Neat Science Thursday – Misleading headlines can affect science policy

If you’re in the US, you can probably tell by the deluge of unwanted political ads in your mailbox and voicemail that it’s election season. These ads (especially the printed ones) serve the very important function of wasting a resources because few of these ads are printed on 100% recycled paper, even if 100% of these ads (that I receive anyway) go straight into my recycling bin.

I don’t even look at these ads any more because I don’t expect any of them to pass a fact check and they are usually full of false or misleading information. I am NOT disappointed that so many of these ads are misleading, simply because I have really low expectations for information coming out of the political arena.

But I don’t have such low expectations of science journalism, which is why misleading science headlines really irk me!

FAST Co recently published a really great article by Eric Jaffe on some recent findings on how misleading headlines can leave lasting impressions–even if you read the article!

    “A misleading headline can thus do damage despite genuine attempts to accurately comprehend an article,” the researchers, led by psychologist Ullrich K. H. Ecker of the University of Western Australia, conclude in a new paper in the Journal of Experimental Psychology: Applied.

    Ecker and colleagues believe the big problem with misleading headlines is that they’re just that–misleading, as opposed to downright wrong. Correcting misinformation requires a lot of mental work. People are perfectly capable of doing that work once they recognize the need, but in the case of misleading headlines, that need isn’t always clear. After all, the misleading headline about genetically modified food is true in a very strict sense: the foods may possess long-term health risks, in the same way the world may end tomorrow. As the researchers put it, misleading headlines may have served to nudge behavior “without readers noticing their slant.”

    “[C]orrecting the misinformation conveyed by a misleading headline is a difficult task,” they write. “Particularly in cases of nonobvious misdirection, readers may not be aware of an inconsistency, and may thus not initiate any corrective updating.”

Even worse is that highly viewed misleading articles in the news often leads to other news sources giving similarly misleading reports in order to capture more attention. This creates a widespread impression that the misleading headlines are not actually misleading and helps cement the misinformation.

Case in point for the day:

Unfounded fears about Ebola transmission through the air are already making for really strange policy. We don’t need such wonky policies to go with genetic screens.

Now get out there and vote.

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Neat Science Thursday – More reasons to use twitter in science

In case you missed it, there was a great post on why you should use Twitter during your PhD on The Thesis Whisperer.

Excerpt:


    Twitter has changed the ‘stuffy’ image of academia. Twitter allows for opinions, debate, and input from others. The conversation that is stimulated is often thought provoking, and helpful when forming your own opinions, especially as a PhD student. It is not often that we are privy to the conversations between experts in our fields, or to their opinions. We often see only their rigorous scientific articles and presentations.

    Live tweeting from conferences, tweet chats, and the opportunity to share personal views has opened up the scientific community to greater interaction through an ongoing conversation. Twitter allows the opportunity to have your ear to the ground, and this is invaluable for a PhD student.

    Finally, Twitter is likely to play an even bigger part in our academic careers than many of us realize. This tweet from @AstroKatie is a testament to the new paradigm of academic impact “My supervisor wrote down huge Twitter presence: 7000 followers on my performance review. Social media outreach FTW!”

If you’re still unconvinced, check out the data sets and presentation slides from ASHG that were released via twitter:

And the jobs and post-doc position notices:

Need any more reasons?

Neat Science Thursday – Social Experiments over the Internet

As a fan of video games with a keen interest in human behaviors, I was fascinated by the Twitch Plays Pokemon social experiments set up on the video streaming site, Twitch. The programmer that designed this experiment streamed a game of pokemon and parsed actionable comments from the channel’s chatroom. The actionable comments (or commands) were then executed in the game allowing the crowd to essentially play the game. Although participation varied at times, the number of participants reached a whopping 1,165,140 giving Twitch plays pokemon red recognition by the Guinness World Records for having “the most participants on a single-player online videogame”.

In spite of speculation that the players would never reach sufficient consensus for each decision point and that trolls would never allow any progress to be made in the game, it took the participants about 16.5 days to finish the game. Red may have spent a lot of time walking into corners, or jumping off ledges, but eventually he made it to the finish line. Twitch plays pokemon series offers a fascinating look at how users organize themselves, contribute to, and alter the landscape surrounding the game. Memes (like Consult Helix) were born and several pokemon became religious icons.

twitch plays pokemon

Of course, in the case of Twitch plays pokemon, the players were actively engaging in that social experiment, and twitch users were not automatically included unless they engaged in the series. In most cases, anyone using the internet is unwittingly taking part in social experiments.

As pointed out in a recent techreview article:

    “When doing things online, there’s a very large probability you’re going to be involved in multiple experiments every day,” Sinan Aral, a professor at MIT’s Sloan School of Management, said during a break at a conference for practitioners of large-scale user experiments last weekend in Cambridge, Massachusetts. “Look at Google, Amazon, eBay, Airbnb, Facebook—all of these businesses run hundreds of experiments, and they also account for a large proportion of Web traffic.”

 
To no one’s surprise, there was outrage when facebook users discovered they were unwittingly taking part of a social experiment, exhibiting:

    how few people realize they are being prodded and probed at all.

    “I found the backlash quite paradoxical,” said Alessandro Acquisti, a professor at Carnegie Mellon University who studies attitudes towards privacy and security. Although he thinks user experiments need to be conducted with care and oversight, he felt the Facebook study was actually remarkably transparent. “If you’re really worried about experimentation, you should look at how it’s being used opaquely every day you go online,” Acquisti said.

The internet has made it much easier to conduct and take part in social experiments–so easy that we’re participating just by going online whether or not we like it! To be fair, the experimenters aren’t limited to nameless faces behind big companies…anyone can get in on the action! In one case, a photographer sent her picture to 40 different amateur photoshop retouchers from 25 different countries using an online task crowdsourcing site called Fiverr. The result revealed interesting variations on how individual retouchers from across the globe defined beauty; however, it would be hard to draw any conclusions given that few studies have been done on the population that participates on Fiverr.

Microtask sites like Amazon Mechanical Turk are also getting into the game. Fortunately there is an effort to learn more about the turkers in this case. In fact, there’s an entire blog dedicated to social science experiments conducted on Amazon Mechanical Turk, leading to a very interesting post about the Amazon Mechanical Turk as the new face of behavioral science.

I wonder how that photoshop experiment would have gone if done using Amazon Mechanical Turkers, especially since AMT allows for prequalification testing of the potential worker pool.

Should people browsing on the internet harbor any hope for privacy or exclusion from being unwittingly used or is that already an illusion of the past?

Neat Science Thursday – Perpetuating Pseudoscience

The myths touched on in a previous Thursday post are extremely persistent, but aren’t usually very harmful. In contrast, the pseudoscience perpetuated by many supplement peddlers, like Dr. Oz, can have serious adverse effects. Hence it was no surprise that many in the scientific community applauded the congressional grilling of Dr. Oz, and John Oliver’s informative yet bleakly entertaining follow up.

While John Oliver did an excellent job highlighting the issue, no one actually believed that something would be done about the use of pseudoscience in selling dubious dietary supplements.

Proving everyone right, Business Insider just published an article on a new trendy supplement–Activated Charcoal:
With the change of the seasons (in this case, summer to fall), it always seems like people feel the need to detox their bodies.

A lot of the recent detox buzz is around activated charcoal. Charcoal, however, isn’t really new – it’s been around since long before the 19th century, when both the ancient Egyptians and Greeks used it as a multi-purpose poison and disease antidote.

Today, it’s most commonly used in emergency settings to treat accidental poisonings or drug overdoses. Well, that’s until someone decided it would make a great supplement for a detox program, anyway.

Seriously? Activated Charcoal as a dietary supplement? Disturbing. And just when you though the pseudoscience nonsense can’t get any worse, it turns out that Consumer Reports has may be pandering to Dr. Oz too, adding to existing concerns about Consumer Reports steady slide into promoting pseudoscience.

The Genetic Literacy project received a memo regarding Dr. Oz’s visit to Consumer Reports from a former senior editor who parted ways with CR after getting “fed up with the woo”. ‘Woo’ refers to pseudoscience, magical thinking, or quackery.

Neat Science Thursday – Scientific communication is hard

For my dissertation research, I studied the mechanisms of viral persistence in the central nervous system using an in vitro model consisting of picornavirus infections of differentiated and undifferentiated murine neural progenitor and stem cells.

To some of my non-science friends and family, that explanation sounds something like, “I studied blah blah blah, miscellaneous pretentious scientific jargon, blah blah.”

Of course, they would still have no idea what I studied, and any further attempts to explain would be met with that kind, patient look that people give you when they’re bored but are too polite to walk away.

So I would tell non-science people that I studied “how a virus hides in the brain for a long time, and the consequences of having a virus hiding in the brain using mouse stem cells”.

This explanation will elicit much more interest, but can sometimes get distorted when described second-hand to others since it’s not as accurate. Also, there may be some fellow scientists who will think you lack professionalism, or don’t know what you’re talking about if you use this sort of description.

If you think about it, there is so much jargon specific to each field of study, it’s actually pretty impressive how well scientists have been able share information across disciplines. Of course, there is always room for improvement in that arena, especially since researchers primarily disseminate their findings via scholarly articles, keying them for easy search amongst fellow researchers in their discipline. The differences in jargon sets contributes to the difficulty in finding research articles outside your field of research which contain research findings that could have important impacts in your own work. It’s also why there has been a lot of interest in annotating research literature and making it more searchable (Go! Go! Mark2Cure) as well as creating an open database where information contained in research literature can be formally structured around a set of shared ontologies (Go! Go! Wikidata.

It’s also great that these findings are becoming more available as funding agencies help to drive open access policies enabling the public to finally access the research paid by tax/donation dollars. But open access does not mean accessible if we consider the sheer volume of jargon that must be learned in order to read and understand scholarly articles. How can we expect tax payers to support research if they are locked out on so many levels? Now before you argue that ‘people need to take personal responsibility for their own education’ or ‘the articles aren’t really hard to read if you actually put in the effort,’ ask yourself this, ‘Am I dismissive of people science communicators that communicate science via non-traditional channels (ie- non-academic journals)?’ If your answer is ‘yes’, you waive all rights to complain about the scarcity of research funding.

If your answer is ‘no’, consider contributing your expertise to a truly accessible knowledge-base/medium like Wikipedia. Efforts are already under way to make information on every human gene of interest publicly available on Wikipedia (the Gene Wiki initiative), and greater participation is needed from scientific community.

For those of you, researchers or not, who ARE able to communicate science with such elegance, enthusiasm, poignancy, and precision–especially those of you on non-traditional channels like Science 2.0, twitter, etc.–thank you for using your talents to engage the public so they can see how their money is being spent. You are awesome! The rest of us will just have to keep trying.

Neat Science Thursday – Too Much Information

Personalized medicine has been a goal for a growing number of biomedical researchers over the last twenty years. Considering the fact that biomedical research literature on personalized medicine has grown from 5-10 articles/year in the 1980’s to over 2500 articles per year since 2013, incredible progress has been made towards this extremely challenging goal. For personalized medicine to happen, at least two elements are necessary: 1. A means of acquiring personalized data is needed, and 2. A means of integrating, analyzing, and applying that data. The explosive improvements in the amount, quality, efficiency, and cost-effectiveness of obtaining personalized data , creates a huge challenge in the integrating, analysis, and application of that exponentially growing body of data available. Thus, the challenge of personalized medicine primarily lies in the integration and analysis of ‘Big Data.’ Yes, there is always room for improvement in data acquisition, but all the growing data is problematic if it cannot be effectively utilized. Researchers from all over the world have been working on analysis tools in order to better extract useful information from the growing available sets of omics data, and [Warning: shameless plug alert] The Su lab’s Omics Pipe is one attempt to automate the best practice multi-omics data analysis pipelines. [End shameless plug]

Barbour Analytics published a fascinating post on the N-of-1 problem in Big Genomics. It is a great read for anyone interested in personalized medicine, rare diseases, big data, and bioinformatics. Here is a teaser to encourage you to take a look at the original post:


    How do we assess the impact of a single novel mutation, or a set of novel mutations, unique to an individual? This is the N of 1 problem in Big Genomics. Statistics, and statistical genetics rely on summary, on binning the patterns of populations of individuals into categories of adequate size that we can compare groups using standard metrics like mean, median, mode, standard error, and in more elaborate frameworks use more sophisticated metrics like moments, edges, vectors and ridges.

    The N of 1 problem in Big Genomics will require modeling approaches, to construct models of the genome, and make projections on the likely function of single de novo mutations, and suites of these private mutations. Robust modeling efforts in this area will be a major challenge in the era of genomic medicine, and personalized medicine. At present we are effectively constrained to study mutations that have recurred throughout evolution. As our population grows, as the number of persons under care, and participating in genomic medicine increases, we will need to address the private mutation issue head on.

    We can look to cancer genomics for some guidance. Cancers are a genomic disease, with both inherited and de novo elements, and direct sequencing of genomes often reveal unique mutations that lead to unique cancer profiles. This field has the advantage of seeing a clear disease manifestation in the form of tumor growth, often restricted to a tissue or cell type. This helps make more direct inferences about the likely function of the novel mutation.

    That said, we face the inherent limitation that a mutation may be unique, or at least rare, and for this reason it is difficult to use traditional statistical approaches, approaches that rely on summary, on the behavior of groups of instances. While the genotype information may be limited to one person in these instances, we can assist ourselves in this effort by capturing more information about clinical and biological phenotype. Detailed phenotypic characterizations of a tumor or affected tissue – extending to the transcriptome, kinome, metabolome, cytokine profiles, cell morphology and indeed clinical status itself, can help us perform a sort of reverse interpolation to infer the function of the single N of 1 de novo mutation. While the mutation may be unique or rare, the disease manifestation itself may be common, or at least share key features with other maladies.

Now go read the original post here: ‘Private Mutations’: The N of 1 in Big Genomics — Barbour Analytics.

Neat Science Thursday – Science communication matters

Tweets about science flooded twitter feeds again yesterday when Science released its list of the top 50 scientists on twitter and their corresponding K-index. If you haven’t heard of the K-index, you’re probably not on very high on it. The top 50 list received an enormous (and rather negative) response in the twittersphere. Why? Aside from obvious flaws of only relying on twitter followers and neglecting to include a lot of great female scientists, the top 50 list revived the reviled K-index!

Scientists that use twitter already took the k-index out of its self-imposed isolation, slapped it with a failing grade as a joke, highlighted its egregious mistakes, directed it to use real data before injecting it with a lethal dose of reality and leaving it to die. Many scientists that use twitter lambasted the K-index because they knew how useful it could be especially for sourcing articles (#icanhazpdf anyone) that are NOT open access and for outreach.

Contrary to popular belief, many scientists spend a lot of energy and effort in outreach because they understand and acknowledge the importance of scientific communication. Don’t think science communication is important? Consider how it affects the parent’s decision on whether or not to vaccinate a child. Science communication isn’t exactly easy either.

Don’t do enough of it (it being sci-comm, of course), and someone else will step in with grossly misleading headlines on how your research shows that aspirin cures cancer. Do too much of it, and fellow scientists will disrespect you and remind you that your place is back at the bench (K-index anyone). Do it poorly and you will have the same results as not doing enough, or you will be ignored to the point that when someone confirms your findings it will look completely like a huge discovery rather than an incremental one.

Those in science that are able to communicate science with elegance, enthusiasm, poignancy, and precision are praiseworthy. Hats off to those of you who do it so well–that means you, GeneoTW writers, Science 2.0 and other science bloggers. You are all awesome! For the rest of us, we’ll just have to keep trying.

Now go back to tweeting great science, people.