Fake News

It’s 2017 and “Assessing the reliability of online information” is still relevant.

In 2012, Stephen Judd wrote a blog Is that so? – Assessing the reliability of online information”. This blog is an update to his post with a focus on fake news and social networks, especially since Facebook and Google are beginning to take steps to combat it.

Facebook and Google see themselves as technology companies, but critics see them as media conglomerates with the power to deliver fake and real news to most news consumers. The Pew Research Center states that 62 percent of U.S. adults get news on social media. Facebook and Google are taking the first steps in combating fake news by prohibiting advertising on sites found to “contain content that is illegal, misleading or deceptive, which includes fake news.” This reduces the fake news sites revenue, but perhaps the best way to fight fake news is to become more knowledgeable about detecting fake news.

Judd’s post list the C.R.A.A.P, (currency, relevance, authority, accuracy and purpose) test as a way to determine the accuracy of an information source. ABC News reports that 5 questions all journalism students learn can be applied to spotting fake news.

  • Who is telling the news?
  • What is the evidence?
  • Where did the information come from?
  • When was the information reported?
  • Why is the information being reported?

Additionally, we can become more media literate by following the advice of Melissa Zimdars and Alexios Mantzarlis. NPR summarized their best practices as:

  • Pay attention to the domain and URL
  • Read the “About Us” section
  • Look at the quotes in a story
  • Look at who said them
  • Check the comments
  • Reverse image search

There are a number of sites that regularly vet news stories and rumors, including, Snopes.com and Factcheck.org. Technology will not be able to detect all fake news, so it is imperative that we become more knowledgeable about detecting fake news ourselves. Sharing and perpetuating fake news stories can damage our personal and organizational reputations. Stopping the spread of fake news is not something that will happen overnight, but we must continue to be vigilant to not be duped by fake news.

Author: Terrence Wolfork (+Terrence Wolfork,@trwolfork )

This post was published on the Military Families Learning Network blog on January 23, 2017.

Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 Unported License.

Assumptions of basing our work on diffusion of innovation

In our last post, we asked you to think about why, as an adult educator, you do the things you do. We suggested that for many of us, especially those working in Cooperative Extension, the “why” was based at least in part in diffusion of innovation theory. Unfortunately, diffusion of innovation theory and the assumptions it leads to are causing us to fall short.

Most of the foundation of diffusion of innovation theory was established more than 50 years ago. In 1941 Bryce Ryan began studying how the innovation of hybrid corn, released in 1928, spread across Iowa. His 1943 study with Neal Gross showed that the adoption of hybrid corn began with a small number of farmers and diffused from there, implying that targeting innovative farmers to adopt innovation would speed up the adoption among all farmers (Stephenson, 2003).

The work of Ryan and Gross led to further studies, most notably by Everett Rogers, who developed the classic adoption curve and the categories of adopters in 1958.

Innovation Adoption Curve
Image by Jurgen Appelo, Flickr, downloaded 12/5/2016, https://flic.kr/p/8VBTUM

These categories and the resulting focus on innovators and early adopters have led to serious questions about Cooperative Extension’s reliance of diffusion of innovation theory, including Garry Stephenson’s question, “By Utilizing Innovation Diffusion Theory, have we caused harm in some way to the population we serve?”

Stephenson points out that a focus on innovators can widen gaps in equity. Innovators in agriculture tend to have higher incomes and larger operations than non-adopters. By marketing innovations first to innovators in hopes of influencing others, we may be widening the gap between the haves and the have-nots. In agriculture, non-adopters can be further hurt when bigger operations adopt innovations that increase yields which lowers crop prices.

The focus on innovators is especially concerning in light of the work of Duncan Watts and his colleagues suggesting that under most conditions, social change is driven not by “influentials” (opinion leaders) but by easily influenced individuals influencing other easily influenced individuals (Watts and Dodds, 2007) (Thanks to reader Kevin Gamble, @k1v1n, for pointing out Watts’ work).

Watts says a trend’s success depends less on the person who starts it and more on whether the conditions favor that trend (Thompson, 2008). Creating the right conditions is complex. What makes not just one person, but a majority of people ready for change? It’s complex, and our reliance of diffusion of innovation theory leads us to simplification.

Cooperative Extension tends toward a one-size-fits-all approach that better aligns with our reliance on mass media. If we think of our audience as a homogeneous group, all equally ready for change, we can rely on a single message delivered on a limited number of channels to reach them.

Relying on diffusion of innovation theory also simplifies how we look at problems. It leads us to assume problems can be solved by innovations, especially those devised by “experts,” and especially those “experts” at our land-grant universities. However, not all problems can be solved by innovations. Cooperative Extension is being called upon to help address “wicked problems,” complex social issues that cannot be “solved.”

As we said in our previous post, diffusion of innovation theory is implicit in the logic model which in turn guides our program planning. But, as Thomas Patterson pointed out, our planning model has “failed to result in programs capable of solving ill-defined, complex human problems where there’s disagreement on the desired outcomes” (Patterson, 1993).

We need new theories that support our work in the areas where diffusion of innovation leads us to fall short.

Readers suggest alternate theories

In addition to Kevin’s suggestion that we look at the work of Duncan Watts, readers of our last post also suggested Embracing Chaos and Complexity: A Quantum Change for Public Health (thanks Peg Boyles @ethnobot), the Unified theory of acceptance and use of technology (thanks Jared Decker @pop_gen_JED), Extension 3.0: Knowledge Networks for Sustainable Agriculture from UC Davis  and Adaption – Innovation Theory from Virginia Tech (both thanks to Jeff Piestrak @Jeff_Piestrak). Each of these theories/efforts can serve to complement (and sometimes contradict) the theory of the Diffusion of Innovation.

If you are familiar with any of these theories, or others, which do you think holds the most promise for Cooperative Extension?

Authors: Bob Bertsch (@ndbob), Karen Jeannette (@kjeannette), and Stephen Judd (@sjudd)

This post was published on the Military Families Learning Network blog on January 11,2017.

Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 Unported License.