not survey people to determine whether those who searched for impeach Obama are more likely to want to impeach the president than those who did not search for impeach Obama? SOCIAL MEDIA DATA We now turn to social media data, with a particular focus on Twitter data. By using the term social media data, we are referring to data that has distinct characteristics. First, it is PS January
3 Symposium: Drawing Inferences and Testing Theories with Big Data posted online by the mass public. Second, people can freely choose the set of people from whom they receive information; that is, they choose the information network in which to embed themselves. Third, people can comment on information that others post. Fourth, there may or may not be anonymity. We consider Twitter data. A user on Twitter can follow people who they know in the offline world; there is no anonymity. However, John Smith-Public on Twitter also can follow Jane Smart-Political-Analyst because many of his friends follow her. However, John SP cannot be certain if Jane SPA is a 45-year-old woman in Los Angeles as her profile claims or a 17-year-old in China pretending to be her. Furthermore, tweets by Mary Good-Environment might actually be from a coal-industry lobbyist trying to make moderate-sounding comments to suggest that global warming is not a problem. In either case, John SP knows only that the tweets sent over time by Jane SPA and Mary GE represent those sent by the same person (or entity). Twitter data is big data: worldwide, there are more than 270 million active Twitter accounts. 4 Everyone on Twitter has chosen whom to follow, thereby publicly revealing their network to us. This network includes people that they follow who can reveal something about their politics as well as the people who they follow for presumably nonpolitical reasons (e.g., entertainers, sports figures, friends, and relatives) but who nevertheless may provide them with political information. We learn who people are following because this information is publicly available on Twitter; we do not have to depend on their recall or their description of the four or eight people with whom they communicate most often. This means that we can observe which types of political information people are choosing to obtain. If we want to study the effect of political information on attitudes and/or behavior, this is valuable data. If we only want to examine which information is likely to be believed, this also is valuable because we can observe what information people choose to pass on via retweets. We believe that it is fruitful to think of Twitter as unstructured survey data. In most surveys used by political scientists to study political behavior, an analyst asks the questions using a structured format, which most often requires closed-ended a proxy for how much the set of Twitter users care about the political issue of immigration. However, this suggests obvious potential pitfalls. The term immigration could be used in contexts other than political contexts. Thus, faith in counting, without human verification from a small subsample of tweets with the term immigration to confirm that a majority is about politics, could result in substantial errors. Of course, depending on the data, confirming that a majority of such tweets is about politics might not be adequate. Imagine that we observe a 10-percentage-point increase in the number of tweets per week containing immigration. If we know that only 70% of the tweets concerned the political topic of immigration, then it would be risky to conclude that a 10-percentage-point increase in tweets containing the term immigration signaled an increase in concern for the political topic of interest. The increase could represent an increase in nonpolitical uses of the term immigration. 5 However, in addition to searching for occurrences of terms preselected by an analyst, we can search in the text for terms that appear together in our attempt to identify topics. This is common in the machine-learning community via Latent Dirichlet Analysis (LDA): a corpus of documents is created (here, the set of tweets) and analyzed based on an underlying probabilistic model to assign words to topics and, thus, tweets (based on word content) to topics. The analyst then looks at the words in each topic and decides whether the topic revealed by LDA in fact represents a substantive political topic. Thus if, as political scientists, we want to know what people on Twitter are talking about, and what political events or stories or topics they are talking about, we have ways to do so. This brings us to the obvious question: do we care what people on Twitter are discussing? If we can say that they are representative of some population we care about (e.g., votingage adults), then we would care. We also care if there are simply enough of them to care about, but it would make inference much harder. For instance, if we measure the opinion of the top 20% of US households based on income, and we observe that it changes on an economic issue, then we could relate that to politics: we would know that the opinion of the rich is changing. One potential solution to the problem of making opinion on Twitter politically relevant is to look at how opinion varies across Twitter users based on politically relevant characteristics of the users. responses. We can think of Twitter as having completely open-ended responses, in which the analyst has not even posed a question. Thus, it is an unfiltered look into what people choose to say about politics. We can learn which issues that people on Twitter think are important over time, and we can do this in at least two different ways. As analysts with substantial expertise in the area of study, we can search for terms on Twitter that we think correspond to topics of interest. For example, we can search for the term immigration to determine how often it is used. This could serve as But, Twitter users are not a politically relevant category; thus, simply knowing that opinion on Twitter is changing (or even what it is in a static sense) is not as useful for us as political scientists. 6 Thus to make use of opinion measured on Twitter, we want to be able to somehow relate Twitter users to the population of interest. One potential solution to the problem of making opinion on Twitter politically relevant is to look at how opinion varies across Twitter users based on politically relevant characteristics of the users. Very little information about Twitter users is 86 PS January 2015
4 provided by default. But, we can estimate the gender of Twitter users based on their name with a high degree of accuracy. And Barberá (2014) showed that we also can accurately estimate the ideology of many Twitter users. Only about 5% of tweets are geo-coded, which allows a very accurate estimation of location. However, based on self-reported place, we can accurately predict the state a user resides in for about 70% of users. This means that, for example, we can identify that among Twitter users, immigration is talked about more in Arizona than in Idaho. And we also can identify that among Twitter users, immigration is talked about more by people on the political right than on the left. While there is no guarantee that those relationships hold in the voting-age population, it is not a bad hypothesis that they do. Another way that opinion on Twitter can be politically relevant is to observe how it changes in response to political events; again, this can be refined by observing changes across different groups of users. Perhaps Twitter s greatest strength as a data source is that it is unprompted by survey questions, which means that it provides an unfiltered measure of whatever people are choosing to discuss. A long-standing question in the study of representation has been: Who leads and who follows? Do elected representatives set the agenda and constituents follow, or do elected representatives respond to issues about which their constituents care? In research with our colleagues in the Social Media and Political Participation laboratory at New York University on this question, we examined tweets by politically interested Twitter users (i.e., those who followed political figures) and tweets by members of Congress during a 14-month period (Barberá et al. 2013). This allowed us to compare topics that the mass public was talking about with those that members of Congress were discussing and to determine which group talked about a topic first (i.e., led) and which group talked about it after the other group (i.e., followed). We want to point out some inferences not to draw from Twitter data. It is easy to look at Twitter data and notice a high correlation between the proportion of tweets for congressional candidates and the proportion of votes for them. First, inferring any causality from this would obviously be As one of the specific purposes Twitter is used for is to disseminate information during protests, we also can learn about the strategy of protest by studying Twitter data. We can determine with whom protesters are trying to communicate by their choice of language (Tucker et al ), and we can learn which people in protest networks are most likely to be influential in spreading information (Metzger et al ). Twitter as a New Variable We point out that Twitter data is big data with two potentially interesting characteristics for political scientists. First, as we have previously discussed, Twitter data is a potential source of information about the political world, just as telephone and Internet surveys are. And it is very interesting data: repeated observations on millions of individuals collected over time. But second, Twitter is a potential variable itself in the political world. Twitter can provide much politically relevant information to people that has the potential to alter their political behavior. Twitter can provide people information about opportunities to participate politically, about the cost of participation, and about the potential outcomes of participation. Twitter can provide people information about opportunities for participation by informing people about everything from protest activities (i.e., where and when), to when a candidate forum or rally for a candidate is scheduled, to where a polling place is located. Without a means to know where a protest is, one cannot participate. Without a means to know where a candidate forum or candidate rally is, one cannot participate. Twitter is of course not the only means to know about these things. Thus, we need to be careful about drawing inferences about its causal impact, a point we return to below. Twitter can also can provide people information about the cost of participation. If people are considering participation in a protest, they can find out whether or not the protest is well attended; or sparsely attended if they are worried that being one of the few people at a protest will make them targets of a hostile regime. They also can find out if a protest has the potential for violence: finding out that people are being shot at, or that large numbers of heavily armed military As Twitter allows people to publicly share information about their views on politics, Twitter can also provide information to political elites about public opinion. a bad idea. The winning congressional candidates spent more money than their opponents, had more supporters in their district, had higher name recognition, and so forth. Therefore, it is no surprise that a higher proportion of tweets were about them. We note that one could attempt to identify challengers who might be doing better than expected by unusually high numbers of posts on Twitter. However, this would require a model of the expected baseline number of tweets, which is likely to vary with district demographics as well as the demographic source of support for a candidate. 7 personnell have been seen heading towards the site of a protest, would give people valuable information about the likely costs of attending. Furthermore, Twitter can inform people about potential benefits of participation in the form of outcomes. If people learn on Twitter that one candidate advocates a policy of which they are in favor, there is more reason to participate and they are more likely to do so (Leighley and Nagler 2014 ). Moreover, Twitter can provide not only information about political events and political elites but also what users friends think about politics. This may lead to a world in which people PS January
5 Symposium: Drawing Inferences and Testing Theories with Big Data have an easier time making correct political decisions in their own self-interest. A long-standing paradox of democracy and political discourse is that citizens are presumed to vote for candidates closest to their political views. However, given the low likelihood of being pivotal in any election, it is difficult to explain why citizens would make the effort required to learn the candidates views. The cheapest available solution is generally thought to be simply finding out for which candidate our politically informed friends are voting, with the implicit assumption that our friends have similar interests. Because Twitter provides a mechanism for people to broadcast their political views to their friends and for people to receive the political views of their politically interested friends, Twitter can be a way to achieve this information shortcut. As Twitter allows people to very publicly share information about their views on politics, Twitter can also provide information to political elites about public opinion. 8 To find out if Twitter use is having an impact on political behavior and politics, we would want to do the obvious: compare Twitter users to non-twitter users. We want to match a Twitter user to someone who is identical on all observable characteristics, and see if the Twitter user is likely to learn more during an election campaign (or during any period) than the non-twitter user. We also can test persuasion: is the Twitter user more likely to change their intended vote choice (or choice of whether or not to vote) during the campaign than the non-twitter user? This requires panel data: using traditional survey techniques to collect a set of respondents on Twitter and a set of respondents off Twitter, and comparing their responses over time. There is simply no way to draw causal inferences on the effect of Twitter by studying only those people on Twitter. However, we can indirectly observe people who were not on Twitter previously by comparing people on Twitter who joined at different times. In this case, causal inferences are possible, but more limited. 9 We think it is too early to claim that Twitter is having a large effect on political behavior; but we think it is important to study it in a rigorous manner to answer the question of whether it is having an effect, and, if so, what the effect of Twitter is and when it is likely to have the most impact. CONCLUSION If we believe the old adage that to err is human, to really mess things up requires a computer, then it follows that we could make big mistakes with big data. However, we also can learn much from big data; and following the same careful attention to the requirements of causal inference that we would follow with any observational dataset, we can indeed draw causal inferences. ACKNOWLEDGMENTS This paper was prepared for presentation at the August 2014 APSA Annual Meeting in Washington, DC. The authors are members of the New York University Social Media and Political Participation (SMaPP) laboratory. The writing of this article was supported by the INSPIRE program of the National Science Foundation (Award #SES ) and Dean Thomas Carew and the Research Investment Fund (RIF) of New York University. NOTES 1. Even if we have all of the data, it is a sample of all of the data that could exist over time or all of the data to which we want to generalize. Therefore, conceptually, it is still a sample. 2. We have observed many more elections and find this to be true (Leighley and Nagler 2014 ). 3. See Biemer and Lyber Source : Twitter s second-quarter 2014 results. 5. This is less likely a potential problem when we attempt causal inference and look at temporal relationships among more than one variable. In those cases, the typical assumption that the noise is expected to be orthogonal to the quantity of interest makes more sense. 6. We might think that the population of Twitter users discussing politics is politically relevant because it is this population that is disseminating political information via this new source. 7. On using Twitter as a source of big data to conduct polling, see Beauchamp (2013). 8. Of course, this assumes that political elites employ sophisticated data scientists to analyze the Twitter data. 9. Another way that Twitter can have an impact is when people on Twitter share information they receive with people off Twitter. See Vacarri et al. (2013). REFERENCES Barberá, Pablo Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis. DOI / pan/mpu011. Barberá, Pablo, Richard Bonneau, John T. Jost, Jonathan Nagler, and Joshua Tucker Is There Anybody out There? The Effects of Legislators Communication with their Constituents. Paper presented at the 2013 Text as Data Workshop. London: London School of Economics and Political Science. Beauchamp, Nick Predicting and Interpolating State-Level Polling Using Twitter Textual Data. Paper presented at the 2013 APSA Annual Meeting, Chicago, IL. Biemer, Paul, and Lars Lyber (eds.) Special Issue on Total Survey Error. Public Opinion Quarterly 74 (5): Bonica, Adam Ideology and Interests in the Political Marketplace. American Journal of Political Science 57 (2): Leighley, Jan E., and Jonathan Nagler Who Votes Now? Demographics, Issues, Inequality, and Turnout in the United States. Princeton, NJ : Princeton University Press. Metzger, Megan, Duncan Penfold-Brown, Pablo Barberá, Richard Bonneau, John Jost, Jonathan Nagler, and Joshua Tucker Dynamics of Influence in Online Protest Networks: Evidence from the 2013 Turkish Protests. Paper presented at the 2014 APSA Annual Meeting, Washington, DC. Tucker, Joshua A., Megan Metzger, Duncan Penfold-Brown, Richard Bonneau, John Jost, and Jonathan Nagler Social Media and the #Euromaidan Protests. Paper presented at the Workshop on Mass Protest. London: London School of Economics. Vaccari, Christian, Augusto Valeriani, Pablo Barberá, Richard Bonneau, John T. Jost, Jonathan Nagler, and Joshua Tucker Social Media and Political Communication: A Survey of Twitter Users during the 2013 Italian General Election. Rivista Italiana di Scienza Politica 43 ( 3 ): PS January 2015
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Ironman died while officials debated rough conditions
A pre-inquest hearing into the death of ironman Saxon Bird at the National Surf Life Saving Championships on Queensland's Gold Coast has heard several officials were wanting to call off the event.
The State Coroner has been told that the teenager was struck by a stray board in rough conditions during the paddle ski leg of the national championships at Kurrawa Beach in March.
An autopsy found drowning was the cause of death.
Today's pre-inquest hearing was told he died just minutes before a meeting called by event officials to discuss calling off competition.
They had canvassed the views of a number of competitors including elite ironman Shannon Eckstein who said the surf was at the top of his competitive range and advised the rest of the day's events should be postponed or moved.
Competitors and officials will be called to give evidence at an inquest early next year.
Topics: courts-and-trials, law-crime-and-justice, sport, surfing, brisbane-4000, australia, qld, southport-4215
First posted


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Veteran actress and Bharatiya Janata Party MP Hema Malini was injured in a road accident that took place on Thursday night. The saddest part is that a young girl child got killed.
The accident took place at Dausa in Rajasthan, when Hema was sitting in the front seat of the Mercedes car. She was returning from Jaipur after a visit to Mehendipur Balaji temple in Karaili district when her car got rammed into an Alto at the Lalsot bypass on the highway.
The Alto car passengers where a family from Lalsot who were taken to the hospital in Dausa and where later referred to Jaipur.
Heartbreaking News: Aditya-Shraddha Kapoor Breakup Confirmed
SP Anshuman Bhomia from Dausa revealed that the case has been registered against Hema Malini's driver and both the vehicles have been seized.
Meanwhile the veteran actress Hema Malini was rushed to the Fortis hospital as she has sustained injuries on her forehead, cheek, and legs. The doctors conducted a CT scan and administered stitches on her wound. Currently a neurosurgeon and a plastic surgeon are treating her and have kept her under overnight observation.
Her daughter Esha Deol too rushed to the hospital to be by her ailing mother's side. The doctor who tend to the actress's wounds said, "She arrived at the Emergency with a cut above the right eyebrow and complained of backache, but other than that, there were no visible injuries."
"She was coherent, which is always a good sign. We're running scans for internal injuries, if any," he added.
Shiv Ram
Hema ji has injuries above her eyes,in her back & legs.I drove her to hospital in my car. She was in pain: Shiv Sharma
Spot Report
1 child dead and 4 injured in an accident in Dausa, Rajasthan. BJP MP Hema Malini also injured.
Hema At The Spot
"She arrived at the Emergency with a cut above the right eyebrow and complained of backache, but other than that, there were no visible injuries," said a doctor.
Hema In Car
"Deeply saddened by the news of Dausa accident. I pray for the victims. We're making all efforts to provide immediate relief & medical care," said Vasundhara from BJP party.


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Burger King plans to add a mobile-payment option to its domestic stores next month with plans to make the technology available at all of its more than 7,000 units in the United States within a few months, according to a report from Bloomberg. The app will provide coupons and nutrition facts, and will allow payment values to be loaded onto a virtual card, according
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