1.2: Old to New Media
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Social media have evolved through human cultural practices along with technological affordances.
Key points
- Social media evolution bridges cultural practices and technological changes.
- Blending old and new technologies is evident, such as the iconic shutter sound of digital cameras.
- Diverse methods, beyond traditional media, have been historically used for broad communication.
- User-generated content challenges the traditional one-way flow of information.
- Major platforms like Google and Facebook profoundly influence industries, local economies, and cultures.
- Web 3.0, driven by Tim Berners-Lee’s mission, aims to give users control over their data, challenging the current paradigm of data ownership on social networks.
In this chapter
- Section 1: Media convergence
- Section 2: The history of communicating with many at once
- Section 3: A millennial shift: Web 2.0 as user contributions
- Section 4: Dominating today: The platform economy
- Section 5: Future directions in the online world
- Virtuality 3.0: SXSW in Austin, TX — Social Media and Ourselves podcast
- Core Concepts
- Core Questions
- How will AI affect workers? Tech waves of the past show how unpredictable the path can be
An interactive H5P element has been excluded from this version of the text. You can view it online here:Section 4: Dominating today: The platform economy
…we are in the middle of a contest to define the contours of what we call the “platform society”: a global conglomerate of all kinds of platforms, which interdependencies are structured by a common set of mechanisms.”
– José Van Dijck and Thomas Poell, Social Media and the Transformation of Public Space. Social Media + Society, July-December 2015: 1.
Human-to-human connection is what social media is supposed to be about. This belief, this hope, was an impetus for this book when I began writing it in 2016. Historically, the human-to-human connection was also what the internet itself reached for, at least in the dreams of its creators. This Web 1.0 or the “read-only” web as it would later be called was quite limited in its reach compared to today. And yet…that potentially infinite web of networks was still a wonder, and a site of international connections and information wars (as you’ll see in Chapter 5 with the Zapatistas).
Then what happened? Well on the surface, the web simply became more social. By the early 2000s with Web 2.0 and the “read/write web,” great excitement and euphoria surrounded the participatory cultures that blossomed on Web 2.0 sites. The wonder of the web refracted across our lives, as we marveled at how easily we could connect with one another. This world of connections broadened our human imaginations and expectations in irreversible ways. And many were overjoyed when, by 2009, all this human connection grew teeth – which is to say viability in the form of real currency exchange – with the “sharing economy” that enabled regular folk to share services and goods with one another. Platforms that began as tiny businesses with few assets gained tremendous value as the places to go to socialize online, with family, customers, friends, and influencers. The more real or potential network connections we had who used a platform, the more certain we became that we had to use it too. In the platform economy, the more, the merrier. These network effects continue to drive audiences to platforms at dizzying rates, rapidly eclipsing product pipelines and business models that dominated in times past.
Behind the visible connections, all this sociality also marked the beginning of voracious – yet invisible – intermediaries. We were giddily giving up our data in exchange for the peer-to-peer exchange of services, a backroom exchange with implications few would recognize for nearly another decade.
And today? Welcome to the “platform society,” in which we are connected to one another, but only through platforms that derive immense power from and over our human connections.
What are platforms?
I define a platform as follows:
Platform: An ecosystem that connects people and companies while retaining control over the terms of these connections and ownership of connection byproducts such as data.
Google, Apple, Facebook, and Amazon: These are the major platforms that José Van Dijck argues have defined how society and both public and private life function today. These platforms reach deeply into human lives worldwide, with their publicly understood purposes forming only a fraction of their activities and profits. And rippling from these big four platforms are smaller ones, which emulate their models in various ways. These platforms and their stakeholders transform not just what we buy and enjoy but what we need to live and thrive: how we educate, how we govern and are governed, and how we structure our societies.
The impact of globally operating platforms on local and state economies and cultures is immense, as they force all societal actors—including the mass media, civil society organizations, and state institutions—to reconsider and recalibrate their position in public space. (Van Dijk and Poell, 1.)
Platforms have a profound effect on how societal life is organized. Airbnb has changed not just the hospitality sector, but also neighborhood dynamics and social life. Uber has not only affected the taxi industry; it has affected the construction of roads and public transportation services. We do not yet vote through platforms, yet they have had irreversible effects on our elections. Today almost every sector of public life has become platformized: Higher Education. News and Journalism. Fitness and Health. Hospitality. Transportation. And in these platforms, transactions that are visible to consumers are undergirded by other transactions in which consumers become unwitting producers, their data a form of currency that subsidizes the transactions they chose to engage in in the first place.
Section 5: Future directions in the online world
With so much human activity and cultural expression enabled in Web 2.0, what is Web 3.0? Look this up on the web and you will find no shortage of responses. There is no consensus – no agreement among experts or among users. We don’t even know if we are already using Web 3.0, because it is hard to know where Web 2.0 ends.
Surely one valuable perspective on the present and the future of the internet would come from Tim Berners-Lee, who invented the World Wide Web (WWW) in 1989. (It was released to the public in the 1990s; read more of that history here.)
Today Tim Berners-Lee has a new mission – to make sure we really are connected by the internet. He describes what drove him to pursue this mission this way:
“Now people feel very disempowered, because the end result is that they’re telling their computer who their friends are, and who’s in the photographs, and planning things and designing things — and those plans and designs and friendships are sucked up and held by these social networks. And they’re not really social networks, they’re silos.”
The data you create as you move across online spaces is often controlled and owned by those spaces. Berners-Lee is now working to develop new methods of linking data across virtual space without relying upon governments, corporations, or the many others with an interest in controlling that data. You can read more about this new mission in this TechCrunch article.
“Right now we have the worst of both worlds, in which people not only cannot control their data, but also can’t really use it,” Berners-Lee said in the project’s announcement last year. “Our goal is to develop a web architecture that gives users ownership over their data.”
Student Insights: Old vs. New Media (audio & writing by Tyler Amberg, Fall 2021)
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https://opentextbooks.library.arizona.edu/humansrsocialmedia/?p=45#h5p-16
TASL: Music includes Melody 6 and Drums 3 from iVoices Innovation Pack by Gabe Stultz, iVoices Media Lab, CC-BY.
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Respond to this case study: What affordances do you take for granted? How would your day-to-day life change if a technology you relied upon was no longer available? What might you substitute or repurpose to fill that need?
Virtuality 3.0: SXSW in Austin, TX — Social Media and Ourselves podcast
Virtuality 3.0: SXSW in Austin, TX
Release date: April 1st 2022
The SMO podcast team was at SXSW 2022 with the University of Arizona Wonder House. In this episode, Diana sets out to disentangle the culture of the city of Austin and the hyperconnected projection that is SXSW. Are they in a codependent relationship? What has SXSW done to Austin’s music scene? Why do even tech-savvy people prefer f2f to online? And what does all this mean for the next frontier of tech experiences, Virtuality 3.0? Interviews with Austin residents and visitors include Thor Harris of Swans, Luke Savisky of the 1990 film Slacker, and the crew of the food truck Cocina de Carnival aka Big Bertha.
LISTEN • LISTEN WITH TRANSCRIPT
Respond to this podcast episode…How did the podcast episode “Virtuality 3.0: SXSW in Austin, TX” use interviews, student voices, or sounds to demonstrate a current or past social trend phenomenon? If you were making a sequel to this episode, what voices or sounds would you include to help listeners understand more about this trend, and why?
Core Concepts and Questions
Core Concepts
analog
not digital. This term technically refers to reliance on processes that are continuous rather than enacted through specific values (digits), but it can be informally used to mean nearly anything that is not digital.
broadcast media
one subcategory of older media, including television and radio, that communicates from one source to many viewers
culture
a concept encompassing all the norms, values, and related behaviors that people who have interacted in a social group over time agree on and perpetuate
net neutrality
a shorthand name for a key set of features that have made the internet what it is today
network effects
the more a platform is used, the more likely that platform is where we go to interact with family, or friends, or customers, or all of these. In other words, in the platform economy, the more, the merrier
platform
an ecosystem that connects people and companies while retaining control over the terms of these connections and ownership of connection byproducts such as data
print media
a subcategory of older paper-based media such as newspapers, books, and magazines, that many users access individually
technological convergence
blending of old and new media. For example, cellular phones were once shaped more like analog (non-digital) phones
Web 2.0
sites that afford user contributions, such as likes and votes
Core Questions
A. Questions for qualitative thought:
- What are examples of qualities that digital media have inherited from traditional media other than those discussed here? Try to think of some that don’t make the new media work better.
- Can you give an example of a site that allows you to create and share? And then of one that still treats you like little more than “eyeballs”? Explain.
- Do you think you are part of “the people formerly known as the audience?” Is it still possible to feel that you are only an audience (not a participant) in the age of social media? Or are there different terms we should use now?
- Try to conceptualize a platform that you use. Make it a place, familiar or imaginary. How is it organized? Who is there? How are they behaving?
B. Review: Which is the best answer?
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C. Game on!
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Related Content
Consider It: How will AI affect workers? Tech waves of the past show how unpredictable the path can be
Bettmann via Getty Images
Bhaskar Chakravorti, Tufts University
The explosion of interest in artificial intelligence has drawn attention not only to the astonishing capacity of algorithms to mimic humans but to the reality that these algorithms could displace many humans in their jobs. The economic and societal consequences could be nothing short of dramatic.
The route to this economic transformation is through the workplace. A widely circulated Goldman Sachs study anticipates that about two-thirds of current occupations over the next decade could be affected and a quarter to a half of the work people do now could be taken over by an algorithm. Up to 300 million jobs worldwide could be affected. The consulting firm McKinsey released its own study predicting an AI-powered boost of US$4.4 trillion to the global economy every year.
The implications of such gigantic numbers are sobering, but how reliable are these predictions?
I lead a research program called Digital Planet that studies the impact of digital technologies on lives and livelihoods around the world and how this impact changes over time. A look at how previous waves of such digital technologies as personal computers and the internet affected workers offers some insight into AI’s potential impact in the years to come. But if the history of the future of work is any guide, we should be prepared for some surprises.
The IT revolution and the productivity paradox
A key metric for tracking the consequences of technology on the economy is growth in worker productivity – defined as how much output of work an employee can generate per hour. This seemingly dry statistic matters to every working individual, because it ties directly to how much a worker can expect to earn for every hour of work. Said another way, higher productivity is expected to lead to higher wages.
Generative AI products are capable of producing written, graphic and audio content or software programs with minimal human involvement. Professions such as advertising, entertainment and creative and analytical work could be among the first to feel the effects. Individuals in those fields may worry that companies will use generative AI to do jobs they once did, but economists see great potential to boost productivity of the workforce as a whole.
The Goldman Sachs study predicts productivity will grow by 1.5% per year because of the adoption of generative AI alone, which would be nearly double the rate from 2010 and 2018. McKinsey is even more aggressive, saying this technology and other forms of automation will usher in the “next productivity frontier,” pushing it as high as 3.3% a year by 2040.
That sort of productivity boost, which would approach rates of previous years, would be welcomed by both economists and, in theory, workers as well.
If we were to trace the 20th-century history of productivity growth in the U.S., it galloped along at about 3% annually from 1920 to 1970, lifting real wages and living standards. Interestingly, productivity growth slowed in the 1970s and 1980s, coinciding with the introduction of computers and early digital technologies. This “productivity paradox” was famously captured in a comment from MIT economist Bob Solow: You can see the computer age everywhere but in the productivity statistics.
https://datawrapper.dwcdn.net/i96wK/10/
Digital technology skeptics blamed “unproductive” time spent on social media or shopping and argued that earlier transformations, such as the introductions of electricity or the internal combustion engine, had a bigger role in fundamentally altering the nature of work. Techno-optimists disagreed; they argued that new digital technologies needed time to translate into productivity growth, because other complementary changes would need to evolve in parallel. Yet others worried that productivity measures were not adequate in capturing the value of computers.
For a while, it seemed that the optimists would be vindicated. In the second half of the 1990s, around the time the World Wide Web emerged, productivity growth in the U.S. doubled, from 1.5% per year in the first half of that decade to 3% in the second. Again, there were disagreements about what was really going on, further muddying the waters as to whether the paradox had been resolved. Some argued that, indeed, the investments in digital technologies were finally paying off, while an alternative view was that managerial and technological innovations in a few key industries were the main drivers.
Regardless of the explanation, just as mysteriously as it began, that late 1990s surge was short-lived. So despite massive corporate investment in computers and the internet – changes that transformed the workplace – how much the economy and workers’ wages benefited from technology remained uncertain.
Early 2000s: New slump, new hype, new hopes
While the start of the 21st century coincided with the bursting of the so-called dot-com bubble, the year 2007 was marked by the arrival of another technology revolution: the Apple iPhone, which consumers bought by the millions and which companies deployed in countless ways. Yet labor productivity growth started stalling again in the mid-2000s, ticking up briefly in 2009 during the Great Recession, only to return to a slump from 2010 to 2019.
San Francisco Chronicle/Hearst Newspapers via Getty Images
Throughout this new slump, techno-optimists were anticipating new winds of change. AI and automation were becoming all the rage and were expected to transform work and worker productivity. Beyond traditional industrial automation, drones and advanced robots, capital and talent were pouring into many would-be game-changing technologies, including autonomous vehicles, automated checkouts in grocery stores and even pizza-making robots. AI and automation were projected to push productivity growth above 2% annually in a decade, up from the 2010-2014 lows of 0.4%.
But before we could get there and gauge how these new technologies would ripple through the workplace, a new surprise hit: the COVID-19 pandemic.
The pandemic productivity push – then bust
Devastating as the pandemic was, worker productivity surged after it began in 2020; output per hour worked globally hit 4.9%, the highest recorded since data has been available.
Much of this steep rise was facilitated by technology: larger knowledge-intensive companies – inherently the more productive ones – switched to remote work, maintaining continuity through digital technologies such as videoconferencing and communications technologies such as Slack, and saving on commuting time and focusing on well-being.
While it was clear digital technologies helped boost productivity of knowledge workers, there was an accelerated shift to greater automation in many other sectors, as workers had to remain home for their own safety and comply with lockdowns. Companies in industries ranging from meat processing to operations in restaurants, retail and hospitality invested in automation, such as robots and automated order-processing and customer service, which helped boost their productivity.
But then there was yet another turn in the journey along the technology landscape.
The 2020-2021 surge in investments in the tech sector collapsed, as did the hype about autonomous vehicles and pizza-making robots. Other frothy promises, such as the metaverse’s revolutionizing remote work or training, also seemed to fade into the background.
In parallel, with little warning, “generative AI” burst onto the scene, with an even more direct potential to enhance productivity while affecting jobs – at massive scale. The hype cycle around new technology restarted.
Looking ahead: Social factors on technology’s arc
Given the number of plot twists thus far, what might we expect from here on out? Here are four issues for consideration.
First, the future of work is about more than just raw numbers of workers, the technical tools they use or the work they do; one should consider how AI affects factors such as workplace diversity and social inequities, which in turn have a profound impact on economic opportunity and workplace culture.
For example, while the broad shift toward remote work could help promote diversity with more flexible hiring, I see the increasing use of AI as likely to have the opposite effect. Black and Hispanic workers are overrepresented in the 30 occupations with the highest exposure to automation and underrepresented in the 30 occupations with the lowest exposure. While AI might help workers get more done in less time, and this increased productivity could increase wages of those employed, it could lead to a severe loss of wages for those whose jobs are displaced. A 2021 paper found that wage inequality tended to increase the most in countries in which companies already relied a lot on robots and that were quick to adopt the latest robotic technologies.
Second, as the post-COVID-19 workplace seeks a balance between in-person and remote working, the effects on productivity – and opinions on the subject – will remain uncertain and fluid. A 2022 study showed improved efficiencies for remote work as companies and employees grew more comfortable with work-from-home arrangements, but according to a separate 2023 study, managers and employees disagree about the impact: The former believe that remote working reduces productivity, while employees believe the opposite.
Third, society’s reaction to the spread of generative AI could greatly affect its course and ultimate impact. Analyses suggest that generative AI can boost worker productivity on specific jobs – for example, one 2023 study found the staggered introduction of a generative AI-based conversational assistant increased productivity of customer service personnel by 14%. Yet there are already growing calls to consider generative AI’s most severe risks and to take them seriously. On top of that, recognition of the astronomical computing and environmental costs of generative AI could limit its development and use.
Finally, given how wrong economists and other experts have been in the past, it is safe to say that many of today’s predictions about AI technology’s impact on work and worker productivity will prove to be wrong as well. Numbers such as 300 million jobs affected or $4.4 trillion annual boosts to the global economy are eye-catching, yet I think people tend to give them greater credibility than warranted.
Also, “jobs affected” does not mean jobs lost; it could mean jobs augmented or even a transition to new jobs. It is best to use the analyses, such as Goldman’s or McKinsey’s, to spark our imaginations about the plausible scenarios about the future of work and of workers. It’s better, in my view, to then proactively brainstorm the many factors that could affect which one actually comes to pass, look for early warning signs and prepare accordingly.
The history of the future of work has been full of surprises; don’t be shocked if tomorrow’s technologies are equally confounding.
Learn what you need to know about artificial intelligence by signing up for our newsletter series of four emails delivered over the course of a week. You can read all our stories on generative AI at TheConversation.com.
Bhaskar Chakravorti, Dean of Global Business, The Fletcher School, Tufts University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Hear It: Air Facebook
Platforms can be difficult to understand and conceptualize. Humor can help; so can illustration, and imagination. Here is how I imagine one platform that’s been significant in my life, but that I find it difficult to leave due to network effects.
About the author
Dr. Diana Daly of the University of Arizona is the Director of iVoices, a media lab helping students produce media from their narratives on technologies. Prof Daly teaches about qualitative research, social media, and information quality at the University of Arizona.
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