1 Opening Concepts

by Dave Bostwick

CURTAIN-RAISER

You’ll be reading some of my personal perspectives in this OER text, so I want to raise the curtain and introduce myself by taking you back to the 1980s when I first attended college. Below is a photo of me singing from way back then (when I had a lot more hair).

Like a few of my peers, I watched a lot of MTV, which made me want to become an international rock star more than a news journalist. In less than a decade, my generation went from 8-track tapes to cassette tapes to compact discs (CDs). Of course, there was no internet for consumers. Small-screen personal computers were a clunky novelty, high-quality stereo speakers were huge, and it would have been impossible for us to imagine a streaming service like Spotify.

I spent my childhood in a rural area of Oklahoma, and my family used a directional TV antennae to pick up one station at a time. For example, if you wanted to watch shows broadcast on a TV station in Wichita Falls, Texas, you had to point the antennae toward Wichita Falls. I remember moving to a larger city and getting a cable TV box connected to a controller with a physical cord. At the time, it seemed almost unbelievable that my roommates and I could sit on the couch and change the TV channel without getting up to turn a control knob.

Regardless of your age, you probably have memories of how media consumption habits have changed in your lifetime. This Open Educational Resource (OER) text will explore how mass media technologies have evolved over several centuries. We’ll also look at how mass media affects users (and vice versa), paying special attention to ethical concerns.

OPENING CONCEPTS

This chapter includes several interactive exercises to introduce some key concepts. Here’s your first flip-card question.

As a current example, it’s an oversimplification to refer to a singular idea of media as liberal or conservative. No single entity controls American media, so it’s best to think of the word media as plural. The U.S. media landscape is extremely diverse in content and ownership. It is not a singular concept, and it includes a variety of liberal and conservative outlets.

Whenever possible, try to be specific about the type of medium or media outlets that you discuss.

MEDIA LITERACY

Many of today’s media critics complain that social media platforms devalue accuracy and make it difficult for consumers to gain reliable information. Several chapters will mention this and similar concerns about media literacy.

Here’s a warm-up question to help you think about whether this is a new or old problem.

Franklin wasn’t alone among our founding fathers. John Adams wrote in his diary about “Cooking up Paragraphs” and “working the political Engine!”

The following 2018 audio from National Public Radio details the history of fake news in American journalism, including an example from Pea Ridge, Arkansas.

One could say that “making stuff up” is an American tradition. It certainly didn’t start with the 2016 and 2020 elections. Here are some additional resources about the history of fake news in the United States.

• Fake News? That’s a Very Old Story. – The Washington Post
• Fake News, Real News: History of Fake News – Middlesex College
• An Independence Day Salute to Our Founding Fathers of Fake News – Thom Fladung of Hennes Communications

THE EVOLUTION OF MEDIA TECHNOLOGY

This OER text will emphasize that, when it comes to disruptive technology, media history repeats itself. Here is a media history question that may give you some perspective on that concept.

Creative Commons Image

The contraption in the picture above is a telegraph, used to send coded messages electronically beginning in the 1800s. If you are a history buff, you can read the History Channel’s explanation of “Morse Code & the Telegraph.”

Next comes a question about image manipulation.

As an example, compare the two images below. In the 1940s manipulated photo on the left, it appears no one is holding the reins of a horse ridden by Italian Prime Minister Benito Mussolini. In the original photo on the right, we see there actually was someone handling the horse while Mussolini posed.

Photoshop was not developed until the 1980s, and this is one of many famous examples of image manipulation that came before digital photography.

You can peruse lots of interesting image-manipulation case studies, starting with an 1860s example involving Abe Lincoln, in the following document from Georgia Tech University.

• Photo Tampering Through History

MEDIA TECHNOLOGY IN THE 2020s

Here’s a poem about a special dog, adopted from an animal shelter, who is one of my best friends.

Harvey the Mutt, a dog of mixed breed,

With a wagging tail, and a heart full of need.

Though not a pedigree pup, you see,

His loyalty and love are pure and truly free.

He’ll be your companion, through thick and thin,

Harvey the Mutt, a true friend within.

You may be wondering why this poem appears in our OER text as part of this chapter. Here’s a flip card  that may explain that.

Content-creation tools using artificial intelligence, such as ChatGPT, are disruptive media technologies in the 2020s. If media history is a guide, we can predict that most people will adjust and adapt to the new technology. It will lead to the elimination of some jobs and the creation of others.

However, our media landscape will forever be changed, much like it was with the advent of the printing press, the telegraph, radio, movies, television and the internet.

IS ARTIFICIAL INTELLIGENCE REALLY BULL*#*#?

You may be surprised to learn that there is an academic definition of the term bullshit based largely on the pioneering work of a Princeton professor, Harry Frankfurt, who wrote an influential book titled On Bullshit.

Three academicians at the University of Glasgow in Scotland applied Frankfurt’s principles to publish a research work titled “ChatGPT Is Bullshit.” They argued that, rather than hallucination, the more appropriate term to describe the output of large language models, such as ChatGPT, is bullshit. Here is part of their argument:

The problem here isn’t that large language models hallucinate, lie, or misrepresent the world in some way. It’s that they are not designed to represent the world at all; instead, they are designed to convey convincing lines of text.

In the audio below, one of the co-authors, Joe Slater explained the challenges of truth and verification related to text generated by large language models such as ChatGPT.

Audio: Truth and Verification

(transcript of the audio)

Slater also provided a brief warning about unintended plagiarism resulting from text generated by AI-generated text.

Audio: Unintended Plagiarism

(transcript of the audio)

CONCERNS ABOUT AI

The argument from the Scottish researchers above is one of many evolving viewpoints about AI-generated content, which likely will be both reviled and embraced by future media professionals and educators.

If you are a student in academic courses, I strongly recommend that you consult your instructor before using any AI tools to complete assigned work. This will help you avoid a breach of academic integrity.

Accuracy is frequently a concern. In a 2023 research study conducted by the startup company Vectara, AI chatbots invented information at least 3 percent of the time. In some situations, that number climbed as high as 27%.

Also, research suggests that consumers often cannot tell the difference between authentic online reviews and ones produced by AI. This can contribute to a media landscape in which we become skeptical of everything we see.

Consumers face similar challenges with judging the authenticity of online photos and videos. You can quiz yourself with the following interactive exercise from The New York Times:

• A.I. Is Getting Better Fast. Can You Tell What’s Real Now?

WHY YOU SHOULD EMBRACE AI

On the other hand, many media professionals already use AI tools to complete tedious tasks and sift through data, so it’s a good idea for you to experiment with AI tools now.

For example, there are a limited number of ways to phrase a brief promotional description of a typical apartment with two bedrooms and two bathrooms. As long as the advertising pitch for the apartment is accurate, readers may not care if it is generated by an AI tool.

Despite concerns about accuracy and ethics, future media professionals will benefit from understanding how artificial intelligence is used to generate text, audio and visual content. Plus, the amount of hallucination (or bullshit) may decrease as AI products evolve.

TWO CLOSING JOKES (an experiment with ChatGPT)

One drizzly Friday in 2023, my collaborator on this textbook project, Alyssa Riley, found some online jokes to share with a few students who needed a bit of comic relief. Here are two of the jokes:

JOKE 1 – What did the police officer say to his belly button?
You’re under-a-vest

JOKE 2 – How do you get a country girl’s attention?
a-tractor

As an informal experiment, Alyssa then asked ChatGPT (free version) to answer those two questions in the context of a joke.

ChatGPT provided a verbatim punch line on the first joke, meaning that the language used for Joke 1 was stored someplace that ChatGPT could locate and replicate.

ChatGPT’s response on the second joke, however, demonstrated how a large language model (LLM) can generate odd or unexpected content in ways we do not fully understand.

JOKE 2 – How do you get a country girl’s attention?
(ChatGPT Answer) Play a tune on your banjo and ask her if she wants to two-step with you under the stars, but make sure to bring a sweet tea and a pickup truck – because nothing says romance like a tailgate serenade!

I’ll admit this was just an amusing experiment with an AI tool. Consider, though, the broader implications when an AI tool generates a similarly random response in a query for specific medical or financial advice instead of the punchline to a joke.