Group items tagged

Over recent years, data has become an indispensable source for journalists and news organisations, providing excellent material for investigative work as well as storytelling. This has led to the emergence of data journalism, which, broadly speaking, uses information science and analytical techniques in conjunction with journalistic workflows to produce compelling stories rooted in data. Despite the relative maturity of data journalism and the growing application of data in editorial workflows, there is still a lot to learn about the systematic, seamless and effective integration of data and computational tools in newsrooms. It is time time for a holistic assessment of this emerging field by looking deeply into the ways newsrooms across the world have adopted data in their day-to-day workflows, the formation of their data teams, their best practices for producing high quality data driven investigative work, their success and failure stories, and emerging training requirements.

'Significance' is a magazine published by the UK Royal Statistical Society and American Statistical Association. E.g. this article discusses the data we might use to analyse food security, thinking about what sources of data are available and the questions they might help us answer. "data on food insecurity is biased towards the environment in which it was created and the priorities of those who collect or commission it. Data from schools is concerned with reimbursement; government data might be focused on budgetary constraints or accountability; grocery stores could (if willing) tell us what food is bought, but not how it is used; meanwhile, non-profits are most interested in demonstrating impact to funders. There is a wide variety of data sets available, but very few are created with the intent to understand the real drivers of hunger and poverty. The data may be repurposed, but modellers must be especially careful to moderate the assumptions of each data set. ...... It is also especially important that those experiencing hunger and poverty are consulted when designing any data analysis project. The input of domain experts is crucial to the success of data science endeavours, and those experiencing poverty know the right questions to ask."

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

But because a machine learning model learns the boundaries of its world from its input data, just three people informed how any model using that dataset would interpret if 'childbirth' was emotional. This led to a perspective that has informed all of my work since: a dataset is a worldview. It encompasses the worldview of the people who scrape and collect the data, whether they're researchers, artists, or companies. It encompasses the worldview of the labelers, whether they labeled the data manually, unknowingly, or through a third party service like Mechanical Turk, which comes with its own demographic biases. It encompasses the worldview of the inherent taxonomies created by the organizers, which in many cases are corporations whose motives are directly incompatible with a high quality of life.

In 1967, following riots in Detroit, Philip Meyer used survey research methods, powered by a computer, to show that college-educated people were just as likely to have rioted as high school drop outs. His story was one of the first examples of computer assisted reporting and precision journalism, in which journalists use social science methodologies to extract and tell stories. In recognition of his contribution to the area, each year's best computer-driven and precision stories are celebrated through the Philip Meyer Journalism Award. The Award's 2016 winners have just been announced, with the successful entries showcasing techniques derived from quantitative and qualitative methods, such as surveys using randomly-selected respondents, descriptive and inferential statistical analysis, social network analysis, content analysis, field experiments, and more.

Podcast from UTS Think:Digital Futures (that I briefly appear on). Sometimes we get caught up thinking data and science are the be all and end all - it can give us a lot of answers sure, but the devil is in the detail. How are we interpreting data wrong? And why do we have trouble communicating it? Presenters/Producers: Cheyne Anderson & Ellen Leabeater Speakers: Jon Wardle - Faculty of Health, University of Technology Sydney Simon Knight - Lecturer, Connected Intelligence Centre, UTS Mark Moritz - Associate Professor, Department of Anthropology, Ohio State University Think: Digital Futures is supported by 2SER and the University of Technology Sydney. http://2ser.com/shows/think-digital-futures/

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

Here's a discussion of how data science techniques that look at the kinds of things you watch, and try and make recommendations or customise based on that can work, and what might be problematic about that. "Some are noticing Netflix's tendency to entice black users with movie posters featuring black actors, no matter how minor their role in the film."

In August 2016, the Australian government released an "anonymised" data set comprising the medical billing records, including every prescription and surgery, of 2.9 million people. Names and other identifying features were removed from the records in an effort to protect individuals' privacy, but a research team from the University of Melbourne soon discovered that it was simple to re-identify people, and learn about their entire medical history without their consent, by comparing the dataset to other publicly available information, such as reports of celebrities having babies or athletes having surgeries.

The eBook contains 16 short stories about brilliant Australian research data projects that have led to real-life impacts for Australia and beyond. It is intentionally very punchy and image-led. The stories were collected during ANDS' #dataimpact campaign which ran through 2016.

There's a huge debate going on in social science right now. The question is simple, and strikes near the heart of all research: What counts as solid evidence? The answer matters because many disciplines are currently in the midst of a "replication crisis" where even textbook studies aren't holding up against rigorous retesting. ...

Alcohol and Other Drug (AOD) Media Watch is based on the same premise as the ABC show Media Watch. It aims to highlight poor examples of journalism regarding AOD-related issues in the hope that we can assist journalists to report more objectively using science and evidence rather than perpetuating myths, opinions and moral panic. Research has found moral panics in the media can actually be detrimental. Moral panics in the media can actually be detrimental by counter-intuitively leading to increased drug use since it increases the perception that more people are using the drug than actually are. It has also been found to found that moral panics reduce the degree to which some people believe that the drug being reported on is harmful. It also reduces the credibility of AOD information in the media.

Last month, CNBC reported on a page in Google's account settings titled "Purchases" - a month-by-month list of items you've bought across online services like Amazon and other apps that are collected via Google services like Gmail. Purchases is a jarring example of how leaky our data really is and how large companies can aggregate that information unbeknown to the consumer. I, for one, was unaware that almost every concert ticket, Domino's pizza and Amazon purchase (including a 2014 accidental purchase of the film "Tango & Cash") was being logged by Google. Equally troubling: The purchases can't easily be deleted from the page without also deleting the receipt emails from your Gmail account.

Data aren't dead, contrary to what some pundits stated post-election [2], rather the limitations of data are not always well reported. While pollsters will be reworking their models following the election, what can media journalists do to improve their overall coverage of statistical issues in the future? First, discuss possible statistical biases, such as errors in sampling and polling, and what impact these might have on the results. Second, always provide measures of uncertainty, and root these uncertainties in real-world examples.

some examples of how the 3 types of machine learning - supervised, unsupervised, and reinforcement - have already been used for journalistic purposes, and using those to explain what those are along the way. Examples include: supervised learning to investigate doctors and sex abuse; unsurprivsed learning to identify motifs in Wes Anderson films; reinforcement learning to create a rock-paper-scissors that can beat you...

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives - and our futures. The world in 2020 has been given plenty of reasons to be wary of algorithms. Depending on the result of the U.S. presidential election, it may give us one more. Either way, it's high time we questioned the impact of these high-tech data-driven calculations, which increasingly determine who or what we see (and what we don't) online. The impact of algorithms is starting to scale up to a dizzying degree, and literally billions of people are feeling the ripple effects. This is the year the Social Credit System, an ominous Black Mirror-like "behavior score" run by the Chinese government, is set to officially launch. It may not be quite as bad as you've heard, but it will boost or tighten financial credit and other incentives for the entire population. There's another billion unexamined, unimpeachable algorithms hanging over a billion human lives.

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation's series on the science of free will.

Cool study showing the potential of data analyses to give us new kinds of insights! What does instagram tell us about peoples' nutrition decisions? Food deserts, or places where people have limited access to fresh food, are usually measured by the distance people have to travel to get to a large grocery store. What's harder to measure is what the residents of these areas are actually eating day to day. To do so, researchers typically have to rely on surveys, ...In a recent study, De Choudhury and her colleagues propose another method: mining Instagram. All those artfully arranged plates, all that latte art, just waiting for someone to analyze it!

Adrienne is a Marvel movie fanatic: Her favorite films all involve the Hulk, Thor or Black Panther. Brandon prefers animated features like Inside Out, The Incredibles and anything with Buzz Lightyear. I like both kinds, although I'm probably closer to Adrienne than Brandon. And I might skew a little toward Cora, who loves thrillers like Get Out and The Shining. Whose movie preferences are closest to yours: Adrienne's, Brandon's or Cora's? And how far are your cinematic tastes from those of the other two? It might seem strange to ask "how far" here. That's a question about distance, after all. What does distance mean when it comes to which movies you like? How would we measure it?

We learned this week that the Trump campaign may have tried to dissuade millions of Black voters from voting in 2016 through highly targeted online ads. The investigation, by Channel 4, highlighted a still little-understood online advertising technique, microtargeting. This targets ads at people based on the huge amount of data available about them online. Experts say Big Tech needs to be much more transparent about how microtargeting works, to avoid overblown claims but also counter a potential threat to democracy.