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Data Literacy in the Era of “Fake News”

Shevon Desai, University of Michigan

MiALA Data Literacy Bootcamp

March 8, 2019

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What are we talking about?

Data literacy

Ability to read and write data
Data - numbers and a lot of other stuff

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What are we talking about?

“Fake News”

Different things to different people
Ranging from completely, deliberately false to flawed, inaccurate information
Exists within ecosystem of mis- and disinformation

So what are we talking about when we say data literacy and fake news?

Fake news - in quotation marks because don’t believe this is a meaningful term
Means many things to many people, and worth iterating what those are

False news stories - deliberately made up with no truth and no verifiable facts
Stories with inaccurate information
Information that is deliberately slanted or manipulated to influence opinion
Stories with misleading headlines designed to entice clicks or elicit emotional response
“Fake news” is also a term used to discredit a source you disagree with
Fake news exists within a larger ecosystem of misinformation and disinformation. MISINFORMATION (false or inaccurate information that may be inadvertently spread); DISINFORMATION (deliberately spread for some specific purpose - to sway opinions, for financial gain, to entertain, etc.)

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From Claire Wardle, FirstDraft https://firstdraftnews.com/fake-news-complicated/

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Changing nature of the news

24 Hour News Cycle
Democratization of News Production
Technological Advances
Technological Manipulation
Changing Economy of the News Media
Economy of the Internet
Blurred Line Between Facts and Opinions
Lack of Public Trust in the News/Other Institutions
The News Comes To You

News environment more generally - changing nature of news

From news orgs, pressure to fill 24 hr cycle; with online outlets - pressure to publish things ASAP, may not spend as much time on things like fact checking.

Also - what is news? Citizen journalists - publish on their own. Or social media gets picked up or reported as news. Example of Twitter photo of buses outside campaign rally. Was posted on social media - individual who made an assumption and posted what he thought was true, that got shared by thousands.

Fact/opinion - used to be easier when all in print and could see what section of the paper an article was from. Now opinion may get separated from “section” - sign of responsible news outlets that are more clearly identifying what is news vs opinion. Cable news - employ “pundits” - they are different from reporters. More transparency is being pushed.

Younger adults haven’t grown up reading a print newspaper, don’t even have benefit of having that expectation of the section heading - used to seeing articles without context; taken out of context. Context important with ALL types of info, one thing emphasized in class.

Data journalism is a thing and has been a thing for many years now.

Struck by report on how news alerts now seen by journalists as a standalone platform (from Tow Center, Columbia Journalism School)

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https://medium.economist.com/data-visualisation-from-1987-to-today-65d0609c6017

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Modern data visualizations are complex

Presented to a non-expert audience but accompanied by very lengthy description of methodology, analysis, implications

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Flexing the data literacy muscle

How do we help students develop data literacy skills - practical strategies:

Do the visuals make sense, or are they designed to elicit an emotional response
Causation ≠ Correlation
Keep an eye out for the y-axis
What is being counted? Where do the numbers come from?

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Flexing the data literacy muscle

How do we help students develop data literacy skills - practical strategies:

Is it really a big number? (statistical benchmarks)
Apples vs oranges
Where does this data come from? What is being counted?
Is it too good to be true?

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http://viz.wtf/post/182530727057

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How about this one? To illustrate lack of context - no big picture. What are total or average number of engagement behaviors for articles? How was mainstream news defined? Is it small number of users who are doing most of this engaging with fake news articles? (from Nov 17, 2016, just after election)

Just to be clear, kinds of discussions we are having now are also what we can do with our students
Modeling the kind of open conversation that can have with students
One takeaway from fake news class is that students need the space (literal and figurative) in which they can have the honest conversations, time to mull over what is being presented and said
Important thing to remember is that cannot divorce ourselves, our identities/worldview from the process - there is emotion involved (Bluemle article)

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Statistics (get the big picture!)

If we hear them repeated over and over, we start to believe them

Who created this statistic
Why was it created
How was it created

Joel Best, “Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists” p. 24 (2012, University of California Press)

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A few points about teaching data lit and news

Start with the obvious/more easily understood
Data can be personal
Use low-stakes examples
Leave room for conversation

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Group Activity (~10 min)

If you’ve got data/tables

Decide what data you want to present and how (prototype it - be creative!)

Questions:

Who are you
What story are you trying to tell

If you’ve got a statistic/claim/data point

Questions:

Where did this number come from?
What is the big picture?

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Acknowledgements

Hailey Mooney, Angie Oehrli - co-developers of Fake News, Lies and Propaganda (1 credit course on fake news)

Angie Oehrli and Kristin Fontichiaro, co-PIs of Creating Data Literate Students grant

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Additional resources

U-M Library fake news research guide
Fake News, Lies and Propaganda: the Class - lesson plans, course curriculum
https://firstdraftnews.org/fake-news-complicated/
Bluemle, S. R. (2018). Post-Facts: Information Literacy and Authority after the 2016 Election. portal: Libraries and the Academy 18(2), 265-282. Johns Hopkins University Press.
Creating Data Literate Students (IMLS grant site)
Data Literacy in the Real World : Conversations and Case Studies – discussion guides and activities
Best, J. (2012). Damned lies and statistics : Untangling numbers from the media, politicians, and activists.
https://www.statisticshowto.datasciencecentral.com/misleading-graphs/
WTF Visualizations http://viz.wtf/

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Thank you! Questions?

Shevon Desai

shevonad@umich.edu

Slide deck: http://tinyurl.com/yxujr7ty