Surveys Can Make People Go Extreme by Esther Inglis-Arkell

http://io9.com/surveys-can-make-people-go-extreme-1621840701

There are all kinds of reasons why people don't tell the truth when asked questions. Sometimes they suddenly turn into fanatics. They hate, or love, anything. Here's how you catch people when they go extreme, or when they try to just get along.

We already know that people deliberately lie when given surveys on sex and drugs, but they also lie when given surveys about the importance of flossing and whether people should smoke in shopping malls. The difference is, many people don't even know that they're lying. People are driven to exaggerate (or even invent) their likes and dislikes, and so when they're asked to score, from one to five, their support for an issue or agreement with a statement, they avoid the middle and go right for one and for five.

This bias, called "extreme response bias" has annoyed many manufacturers, or politicians, who believed their targeted audience was passionately in favor of a new flavor of coke or a ban on littering, trotted the idea out, and gotten a lackluster response. Sometimes people are actually passionate about a subject, and sometimes they just want to be that way. Researchers took a look at separating out the two. They came up with a few guidelines to tell if people were inflating their opinions.

First of all, the more options you give a person, the more likely they are to go for the fringe opinion. A survey asking people to rate their experience on a scale of one to five will get far fewer extreme responses than a survey that asks people to rate their experience on a scale from one to ten. Individually, people with more education tend to be less extreme in their responses. The most telling variable, though, is another kind of bias.

Acquiescence bias is the tendency of a surveyed individual to go along with whatever the surveyor suggests. This is why researchers agonize over trying to make each question as neutral as possible. Ask people "don't you think smoking should be completely banned in malls," and they will tend to say yes. Ask them, "don't you think people should be allowed to smoke in public malls," and they will also tend to say yes. In order to be accurate, researchers can't tip their hands and let people know what answer they expect, or want. If, on the other hand, what the researchers want is to tell how many people responding to their questions are just going along with it, they can put out two surveys, one with a question that tips people one way, and one with a re-worded version of the question that tips people the other way.

Acquiescence bias tends to be a harbinger of extreme response. If people aren't going to be honest - either with the surveyors or themselves - they're at least going to be enthusiastically dishonest. So the more acquiescence everyone gets, the more extremity they should expect to see.

New algorithm identifies data subsets that will yield the most reliable predictions by Larry Hardesty

http://phys.org/news/2014-07-algorithm-subsets-yield-reliable.html#jCp

Much artificial-intelligence research addresses the problem of making predictions based on large data sets. An obvious example is the recommendation engines at retail sites like Amazon and Netflix.

But some types of data are harder to collect than online click histories —information about geological formations thousands of feet underground, for instance. And in other applications—such as trying to predict the path of a storm—there may just not be enough time to crunch all the available data.

Dan Levine, an MIT graduate student in aeronautics and astronautics, and his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics, have developed a new technique that could help with both problems. For a range of common applications in which data is either difficult to collect or too time-consuming to process, the technique can identify the subset of data items that will yield the most reliable predictions. So geologists trying to assess the extent of underground petroleum deposits, or meteorologists trying to forecast the weather, can make do with just a few, targeted measurements, saving time and money.

Levine and How, who presented their work at the Uncertainty in Artificial Intelligence conference this week, consider the special case in which something about the relationships between data items is known in advance. Weather prediction provides an intuitive example: Measurements of temperature, pressure, and wind velocity at one location tend to be good indicators of measurements at adjacent locations, or of measurements at the same location a short time later, but the correlation grows weaker the farther out you move either geographically or chronologically.

Emotional Contagion on Facebook? More Like Bad Research Methods by JOHN M. GROHOL, PSY.D.

http://psychcentral.com/blog/archives/2014/06/23/emotional-contagion-on-facebook-more-like-bad-research-methods/

A study (Kramer et al., 2014) was recently published that showed something astonishing — people altered their emotions and moods based upon the presence or absence of other people’s positive (and negative) moods, as expressed on Facebook status updates. The researchers called this effect an “emotional contagion,” because they purported to show that our friends’ words on our Facebook news feed directly affected our own mood.

Nevermind that the researchers never actually measured anyone’s mood.

And nevermind that the study has a fatal flaw. One that other research has also overlooked — making all these researchers’ findings a bit suspect.

Putting aside the ridiculous language used in these kinds of studies (really, emotions spread like a “contagion”?), these kinds of studies often arrive at their findings by conducting language analysis on tiny bits of text. On Twitter, they’re really tiny — less than 140 characters. Facebook status updates are rarely more than a few sentences. The researchers don’t actually measure anybody’s mood.

So how do you conduct such language analysis, especially on 689,003 status updates? Many researchers turn to an automated tool for this, something called the Linguistic Inquiry and Word Count application (LIWC 2007). This software application is described by its authors as:

The first LIWC application was developed as part of an exploratory study of language and disclosure (Francis, 1993; Pennebaker, 1993). As described below, the second version, LIWC2007, is an updated revision of the original application.

Note those dates. Long before social networks were founded, the LIWC was created to analyze large bodies of text — like a book, article, scientific paper, an essay written in an experimental condition, blog entries, or a transcript of a therapy session. Note the one thing all of these share in common — they are of good length, at minimum 400 words.

Why would researchers use a tool not designed for short snippets of text to, well… analyze short snippets of text? Sadly, it’s because this is one of the few tools available that can process large amounts of text fairly quickly.

Who Cares How Long the Text is to Measure?

You might be sitting there scratching your head, wondering why it matters how long the text it is you’re trying to analyze with this tool. One sentence, 140 characters, 140 pages… Why would length matter?

Length matters because the tool actually isn’t very good at analyzing text in the manner that Twitter and Facebook researchers have tasked it with. When you ask it to analyze positive or negative sentiment of a text, it simply counts negative and positive words within the text under study. For an article, essay or blog entry, this is fine — it’s going to give you a pretty accurate overall summary analysis of the article since most articles are more than 400 or 500 words long.

For a tweet or status update, however, this is a horrible analysis tool to use. That’s because it wasn’t designed to differentiate — and in fact, can’t differentiate — a negation word in a sentence.¹

Let’s look at two hypothetical examples of why this is important. Here are two sample tweets (or status updates) that are not uncommon:

“I am not happy.”
“I am not having a great day.”

An independent rater or judge would rate these two tweets as negative — they’re clearly expressing a negative emotion. That would be +2 on the negative scale, and 0 on the positive scale.

But the LIWC 2007 tool doesn’t see it that way. Instead, it would rate these two tweets as scoring +2 for positive (because of the words “great” and “happy”) and +2 for negative (because of the word “not” in both texts).

That’s a huge difference if you’re interested in unbiased and accurate data collection and analysis.

And since much of human communication includes subtleties such as this — without even delving into sarcasm, short-hand abbreviations that act as negation words, phrases that negate the previous sentence, emojis, etc. — you can’t even tell how accurate or inaccurate the resulting analysis by these researchers is. Since the LIWC 2007 ignores these subtle realities of informal human communication, so do the researchers.²

Perhaps it’s because the researchers have no idea how bad the problem actually is. Because they’re simply sending all this “big data” into the language analysis engine, without actually understanding how the analysis engine is flawed. Is it 10 percent of all tweets that include a negation word? Or 50 percent? Researchers couldn’t tell you.³

Even if True, Research Shows Tiny Real World Effects

Which is why I have to say that even if you believe this research at face value despite this huge methodological problem, you’re still left with research showing ridiculously small correlations that have little to no meaning to ordinary users.

For instance, Kramer et al. (2014) found a 0.07% — that’s not 7 percent, that’s 1/15th of one percent!! — decrease in negative words in people’s status updates when the number of negative posts on their Facebook news feed decreased. Do you know how many words you’d have to read or write before you’ve written one less negative word due to this effect? Probably thousands.

This isn’t an “effect” so much as a statistical blip that has no real-world meaning. The researchers themselves acknowledge as much, noting that their effect sizes were “small (as small as d = 0.001).” They go on to suggest it still matters because “small effects can have large aggregated consequences” citing a Facebook study on political voting motivation by one of the same researchers, and a 22 year old argument from a psychological journal.⁴

But they contradict themselves in the sentence before, suggesting that emotion “is difficult to influence given the range of daily experiences that influence mood.” Which is it? Are Facebook status updates significantly impacting individual’s emotions, or are emotions not so easily influenced by simply reading other people’s status updates??

Despite all of these problems and limitations, none of it stops the researchers in the end from proclaiming, “These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks.”⁵ Again, no matter that they didn’t actually measure a single person’s emotions or mood states, but instead relied on a flawed assessment measure to do so.

What the Facebook researchers clearly show, in my opinion, is that they put too much faith in the tools they’re using without understanding — and discussing — the tools’ significant limitations.⁶

Reference

Kramer, ADI, Guillory, JE, Hancock, JT. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS. http://www.pnas.org/cgi/doi/10.1073/pnas.1320040111

Footnotes:

1. This according to an inquiry to the LIWC developers who replied, “LIWC doesn’t currently look at whether there is a negation term near a positive or negative emotion term word in its scoring and it would be difficult to come up with an effective algorithm for this anyway.” [↩]
2. I could find no mention of the limitations of the use of the LIWC as a language analysis tool for purposes it was never designed or intended for in the present study, or other studies I’ve examined. [↩]
3. Well, they could tell you if they actually spent the time validating their method with a pilot study to compare against measuring people’s actual moods. But these researchers failed to do this. [↩]
4. There are some serious issues with the Facebook voting study, the least of which is attributing changes in voting behavior to one correlational variable, with a long list of assumptions the researchers made (and that you would have to agree with). [↩]
5. A request for clarification and comment by the authors was not returned. [↩]
6. This isn’t a dig at the LIWC 2007, which can be an excellent research tool — when used for the right purposes and in the right hands. [↩]

io9: Anti-Obamacare Ads Backfired, Says A New Statistical Analysis by Mark Strauss

http://io9.com/anti-obamacare-ads-backfired-says-a-new-statistical-an-1604627451

Opponents of the Affordable Care Act have spent an estimated $450 million on political ads attacking the law, outspending supporters of Obamacare 15-to-1. But a state-by-state comparison of negative ads and enrollment figures suggests the attacks ads actually increased public awareness of the healthcare program.

Niam Yaraghi, a Brookings Institution expert on the economics of healthcare, based his analysis on recently released data (below) that tallies how much money was spent on anti-Obamacare ads in each state.

He then examined Affordable Care Act (ACA) data to determine enrollment ratios. Although more than 8 million Americans have signed-up to purchase health insurance through the marketplaces during the first open enrollment period, that number masks the tremendous variation in participation across states. For instance, while the enrollment percentage in Minnesota is slightly above 5%, in Vermont, close to 50%of all eligible individuals have signed up for Obamacare.

Yataghi found that after controlling for other state characteristics such as low per capita income population and average insurance premiums, he observed a positive association between the anti-ACA spending and enrollment:

This implies that anti-ACA ads may unintentionally increase the public awareness about the existence of a governmentally subsidized service and its benefits for the uninsured. On the other hand, an individual's prediction about the chances of repealing the ACA may be associated with the volume of advertisements against it. In the states where more anti-ACA ads are aired, residents were on average more likely to believe that Congress will repeal the ACA in the near future. People who believe that subsidized health insurance may soon disappear could have a greater willingness to take advantage of this one time opportunity.

[Sources: Kantar Media CMAG and the Brookings Institution]

How to read and understand a scientific paper: a guide for non-scientists

Before you begin: some general advice
Reading a scientific paper is a completely different process than reading an article about science in a blog or newspaper. Not only do you read the sections in a different order than they’re presented, but you also have to take notes, read it multiple times, and probably go look up other papers for some of the details. Reading a single paper may take you a very long time at first. Be patient with yourself. The process will go much faster as you gain experience.

http://violentmetaphors.com/2013/08/25/how-to-read-and-understand-a-scientific-paper-2/

Use the "Triple Nod" in Interviews

The triple nod is the non-verbal equivalent of the ellipses. It is a nonverbal cue for someone to keep talking. If you are introverted and aren't great at making conversations, you want to encourage the person you are speaking with to keep talking. Once they are done speaking and pause, nod three times in quick succession and they will often continue. If not, you can pick up where the conversation left off, but this is a great way of showing engagement and lengthening a discussion.

Publishers withdraw more than 120 gibberish papers by Richard Van Noorden

Conference proceedings removed from subscription databases after scientist reveals that they were computer-generated.

The publishers Springer and IEEE are removing more than 120 papers from their subscription services after a French researcher discovered that the works were computer-generated nonsense.

Over the past two years, computer scientist Cyril Labbé of Joseph Fourier University in Grenoble, France, has catalogued computer-generated papers that made it into more than 30 published conference proceedings between 2008 and 2013. Sixteen appeared in publications by Springer, which is headquartered in Heidelberg, Germany, and more than 100 were published by the Institute of Electrical and Electronic Engineers (IEEE), based in New York. Both publishers, which were privately informed by Labbé, say that they are now removing the papers.

Among the works were, for example, a paper published as a proceeding from the 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, held in Chengdu, China. (The conference website says that all manuscripts are “reviewed for merits and contents”.) The authors of the paper, entitled ‘TIC: a methodology for the construction of e-commerce’, write in the abstract that they “concentrate our efforts on disproving that spreadsheets can be made knowledge-based, empathic, and compact”. (Nature News has attempted to contact the conference organizers and named authors of the paper but received no reply*; however at least some of the names belong to real people. The IEEE has now removed the paper).

*Update: One of the named authors replied to Nature News on 25 February. He said that he first learned of the article when conference organizers notified his university in December 2013; and that he does not know why he was a listed co-author on the paper. "The matter is being looked into by the related investigators," he said. 

 

How to create a nonsense paper

Labbé developed a way to automatically detect manuscripts composed by a piece of software called SCIgen, which randomly combines strings of words to produce fake computer-science papers. SCIgen was invented in 2005 by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge to prove that conferences would accept meaningless papers — and, as they put it, “to maximize amusement” (see ‘Computer conference welcomes gobbledegook paper’). A related program generates random physics manuscript titles on the satirical website arXiv vs. snarXiv. SCIgen is free to download and use, and it is unclear how many people have done so, or for what purposes. SCIgen’s output has occasionally popped up at conferences, when researchers have submitted nonsense papers and then revealed the trick.

Labbé does not know why the papers were submitted — or even if the authors were aware of them. Most of the conferences took place in China, and most of the fake papers have authors with Chinese affiliations. Labbé has emailed editors and authors named in many of the papers and related conferences but received scant replies; one editor said that he did not work as a program chair at a particular conference, even though he was named as doing so, and another author claimed his paper was submitted on purpose to test out a conference, but did not respond on follow-up. Nature has not heard anything from a few enquiries.

“I wasn’t aware of the scale of the problem, but I knew it definitely happens. We do get occasional e-mails from good citizens letting us know where SCIgen papers show up,” says Jeremy Stribling, who co-wrote SCIgen when he was at MIT and now works at VMware, a software company in Palo Alto, California.
“The papers are quite easy to spot,” says Labbé, who has built a website where users can test whether papers have been created using SCIgen. His detection technique, described in a study¹ published in Scientometrics in 2012, involves searching for characteristic vocabulary generated by SCIgen. Shortly before that paper was published, Labbé informed the IEEE of 85 fake papers he had found. Monika Stickel, director of corporate communications at IEEE, says that the publisher “took immediate action to remove the papers” and “refined our processes to prevent papers not meeting our standards from being published in the future”. In December 2013, Labbé informed the IEEE of another batch of apparent SCIgen articles he had found. Last week, those were also taken down, but the web pages for the removed articles give no explanation for their absence.
Ruth Francis, UK head of communications at Springer, says that the company has contacted editors, and is trying to contact authors, about the issues surrounding the articles that are coming down. The relevant conference proceedings were peer reviewed, she confirms — making it more mystifying that the papers were accepted.
The IEEE would not say, however, whether it had contacted the authors or editors of the suspected SCIgen papers, or whether submissions for the relevant conferences were supposed to be peer reviewed. “We continue to follow strict governance guidelines for evaluating IEEE conferences and publications,” Stickel said.

 

A long history of fakes

Labbé is no stranger to fake studies. In April 2010, he used SCIgen to generate 102 fake papers by a fictional author called Ike Antkare [see pdf]. Labbé showed how easy it was to add these fake papers to the Google Scholar database, boosting Ike Antkare’s h-index, a measure of published output, to 94 — at the time, making Antkare the world's 21st most highly cited scientist. Last year, researchers at the University of Granada, Spain, added to Labbé’s work, boosting their own citation scores in Google Scholar by uploading six fake papers with long lists to their own previous work².

Labbé says that the latest discovery is merely one symptom of a “spamming war started at the heart of science” in which researchers feel pressured to rush out papers to publish as much as possible.
There is a long history of journalists and researchers getting spoof papers accepted in conferences or by journals to reveal weaknesses in academic quality controls — from a fake paper published by physicist Alan Sokal of New York University in the journal Social Text in 1996, to a sting operation by US reporter John Bohannon published in Science in 2013, in which he got more than 150 open-access journals to accept a deliberately flawed study for publication.

Labbé emphasizes that the nonsense computer science papers all appeared in subscription offerings. In his view, there is little evidence that open-access publishers — which charge fees to publish manuscripts — necessarily have less stringent peer review than subscription publishers.

Labbé adds that the nonsense papers were easy to detect using his tools, much like the plagiarism checkers that many publishers already employ. But because he could not automatically download all papers from the subscription databases, he cannot be sure that he has spotted every SCIgen-generated paper.

http://www.nature.com/news/publishers-withdraw-more-than-120-gibberish-papers-1.14763