At the 2023 Collision Conference, held June 26-30 in Toronto Canada, DCN’s editorial director Michelle Manafy sat down with three media executives to discuss the ethics of using generative AI in journalism. The conversation covered the evolution of AI and its usage in the media, up to today’s much-discussed generative AI tools. Panelists weighed in on a range of use cases and where they would – or would not – permit (or even encourage) the use of generative AI in their media organizations. They also discussed whether or not generative AI is an existential threat to journalism, journalists — and even humanity as a whole. Listen to the discussion here and/or read a few highlights below.
Navigating the ethical landscape of generative AI and journalism
Featuring:
Gideon Lichfield – Global Editorial Director, Wired
Harry McCracken – Global Technology Editor, Fast Company
Traci Mabrey – Head of News, Factiva
Michelle Manafy – Editorial Director, Digital Content Next
A few highlights from the panel discussion:
Traci Mabrey: We’ve been using [machine learning and AI] forever and that’s a really important component as we look at this. This new horizon is going to be something, and I don’t think any of us know exactly what that is yet. But we have been using the building blocks of it for quite some time…
Gideon Lichfield: I think what’s changed is that it now has the capability to produce something that looks like something humans would create from scratch. And I emphasize looks like because it’s very clear that what’s going on is imitation… the fact that it became available as an easy to use interface was really crucial… this technology was around already for a few years, but it wasn’t that easy to access. The big change last year was just that it became easy to access…
Michelle Manafy: We’ve heard of late that some big tech leaders, some really smart folks call generative AI an existential threat. Are we afraid? Should we be afraid? And I don’t just mean as the media. You guys all think about larger issues in society. Is this good? Is it bad? Should we all be scared?
Harry McCracken: I think the worrying about it blowing up the world or killing us all is a little overwrought, particularly because there’s a pretty long list of genuine concerns that are either an issue right now or pretty clearly will be over the next few years involving things like misinformation. There are huge privacy concerns with a handful of large companies grabbing all our data and synthesizing that for their own benefit. I’d say there are plenty of things to worry about with A.I… but destroying the world might be more like the way that social media has, in a lot of ways, degraded the human experience…
Gideon Lichfield: …the increasing volume of just sheer garbage that is out there that is going to be generated by AI: that’s a that’s a real worry. And the job displacement part is also a thing that I worry about. But I think there is a way to use it. There is a way to use A.I. that empowers people, gives them extra tools. But it’s also a great temptation for companies, for employers to simply look at it as a way to save costs…
Harry McCracken: …Journalism is unusual in that the writing is the product. Most of the writing that exists in the world is not the product, just the byproduct. There are a lot of cases where having a computer draft your internal memo or whatever makes a lot of sense and will fill you up to do more important things…
Traci Mabrey: …I think if we look at our journalists and our editors around the world, there’s a very personal scope that goes into everything somebody is writing and somebody is speaking about. And I think that’s a really big component when you look at it. The technology, as Gideon was saying, it is bringing up a set of words. It’s able to make 500 words on X topic regarding this. But that is not the way that I would infuse that information into the world. And it’s not those types of things that make organic journalism and all of the real nuggets that we get from it… I think for the drafting process and the information gathering, certainly saving a lot of time. But we’re certainly on the path of that being a still a very personal end product.
Learn more about how media leaders are developing their policies around the usage of AI and generative AI in their organizations:
When it comes to video storytelling, tools are a critical factor in determining the quality of your product. Reliance on outdated video-production tools can affect your ability to compete with other brands battling for audience attention and loyalty.
Video is becoming an incredibly valuable tool for disseminating information quickly to audiences. In fact, 43.4% of internet users watch online videos as a source of learning each week. For media organizations seeking to build loyalty and report on breaking news, it’s important to have a suite of tools that makes it easy to create brand-consistent videos with just a few clicks.
Given trend shifts in the video-production industry, read on to evaluate if your tool suite could use an upgrade.
What goes into video production?
The world of modern video production is more important than ever, given that companies need to put tools in the hands of as many reporting teams and video editors as possible. While some videos may require high production costs and complex systems, it is beneficial to also have tools that make it easy to respond to timely local news and major events. For content that needs to be published quickly, companies need to have resources available that allow reporters to share content at any time, from anywhere with the click of a button.
Today, publishers producing the best videos customize their themes, ensure brand recognition, publish directly from advanced interfaces and much more. The videos that stand out in today’s crowded marketplace live on multiple platforms and speak directly to viewers while getting online quickly.
Video production challenges
There’s no doubt that today’s video storytelling is better than ever. That quality comes at a cost, though. Modern video storytellers face a variety of challenges with video production, including the following:
1. Time
In today’s fast-paced news environment, publishers need to be able to post top-quality videos in less time than ever before. This time crunch is a real problem for many organizations and can easily separate the winning digital publications from those who fall behind.
To combat time-related issues, teams benefit from platforms that allow them to automate many of the repetitive tasks of video production. Tools like templating and brand recognition packages help publishers push to-the-minute video content without missing crucial deadlines or falling behind the news cycle.
2. Incorrect formatting
Many storytelling teams have tried to get around time-crunch problems by creating one-size-fits-all content. Unfortunately, this kind of content often feels unoriginal and duplicative. Additionally, it may not be properly sized or formatted for the intended platform. As consumers increasingly gather information from alternative sources such as social media, email and websites, it’s important to have content that can be adjusted and reformatted for each location.
To combat this issue, teams have two choices: invest far more time and energy in each video or use a platform that makes it easy to create diverse material that remains on-brand. The right digital publishing tools provide comprehensive customization options, including the ability to swap out themes while applying branding across videos and publishing them in the correct sizing.
3. Software limitations
Relying on limited, low-capacity software to produce critical video content can be a big pitfall for publishers that want to stand out. Software limitations make it virtually impossible to remain competitive in the video-storytelling environment and may even create unnecessary bottlenecks during breaking news events.
To combat software limitations, today’s digital publishers must find video-production software that provides powerful tools, including theme customization, personalization, publishing flexibility and more.
4. Team size
Historically, teams have needed to grow larger to support video storytelling efforts. That’s because video storytelling tools are often complex, and not everyone was equipped to use them. As a result, teams needed to create entire publishing and production departments full of people who knew how to use advanced tools.
Today, however, large teams can actually make it harder to be agile and adaptable. Therefore, the most competitive publishing teams out there are paring down, opting for more intuitive software (that doesn’t require specialized skills to use) rather than larger teams. This allows your teams to be effective in a variety of key focus areas, while still producing engaging content.
Critical Focus
Video production software can help increase competitiveness across the market. With this in mind, let’s look at a couple of recent advancements in video-production software that will help you optimize your strategy.
Automating branded content
To be recognizable, branding should incorporate the same logos and color schemes across all videos. This is necessary to ensure brand recognizability, while producing quality material quickly and easily. Modern video-production software can help you reuse branded assets and themes where they are needed. The ability to save brand themes and layouts across multiple platforms allows media teams to rapidly implement them across the board. This gives teams more time to focus on making the content stand out while maintaining brand consistency.
Anywhere, anytime video production
As teams have pared down and become leaner and more agile, video-production software has morphed to support the needs of small teams. In other words, your teams must be able to easily create videos from anywhere, at any time, with any device. This makes it easy for reporters in the field to cover breaking news across your social channels and your website.
Ease, automation, and always on-brand content
Today’s media teams must have the ability to easily create quality video-storytelling materials without sacrificing speed, accuracy or branding. Today, this is critical for any company that wants to remain competitive, creative and innovative in today’s fast-paced video-publication environment.
Great video production serves several purposes: it spreads breaking news, educates audiences and promotes viewership. Clearly, media companies make a significant contribution in building their brand and that brand trust should be maximized across platforms. Streamlining that process allows teams to focus on creating the kind of content that promotes brand recognition and audience retention.
For a decade, artificial intelligence (AI) has enabled digital media companies to create and deliver news and content faster, to find patterns in large amounts of data, and engage with audiences in new ways. However, with much hyped recent announcements including ChatGPT, Microsoft’s next-gen Bing, and Meta’s LlaMA, media outlets recognize that they face significant challenges as they explore the opportunities the latest wave of AI brings.
In this second story in our two-part series on the evolution of AI applications in the media business*, we explore six challenges that media outlets face around AI tools, from the misuse of AI to generate misinformation, errors and accuracy, to worries about journalistic job losses.
Misinformation
While it has been used by media companies for various purposes over the last 10 years, AI implementations still face challenges. One of the biggest is the risk of creating and spreading misinformation, disinformation and promoting bias. Generative AI could make misinformation and disinformation cheaper and easier to produce.
“AI language models are notorious bullshitters, often presenting falsehoods as facts. They are excellent at predicting the next word in a sentence, but they have no knowledge of what the sentence actually means,” wrote Melissa Heikkilä for MIT Technology Review.
Generative AI can be used to create new content including audio, code, images, text, simulations, and videos—in mere seconds. “The problem is, they have absolutely no commitment to the truth,” wrote Emily Bell in the Guardian. “Just think how rapidly a ChatGPT user could flood the internet with fake news stories that appear to have been written by humans.”
AI could also be used to create networks of fake news sites and news staff to spread disinformation. Just ask Alex Mahadevan, the director of MediaWise at the Poynter Institute, who used ChatGPT to create a fake newspaper, stories and code for a website in a few hours and wrote about the process. “Anyone with minimal coding ability and an ax to grind could launch networks of false local news sites—with plausible-but-fake news items, staff and editorial policies—using ChatGPT,” he said.
Errors and accuracy
Julia Beizer, chief digital officer at Bloomberg Media, says the biggest challenge she sees around AI is accuracy.
“At journalism companies, our duty is to provide our readers with fact-based information. We’ve seen what happens to our discourse when our society isn’t operating from a shared set of facts. It’s clear AI can provide us with a lot of value and utility. But it’s also clear that it isn’t yet ready to be an accurate source on the world’s information,” she said.
Thus far, AI content generators are prone to making factually-inaccurate claims. Microsoft acknowledged that its AI-enhanced Bing might make errors, saying: “AI can make mistakes … Bing will sometimes misrepresent the information it finds, and you may see responses that sound convincing but are incomplete, inaccurate, or inappropriate.”
That hasn’t stopped media companies from experimenting with ChatGPT and other generative AI. Sports Illustrated publisher Arena Group Holdings partnered with AI startups Jasper and Nota to generate stories from its own library of content which were then edited by humans. However, there were “many inaccuracies and falsehoods” in the pieces. CNET, which also produced AI-written articles and came under scrutiny for factual errors and plagiarism in those pieces.
Francesco Marconi, longtime media AI advocate and co-founder of AppliedXL, said that though AI technologies can reduce media production costs, they also pose a risk to both news media and society as a whole.
“Unchecked algorithmic creation presents substantial pitfalls. Despite the current uncertainties, newsrooms should monitor the evolution of the technology by conducting research, collaborating with academic institutions and technology firms, and implementing new AI workflows to identify inaccuracies and errors,” he said.
“The introduction of generative summaries on search engines like Google and Bing will likely affect the traffic and referral to publishers,” Marconi said. “If search engine users can receive direct answers to their queries, what motivation do they have to visit the publisher’s website? This can impact news organizations in terms of display ads and lead generation for sites that monetize through subscriptions.”
Filter and context
The amount of data and information created every day is estimated around 2.5 quintillion bytes, according to futurist Bernard Marr. With the rise of generative AI models, the growth of information available to digital media companies and the public is exponential. Some experts predict that by 2026, 90% of online content could be AI-generated.
It presents a new challenge, according to Marconi. The explosion of data from IoT has created a world where there is too much of it. “We are now producing more information than at any other point in history, making it much more challenging to filter out unwanted information.”
A significant challenge for journalism today is filtering and contextualizing information. News organizations and journalism schools must incorporate computational journalism practices, so that journalists are also responsible for writing editorial algorithms in addition to stories.
“This marks an inflection point, where we now must focus on building machines that filter out noise, distinguish fact from fiction, and highlight what is significant,” Marconi said. “These systems are developed with journalistic principles and work 24/7 to filter out irrelevant information and uncover noteworthy events.”
Replacing journalists
AI-powered text generation tools may threaten journalism jobs, which has been a concern for the industry for years. On the other side is the longstanding argument that automation will free journalists to do more interesting and intensive work. It is clear, however, that given the financial pressures faced by media companies, the use of AI to streamline staffing is a serious consideration.
Digital media companies across the U.S. and Europe are grappling with what the potential of generative AI may mean for their businesses. Buzzfeed recently shared that it planned to explore AI-generated content to create quizzes, while cutting a percentage of its workforce. Last week, CEO of German media company Axel Springer Mathias Doepfner candidly admitted that journalists could be replaced by AI, as the company prepared to cut costs.
There is a valid concern regarding job displacement when considering the impact of AI on employment, Marconi agreed—with a caveat. “Some positions may disappear entirely, while others may transform into new roles,” he said. “However, it is also important to note that the integration of AI into newsrooms is creating new jobs: Automation & AI editors, Computational journalists, Newsroom tool managers, and AI ethics editors.”
Potential legal and ethical implications
One of the other biggest challenges digital media companies and publishers will face with the rise of AI in the newsroom are issues around copyright and intellectual property ownership.
ChatGPT and other generative AI are trained by scraping content from the internet, including open-source databases but also copyrighted articles and images created by publishers. “This debate is both fascinating and complex: fair use can drive AI innovation (which will be critical for long-term economic growth and productivity). However, at the same time it raises concerns about the lack of compensation or attribution for publishers who produced the training data,” according to Marconi.
Under European law, AI cannot own copyright as it cannot be recognized as an author. Under U.S. law, copyright protection only applies to content authored by humans. Therefore, it will not register works created by artificial intelligence.
“AI’s legal and ethical ramifications, which span intellectual property (IP) ownership and infringement issues, content verification, and moderation concerns and the potential to break existing newsroom funding models, leave its future relationship with journalism far from clear-cut,” wrote lawyer JJ Shaw for PressGazette.
Questions remain
While AI is not new, it is clearly making an evolutionary leap at present. However, while media companies may have been slow to adopt technology in the early days of the internet, today’s media executives are keen to embrace tools that improve their businesses and streamline operations. But given the pace at which AI is evolving, there’s still much to learn about the opportunities and challenges it presents.
Currently, there are some practical concerns for digital media companies and large questions still to be answered, according to Bloomberg’s Beizer. She questions how the advancement of these tools will affect relationships: “If we use AI in our own content creation, how should we disclose that to users to gain their trust?”
Wired has already made the first step by writing a policy that places clear limits on what they will use AI for and how the editorial process will be handled to ensure that a quality product is produced.
Beizer also poses the question of “how publishers and creators should be compensated for their role in sourcing, writing and making the content that’s now training these large machines?”
While in some eras, media companies have been swept along with the tide of technological change, with AI media executives are clearly grappling with how to embrace the promise while better managing the impact on their businesses.
Sometimes the weather is the story. However, weather also impacts many of the stories journalists tell – from sports events to celebrity weddings, groundbreaking ceremonies, and more. Maps and weather data can be powerful tools to create more memorable and impactful videos. Digital publications and platforms can leverage these elements to illustrate location, specify routes, and provide predictions regarding the potential impact of current and upcoming weather to create more engaging content.
There are many benefits of incorporating maps and weather data into video content, and all it takes is adding this capability to the existing software stack. Let’s take a look.
Why maps and weather enhance storytelling
When it comes to video content, it’s important to have tools that make it easy to enhance coverage of breaking news quickly. Maps can make it easier to visualize stories, such as election news and big-city races. Creating custom maps for breaking stories helps your viewers feel like they are involved rather than just passively watching. As a result, digital publications can increase engagement and viewer loyalty.
They can even provide coverage on a street level, which helps personalize a reporter’s video stories. By offering street-level viewing, your publication can go beyond generic maps. This personalization adds a level of depth to your storytelling that many other publications lack, making your stories unique and compelling for your viewers.
Additionally, weather data can be used to provide further context and insights to stories of all kinds. Companies can use weather to illustrate the role wind may have on a football game or how the path of a storm might affect an outdoor event. Since weather impacts almost every decision, this content has immense value for audiences.
5 ways map and weather content improves video storytelling
By bringing maps and weather data into video stories, journalists, reporters and content developers can provide more comprehensive, engaging videos. Here are just a few of the ways that maps and weather data can improve your digital video content:
Generate maps for specific stories. Maps can be generated for specific stories with the right tool. They can be customized to support the story with stunning visuals.
For example: Local digital publishers may choose to cover a 5K race or marathon. Incorporating maps into the story would allow teams to graphically showcase the route, and bring in “guests” at certain points along the map.
Illustrate the impact of weather on the story. In the case of a big event such as the Boston Marathon or New York City Marathon, publications might include commentary from elite athletes. Since weather can influence the outcome of races, content creators could showcase the impact of tailwind and humidity and include commentary and predictions about how the weather conditions will impact the race.
Weather was a major factor in the Ineos 1:59 Challenge, in which Eliud Kipoge broke the 2:00 marathon. Kipoge noted that “One of the biggest factors in running a fast marathon is the weather – temperature, humidity, precipitation, wind speed and direction will all have a significant impact, even the air pressure has some effect.” The addition of this unique perspective on how the weather impacts potential outcomes – and makes reporting more engaging.
Display live conditions. Content producers can illustrate the impact of weather on all sorts of current events such as outdoor sports games, state fairs or festivals, graduation ceremonies, holiday community events, and more. (For example, check out this bike race coverage.
Bring stories to life with guests & visuals. Display the impact of weather for upcoming event stories and bring in local meteorologists as expert guests. With weather graphics, teams can discuss the impact a hurricane will have on a community or how snow will affect the morning commute.
Drive revenue with weather-related ads. In addition to providing context and engaging more viewers, this form of storytelling can also be used with sponsors to drive revenue. For example, an owner of a tire company can sponsor the video and provide tips on what type of snow tires are best to buy when winter weather is approaching. Local advertisers – such as hardware stores during BBQ season or local caterers during graduation stories – will also find that the weather tie-in makes their advertising more relevant.
Real-life examples of combining weather data and map overlays
Wondering how to combine weather data and map overlays in videos? Here are a few use cases:
Election coverage Election coverage always draws a large number of viewers. It risks falling flat however, if high-quality graphics are not incorporated. With the right tools, journalists can enhance their election coverage by showing counties graphically. Digital publications and channels can bring reporters in at different polling locations, providing on-the-ground coverage for a more comprehensive and detailed viewing experience.
Marathon, parade, or other coverage of big events Marathons and races cover a large area, which can be difficult when there are only a few available team members. With advanced video tools, however, teams can use a distributed workforce, which allows reporters to create, publish, and live stream content at any time from any location along the race route. The same goes for parades or any events that would be enhanced by maps and weather data.
Telethon coverage The right video tools allow team members and sponsors to create and publish videos without requiring them to use specialized skills, complicated hardware, or expensive equipment. As a result, publications of any size can have the ability to host telethons or fundraising events. With the right tool, guests can easily participate from a remote location. Maps can then be used to showcase which areas have contributed the most and to set goals across a specific demographic.
Final Thoughts
Scaling video production with maps and weather can make videos more engaging since these provide more context to the stories already being told. They can also help audiences understand the scale of a story, or make it more personal through localization. By combining live map and weather data, digital publications and channels can provide real-time, up-to-the-minute coverage to viewers, with all the insights, credibility, and professionalism consumers expect.
About the Author
Jim Politis is part of the Max Weather solutions product management team at The Weather Company, where he focuses on Max Velocity, Max Engage, and Max Social. He was first part of the Max Quality Assurance team, and over the last decade at The Weather Company, Jim has been involved with nearly every product within the Max ecosystem. Prior to joining The Weather Company, Jim served as a broadcast meteorologist in Iowa, where he experienced many extreme weather events, from blizzards to record floods to tornadoes. One thing Jim appreciates about working at The Weather Company is that he no longer needs to present important information while hearing a tornado siren through the building walls! He has a bachelor of science degree in meteorology and an associate’s degree in computer science from Northern Vermont University-Lyndon.
From simple chatbots to generative content creation tools, artificial intelligence has become a force to be reckoned with for digital media companies. But while interest has heated up lately, artificial intelligence (AI) has already been employed by media companies for about a decade. Among other things, AI has long enabled journalists to analyze large amounts of data, extract insights, and identify patterns and trends. It also offers ways to engage audiences through personalized experiences and recommendations.
However, while it is not a new phenomenon, AI has come a long way since 2014 when some of the first media companies started using it for journalistic purposes. In this two-part series, we explore the evolution of AI use in the media, what’s different now, the opportunities that it offers publishers, as well as challenges including the misuse and abuse of AI, errors and accuracy, and concerns about job losses for human journalists.
AI + journalism
The news industry has witnessed three phases of AI innovation over the past decade: automation, augmentation, and generation, according to Francesco Marconi, computational journalist and the co-founder of real-time information company AppliedXL. “During the first wave, the focus was on automating data-driven news stories, such as financial reports, sports results, and economic indicators, using natural language generation techniques,” Marconi said.
In that first wave, AI offered a way to free journalists from menial or repetitive tasks so they could engage in more complex, high-impact, and investigative journalism. It was touted as a way to save organizations time and money by automating the production of certain types of news stories based on data.
During the second wave, the emphasis shifted to augmenting reporting through machine learning and natural language processing to analyze large datasets and uncover trends, Marconi explained. AI was a tool for journalists to use to analyze large amounts of data, text, video and images, to help them identify trends, patterns or outliers.
During this period, digital media companies also began experimenting with chatbots to help journalists tell stories differently. Chatbots simulated human conversations by responding to specific questions with pre-programmed answers. Early examples included Harvard Business Review’s Slackbot, and AccuWeather’s chatbot for Facebook Messenger that took questions from users and provided weather-related answers.
AI today
The third and current wave is driven by the AI generative movement, powered by large language models, which are capable of generating narrative text at scale.
In recent days, digital media companies have been experimenting with and examining how they can use ChatGPT and generative AI. Last month, Jim Mullen, chief executive officer at Reach PLC, told the Financial Times his company had set up a working group to examine how the tool might be used to assist journalists to write short news stories or compile coverage of local weather and traffic.
For media companies that have used AI for years, this new crop of AI has been interesting to watch. While ethical and quality uses are well-established, some of the current applications are still a work in progress.
One early adopter, Bloomberg Media, has used AI for more than a decade. Their deployment uses natural language processing (NLP) and machine learning to extract information from documents, including detecting people, companies and organizations found in the text of their stories. In addition, they also extract insights to help reporters find news about companies or sectors they cover.
“Like most consumers, I’ve been enchanted by the latest crop of AI tools. Tools like ChatGPT and Midjourney are showing us how much creativity these tools can unlock,” said Julia Beizer, Chief Digital Officer at Bloomberg Media.
“But alongside the enchantment, I’ve felt a fair bit of unease. These are early, very public experiments and we are seeing in real time how easy it is for unproven tech to get the facts wrong. This should be a reminder to all of us – particularly those of us in the journalism business – how important the work we do is,” she said.
The difference in today’s AI
For digital media companies, AI is different now for a number of reasons. Newer generative AI models have broader capabilities because of increased computational power, which means models can process larger amounts of data faster. There have been improvements in NLP, which has allowed AI to generate more nuanced language.
And, in recent years, AI tools have become more accessible for digital content companies. The current crop requires far less tech savvy and investment because they’re open-source.
“Initially, AI in the newsroom was limited to big newsrooms with significant resources,” explained Marconi. “However, with the widespread availability of AI infrastructure, such as open-source models and APIs, the technology is now accessible to every newsroom. This broad accessibility will also bring major challenges as the news industry rushes to adopt new standards and workflows.”
Bloomberg’s Beizer believes that the big difference right now is creativity. “The best technology feels like magic. It fades to the background so all you’re left with is the experience. ChatGPT is a simple interface over one of the most powerful pieces of technology we’ve seen,” she said.
So what’s new?
Given the leap forward in ease of use, AI may create new opportunities for those willing to adapt and innovate in this rapidly changing landscape.
“The economics of the media business are changing dramatically. AI has substantially reduced the cost of production” such as including editing, story writing, and creating multiple versions. “But newsgathering and fact-checking still necessitate specialized knowledge and infrastructure that technology companies lack,” Marconi said.
He added that media companies have an opportunity to become a major player in the AI space. That’s because they possess valuable assets for AI development: the text and image data for training models and ethical principles for creating trustworthy systems.
Advances in technology have disrupted the media industry for many years. According to Beizer, generative AI is only the latest chapter. “What I believe has changed this time around is that we are now mature digital companies with digital businesses to protect and grow. This time around, I have no doubt that our industry will rise to the occasion of thoughtfully exploring this disruption from all its angles – and setting the right strategies for our businesses and quality content to flourish,” she said.
Opportunities ahead
There are valid concerns about whether generative AI will be used to flood the market with content in the future. However, reputable media outlets will find that there are responsible applications of AI that will actually allow them to double down on the quality of their journalism and provide better content recommendations and experiences.
For Beizer, the biggest opportunity at the moment is “focusing our companies on what matters most: our customers… In this moment, we should lean even harder into building direct relationships with those users, getting to know what they want and delivering that value. We have the biggest opportunities in the kind of differentiated value that a chatbot cannot provide – analysis, novel points of view, human connection, nuanced reporting and writing. We should deprioritize anything that isn’t that. The future of our business depends on it,” she said.
However, as AI becomes more sophisticated, there are other promising applications for journalists. Imagine an AI-based tool that could help with large projects that newsrooms could never have enough staff to do, or one that checked facts, style, and grammar.
Marconi sees the evolution of AI in recent years as the foundation for the next phase of AI in journalism, which he believes will come to fruition over the next decade. He calls it artificial journalistic intelligence, where machines are able to automate news gathering and production, resulting in improved accuracy, efficiency, and impartiality, he said.
“Nonetheless, humans will continue to play a significant role. Creating editorial algorithms requires human supervision. As a result, in this new era, transparency and auditing of algorithms will become critical functions of newsrooms in the future,” he said.
In part 2, we look at challenges and concerns around generative AI, including the misuse and abuse of AI, errors and accuracy, legal and ethical issues, and concerns about job losses for human journalists.
As artificial intelligence (AI) continues to evolve, we see more and more examples of AI-generated content in our daily lives. It is only a matter of time before brands start to adopt these tools to scale their content creation and power their content marketing strategies. This trend will continue to accelerate as AI gets better at producing quality content.
For publishers, this presents a huge opportunity. Brands will need help distributing the influx of new content, and publishers are in a prime position to provide that assistance. But are we ready? Do we have the infrastructure in place to handle the increased demand?
Only time will tell. But one thing is for sure: the world of brand content is about to get a whole lot bigger, and publishers need to prepare by focusing on strategies to scale their sponsored content programs.
Content is about to get a whole lot bigger, thanks to AI creation
Companies will no longer be limited to having just a few people write their content. Instead, with automated content creation, brands can scale their content quickly and easily – and leverage their content resources to refine the messaging. This means less time spent creating individual pieces of quality content, allowing for more ideas and creativity to be explored on a grander scale. Companies may find themselves communicating with consumers in ways they never thought possible before.
This dynamic change in how brands approach content creation will undoubtedly open up new opportunities and heighten engagement between brands and customers. The content marketing problem will shift focus from resources required to generate impactful content to finding distribution partners that can prove a shift in brand consideration. We expect big changes on the horizon as AI-written content takes on an increasing presence in the market.
Brands will need help distributing all this new content to drive consumer consideration
With the ever-increasing influx of content, brands are truly facing a looming challenge. How can they keep up with the speed and quantity of new content while making sure it has an impact and drives consumer consideration? How will they know which content connects most with consumers?
The answer lies in strategic partnerships with publishers. For brands to be successful, they must find reliable partnerships that allow them to reach their target audiences quickly and efficiently. Brands need experts and strategists who understand how to reach their customers and how to leverage optimization technologies to ensure the right brand content is served to ensure that readers are guided through the buyer’s journey. Reporting will shift from traditional advertising and attribution metrics to content and consideration insights such as lift studies, engagement, and audience insights.
Publishers are uniquely positioned to capitalize on this trend as they have a captive audience
This shift to AI-written content presents an opportunity for publishers to act as mediators between the content and its audience. Publishers have a captive audience and the expertise and experience necessary to ensure that all content reaches the right people in the most efficient manner possible. They can help brands successfully scale their content distribution quickly, efficiently, and accurately so that their message gets out loud and clear. Moreover, leveraging first-party data about their audience enables publishers to provide critical insights into how content is being consumed, as well as its impact on moving readers through their buyers’ journey.
But they’ll need to find innovative ways to scale their operations
We can expect content to explode around us, and for publishers to capitalize on this trend, they need to find effective ways to run their ad operations and make their sponsored content programs scaleable. That means if a publisher relies on manual processes, custom codes, and complex CMSes to run these premium programs they won’t be able to keep up with the sheer volume of sponsored content campaigns thrown at them. To meet the demands of our new digital landscape, organizations must figure out how best to automate their processes to scale sponsored content for brands.
How can publishers prepare for this explosion of content?
To meet the needs of brands adopting AI technology to scale the production of their content, publishers should focus on streamlining in a few key areas:
Campaign setup
Implement their own AI Content tools to suggest variations on existing content that can continually feed into optimization and reporting providing invaluable insight into content performance.
Also, with many publishers running sponsored content through their website CMS, we have found that programs run in this manner cannot meet high demands when sponsored content is sold at scale.
Campaign optimization
Machine learning optimization technology: AI will help draft the content, and publishers can provide content variation recommendations. When you feed this data into an optimization engine, campaign performance will inevitably see a lift and deliver valuable metrics into the type of content that resonates with a brand’s audience.
Opportunity awaits
Taking these steps now can give publishers a major edge in the rapidly changing landscape of brand content distribution. As AI-generated content becomes more prevalent, publishers can capitalize on this trend by finding innovative ways to scale their operations and become strategic mediators between brands and audiences.
Disclaimer: This article was written in part by AI.
From audience analytics to programmatic advertising and automated story creation, media companies have used Artificial Intelligence (AI) for some time. However, this technology is rapidly maturing and opening up new creative and business possibilities that media executives need to be aware of.
ChatGPT, an AI chatbot, is the current poster child for this robotic reckoning. Garnering a huge amount of column inches in recent weeks, the application can provide detailed answers to questions and prompts. Along with other AI-generated innovations like the portrait app Lensa and OpenArt – a gallery of works created by AI – these tools have inspired the latest wave of discussion about the implications of this technology.
Amidst copious innovation and optimism, concerns have also surfaced around AI-generated content, consent, bias, labeling and regulation, as well as the impact on labor markets. None of these issues are going to go away any time soon. Nevertheless, while media companies and policymakers navigate this unfolding landscape, the roll-out and adoption of AI continues to gather pace.
Artificial intelligence at work in the media
With AI having a real moment right now, this is the perfect time to explore the ramifications for media companies. Here are six uses of AI technologies that need to be on your radar:
1. Driving engagement
One of the most common ways publishers are using AI and machine learning is through AI-powered algorithms which personalize content recommendations.
This can help increase engagement and keep readers on your site for longer. That’s particularly useful if time on site is a key performance metric. Of course, it can also enable you to serve more adds to your audience too.
Personalized recommendation technology has long been the mainstay of platforms like Amazon, Spotify, and Netflix. Now it’s becoming increasingly common for other forms of content too.
One early proponent, The Washington Post, uses AI to personalize the news that they deliver based on readers interests and preferences. It’s an approach they’ve been using for some time across their app, newsletters and now the homepage.
Sign up page for The Washington Post’s “For You” newsletter, highlighting the personalized nature of this product (Dec. 2022)
As Digiday explains, the Post offers a personalized “For You” section on the homepage that taps into information provided during onboarding. At sign-up, subscribers or registered users can select their topic preferences. Recommendations are further augmented by your reading history and other performance data.
It’s an area the Post looks set to double down on, as they and other outlets seek to move to a more tailored content offering and away from the “one size fits all” approach of yesteryear.
One of these models, dynamic paywalls, deploys AI to change free article limits. As a result, users hit the paywall at different times, based on their behaviors and other indicators that help to determine a consumer’s propensity to pay.
“Piano has seen visitors subscribe after a single pageview. Others take much longer to make the decision to convert, while some aren’t likely to ever subscribe at all,” Kaufman observes. In response to this variance, he argues, we need “smarter, more satisfying automation.”
AI can help. New York Media and Neue Zürcher Zeitung (NZZ, Switzerland) are just some of the publishers to adopt this model. They have used AI to determine individual paywalls, based on variables including geography, consumption habits and visit behavior, as well as subject matter and the device being used. Expect more publishers to follow suit.
3. Creating content
Many early newsroom experiments with AI focused on the potential to craft stories that typically follow a predictable formula.
One of the earliest to leverage AI for content creation, The Associated Press (AP) has been using AI since 2014 to generate summaries of earnings reports from publicly traded companies. This allows them to quickly and accurately provide readers with key information, freeing up reporters to do other work. “Prior to using AI, our editors and reporters spent countless resources on coverage that was important but repetitive,” their website notes, adding that this “distracted from higher-impact journalism.”
Alongside freeing up reporters, the technology has allowed AP to create more of this content. Automated story generation has enabled AP to increase the volume of these corporate stories by a factor of 10.
At a simpler level, AI is also being used to liberate resources otherwise hoovered up by resource-heavy work such as interview transcriptions.
AP is currently working with local newsrooms to help them increase their use of AI tools. In a survey asking what would be the most useful use of this technology, automating transcription came top.
Image: via AP
4. Distributing content
A further potential benefit of AI can be seen in its ability to support publishers in their desire to get material in front of audiences – wherever they may be.
POLITICO Europe has used AI to convert two of their popular newsletters, Brussels Playbook and London Playbook into daily podcasts. The audio option gives subscribers another way to consume this content on the go.
This type of technological solution can help publishers manage their resources more efficiently, as well as distribute content to different platforms in a timely and cost-effective manner.
A further mainstream iteration of this idea is also being developed by Google. Dyani Najdi, Managing Director of Video and Display EMEA, has highlighted how the tech giant is experimenting with a tool to reformat landscape videos for YouTube. Viewers will see videos in square or vertical formats, with the shape automatically determined by how you are accessing the platform.
Although currently only available for certain video-ad products, it’s not a big leap to imagine this being used for other content in the near future. If it is, that would be a huge time-saver for many publishers. A further boon is the possibility of this technology opening up new distribution avenues, without the time and expense of repurposing everything.
Where we go from here: two trends to keep an eye on
The manner in which AI is being employed is constantly changing. Its possibilities have sparked discussion about the implications for education, journalism and other creative work, as well as the wider knowledge economy.
Within that, here are two key AI-trends for publishers to closely follow and potentially adopt.
1. Leveling-up content, and ad, personalization
Based on their interests and preferences, AI can personalize the news that publishers deliver to readers. Its usage is only likely to increase and become more ubiquitous.
More than 9,000 publishers use Taboola’s recommendation platform. Earlier in the year, they announced that AI functionality had been added to their homepage techstack. The company said that in beta testing companies such as McClatchy, The Independent and Estado de Minas in Brazil, had seen a 30% – 50% increase in clickthrough rates for homepage sections personalized by Taboola.
Alongside content, AI can also be used to deliver a better ad experience. Publishers like Condé Nast are using machine learning to find patterns that can lead to more personalized and contextual ads. In a cookie-less future this type of approach will be essential if ads are to be targeted and relevant.
2. Improving and streamlining workflows
With cuts being seen across the media landscape, a key challenge for publishers in 2023 will involve maintaining output levels (never mind launching new products and verticals) with fewer staff.
AI may help here, given its ability to be used for A/B headline testing and other forms of predictive analysis. It can also tag and generate content such as business, sports and real estate stories. Or, as seen at Forbes, provide detailed prompts for writers.
It can further support social media and off-platform strategies too. The South China Morning Postsaved resources akin to work done by 3.9 full-time employees by using AI to streamline its social media management.
Meanwhile, in Germany, Frankfurter Allgemeine Zeitung has used AI to help editors understand which stories to put behind the paywall. This matters given their freemium model, and the need to balance free content that drives subscriptions with premium subscriber-only content that readers value.
The big picture
This list of uses is far from exhaustive. To it we can also add important developments such as the ability of AI to help address inequalities (through the automatic creation of audio articles, and work to measure gender disparity in news coverage), as well as the rise of automated fact checking and many others.
Although no one knows how this technology will play out, it’s clear that AI can play a valuable role in helping publishers with their operations. As a result, it is no surprise that key activities unlocked by this technology – such as data analytics and automation – are among the top investment areas for publishers in the coming year.
Previously, as the Knight Foundation has found, “when we talk[ed] about AI in newsrooms, we seem to lean heavily on the newsgathering part of the process and maybe do not pay as much attention to the product or the business side of the ecosystem.”
In 2023, that may begin to change, as we see an overdue shift in the thinking about the role that AI plays in supporting the strategic needs of publishers.
From shaping the content you see (Pink News’ positive news filter), to aiding with translations of new international editions (Le Monde’s digital English language product) and improving your SEO (Summari and other tools), AI is here to stay and increasingly integral to publisher strategies.
Against a challenging business backdrop, as outlets begin to focus more on areas like product, subscriptions and retention, AI’s contribution to a publisher’s success will become more prominent and important than ever.
The creator economy is estimated to be a $104.2B market with “a substantial growth trajectory similar to the gig economy.” And, just as the gig economy has transformed the shape of work, the creator economy is impacting the media landscape.
Industry-watcher Simon Owens notes how bootstrapped content entrepreneurs are harnessing platforms like YouTube, Instagram, and Substack to engage new audiences, often through verticals overlooked by mainstream outlets. Some are also very successfully monetizing these efforts. Globally, more than two million creators have six-figure incomes, as they tap into opportunities for sponsorship, subscriptions, and other revenue.
Excitement about this potential has unlocked more than $800 million from venture capitalists in recent years. This is “sending strong signals confirming the creator economy is not only legit, but a force to be reckoned with,” Influencer Marketing Hub suggests.
“This is really a new form of work and a new form of entrepreneurship,” according to Kaya Yurieff, a former CNN journalist who covers the creator economy for The Information.
The rapid rise of this sector is evident in the flurry of deals, creative campaigns, and platform developments featured in a four-times-a-week newsletter Yurieff and her colleagues have produced since April 2021.
Five actionable lessons for publishers
Collectively, the size of this market, its characteristics, and projected growth makes this a sector that media companies should not just report on. They should be learning from it too.
Below are five ways that publishers can learn from the creator economy.
1. Harness the value of niche
“As solo operators, creators handle everything from content creation to marketing to monetization,” Owens explains. “This forces them to be incredibly innovative if they want to compete with much larger media organizations for subscription and advertising dollars,” he adds.
One of the ways that they have addressed this is through niche content verticals. Yurieff points to Substack newsletters focused on indigenous news as an example of one “area that traditional publishers historically haven’t dedicated a ton of resources to.”
“Creators have proved that there are audiences for maybe more varied topics than we’ve seen traditionally in the media,” she says.
For publishers, this demonstrates the potential to superserve smaller, targetted, audiences. It isn’t always about scale. Rather, the focus should be on tapping into passions and subjects that audiences will engage with – and potentially pay for.
2. Learn how creators are growing their businesses
Niche verticals don’t necessarily have to remain small. “The most successful of these solo operators eventually end up hiring staff and acting more like traditional media companies,” Owens points out.
The transferable lessons from these types of success stories are highly relevant to the digital strategies being developed and deployed by publishers. As such, they should be required reading in the C-Suite.
3. Understand it isn’t just about flashy new social networks
Many of the methods used by independent creators to share content and generate engagement – such as newsletters, podcasts, groups and messaging apps, paid memberships, subscriptions and other tactics – will be very familiar to traditional publishers.
That said, in doing this, they are more likely to use newer platforms like Patreon, Discord and Geneva, than traditional publishers. But in terms of their approach, it shows you don’t necessarily have to reinvent the wheel. However, you do need to offer something valuable and distinctive. Content is King. Yet, in many cases, community is nearly as powerful. Distinctiveness can arguably be rooted in content, tone of voice, distribution channels and/or a combination of all three.
The Mamamia podcast network features around 50 different shows and 90 female hosts. According to their website, “it’s the largest women’s podcast network in the world.” Their podcast journey will be of interest to traditional publishers with similar audio ambitions.
By the same token, alongside a daily newsletter and podcast, Front Office Sports has created a paid Pro product. This includes research reports, access to events and market data, as well interactive courses sponsored by the likes of Meta and Ticketmaster.
Areas such as events, courses and research are among the tactics a number of publishers are also exploring as part of their efforts to diversify their revenues. The lessons Front Office Sports learned from these efforts is therefore relevant beyond the creator economy.
Publishers are increasingly moving into spaces like TikTok and Twitch in an effort to engage new (often younger) audiences. To make these efforts more successful, it can pay to hire staffers conversant with the style and tone of the creator economy.
NPR’s Planet Money might seem like a venerable show, but its TikTok account counters that perception. Run by 25-year-old Jack Corbett, it has nearly 750,000 followers. Teen Vogue describes Corbett as “a wacky-professor figure, a talented TikTok comedian, and most importantly, a guide through the largely inaccessible world of economics.”
For NPR, Corbett’s skills potentially brings audiences to Planet Money’s content who would otherwise find it inaccessible, or assume it wasn’t for them. It’s a tactic others should study, if not try to duplicate.
5. Unlock the potential for partnerships
Publishers might, rightly, see the creator economy as a source for talent. But, those hires don’t necessarily need to be full time. Partnerships offer another approach that can potentially work for both parties.
Launched last month, as part of a suite of new advice columns, The Washington Post added TikTok content creator Jules Terpak to its cadre. The weekly column, Ask Jules, explores how technology intersects with digital culture and the lives of her readers.
Moves such as this enable publishers to benefit from a creator’s established audience and recognized expertise. (And in turn, creators may also benefit from an association with, and the resources of, a trusted mainstream media brand.)
Creators have a built-in following and have demonstrated that audiences value their voice and viewpoint. Partnering with them can help publishers diversify their own voice and content offering. This provides a gateway to new audiences for other content and products.
What’s next? Two trends to follow.
The creator economy is home to valuable case studies, talent and skills that the wider media industry can benefit from.
This may become especially acute given the latest set of changes to Facebook’s mobile product. The move is part of what New York Magazine’s John Herrman refers to as social media’s “race to see who can copy TikTok the fastest and with the least dignity.” As a result, It may put a premium on the style and types of content that are the bread and butter of what many top creators produce.
Short-form video
“The algorithm changes will probably push publishers to create more short-form video content,” predicts Owens. “I wouldn’t be surprised if publishers start looking more and more for prior TikTok/Instagram Reels experience when hiring out their social media teams.”
Private communities
For Yurieff, one potential trend to note is the migration of online conversations into “more private spaces… I think that’s something really to watch,” she says.
She cites Discord as an example of a platform which now has “lots of different niches and groups and audiences using it.” It’s part of a wider move among online users to connect in smaller groups, communicating privately and potentially going deeper on certain topics, Yurieff says. These are principles more publishers might want to get on board with.
In that vein, The Information’s move to create opportunities for its subscribers to network directly with each other demonstrates how some of the community principles evident in the creator economy can be applied by media outlets.
“These new features are meant to add more value to the news site’s subscriber base, as opposed to driving meaningful revenue themselves,” Axios reports. Nevertheless, in the future, The Information may look to monetize them.
Like and subscribe
The growth of the industry, and the success of some of its proponents, means that if the creator economy is not on your radar yet, then it should be.
For publishers, the maturing of the creator economy, and the growing numbers engaged in (and with) it, present a number of learning opportunities. We need to be looking at the creator economy, as well as the more traditional media industry, for case studies, talent and insights into how to respond to the next wave of digital disruption.
Yet, despite its growth, “this new breed of creators may be looked upon as charlatans and opportunists by some purists in traditional publishing,” suggests Josep Nolla, VP, Business Development & Partnerships at the e-commerce provider Bolt. “But the reality is there are more similarities than differences between this exciting new economy and traditional publishing,” Nolla advises.
Creators, like traditional publishers, are looking to drive subscriptions, diversify their revenues, and generate engagement and loyalty. They may use different platforms, content styles, or verticals to do this. But arguably, a lot of their core business goals are the same. And there’s a lot to be learned in these similarities – and maybe even more from the differences.
Care to disagree? Then let’s debate it on Discord!
The Center for Cooperative Media sees collaborative journalism as a way to share power among journalists, readers and others to deliver information that centers and addresses people’s needs. They believe that collaboration can be particularly impactful when it involves the community.
Their research analyzes three collaborative journalism experiments in Europe:
The Bureau Local in the U.K is a nonprofit collaboration of journalists and non-journalists (data scientists, academics, citizens, etc.) engaging in topic-driven reporting projects.
“L’Italia Delle Slot” in Italy is a collaboration among one legacy, and two start-up news organizations focused on a single topic.
Lännen Media in Finland is a co-op collaboration among regional news organizations through shared content production and distribution.
They conducted 29 interviews among journalists, senior management, community organizers, data analysts, technical experts, and others.
Collaboration models
Each of the three publications offers different collaboration models:
Lännen Media
This is a co-op model where similar news organizations join on specific topics and do not compete. They manage daily reports via video conferencing and skype with editors and share a content management system to follow what they work on in the different newsrooms.
This setup allows journalists to rotate into the cooperative from the regional newsrooms for two or three years and then return to their original masthead. While larger newspapers contribute more, all members share the costs of running Lännen Media.
“L’Italia Delle Slot
A contractor model that establishes a commercial contract to dictate the collaboration among organizations with specific areas of expertise. In this case, a large legacy news publisher combines efforts with two data-journalism-focused start-ups. In 2013, Effecinque, a start-up, began researching the increase in slot machines in Italy. Effecinque partnered with Dataninja, a data-journalism network, to investigate if slot machines in Italy correlated to the rise in gambling addiction. Effecinque and Dataninja partnered with GEDI Visual Lab to produce a web portal, data visualizations, videos, and other interactive content to showcase the details of the investigation.
This approach allowed the two start-up organizations, GEDI and 13 local newspapers, to define their roles based on areas of expertise. Further, the 13 local newsrooms provided local knowledge to tell the stories about their community using the data set.
The Bureau Local
This project-based collaborative model relies on a nonprofit to act as a central hub that coordinates and supports parallel investigations. It’s often a diverse collaboration from regional BBC bureaus to commercial, chain-owned newspapers to independent local dailies, community-owned sites, and freelancers. Many organizations share data managed by a NGO nonprofit newsroom focused on public interest. This also allows for non-journalists such as data scientists, designers, and others to work together. This type of collaboration often coordinates investigations across national and local levels to help drive discussions among local and national politicians and policymakers.
The digital media ecosystem is a great environment to start collaborations such as establishing networks across localities and shared resources. Jenkins’ and Grave’s research illustrates three collaborative journalism models to showcase each of their unique approaches. Lännen Media’s co-op model shares resources across regional newspapers, “L’Italia Delle Slot’s” contractor model engages expert journalists on short-term investigations. The Bureau Local NGO model manages a shared database by a nonprofit. Each model offers a viable model for publisher sustainability – maintaining a healthy structure of shared economics, goals, and healthy competition.
Sponsored content plays a sizeable role in publishers’ revenue model. According to eMarketer, ad spend on sponsored content in 2021 neared $57 billion. Advertisers find the high production quality of today’s sponsored content a compelling marketing vehicle. As a result, audiences often find it difficult to differentiate between sponsor content and actual news content. Therefore, the FTC requires publishers to label it as advertising. As the market for it grows, it’s essential to understand how the publishers’ production and reliance on sponsored content affects news content.
An advertising quandary
While sponsored content has been around a long time, it’s grown significantly in the past 10 years in the digital media sector. Marketers value the “halo effect” of a publisher’s editorial integrity offering strong reader engagement. Sponsored content is usually a narrative, which contrasts with display and video ads. It often includes custom video, interactive elements, and high-end graphic designs.
Many premium publishers have content studios dedicated to creating sponsored content. The New York Times (NYT) launched T Brand Studio, The Washington Post (WP) owns BrandStudio, and The Wall Street Journal (WSJ) has The Trust.
In a two-step process, Amazeen and Vargo identified 27 sponsored content articles in WP, the NYT, and WSJ across five years. They first scraped the content studios’ Twitter accounts to identify sponsored content links. They then used a custom Bing program to search for sponsored content links inside the news websites. The 27 sponsors include Verizon, Airbus, Volvo, American Petroleum Institute, Dow Jones, Qualcomm, Holiday Inn, Huawei, Purdue Pharma, Netflix, Gartner, Subaru, Oracle, Fox Sports, Deloitte, Walmart, Nordstrom, Allergan, Accenture, Lockheed Martin, Samsung, IBM, Wells Fargo, MetLife, Delta Air Lines, Aetna, and Starz.
The researchers then used the Global Database of Events Language and Tone (GDELT), a news article database, to match sponsored content mentions of corporations and brands to articles matching the corporate or brand sponsor. In all, they found 2,707 articles related to the companies and brands sponsoring content.
Analysis details
The research assessed the relationship of corporate sponsorship to news coverage of the same company across time. The analysis evaluated whether publishers filtered and shaped their news accordingly to accommodate corporate sponsorship. This practice is known as agenda setting. It refers to how the news publishers can influence which issues become the focus of public attention. In the case of sponsored content, publishers can add news coverage of a company and brand or reduce and cut their news coverage.
They divided the news articles into two segments for further analysis:
Elite publishers (NYT, WP, and WSJ), and
General (non-premium) news media landscape.
Omitting any seasonality mentions (i.e., earning calls), 20 of the 27 sponsored content companies showed at least one instance of agenda cutting or agenda building.
Further, of the 27 brands analyzed, only three companies showed an agenda-building effect among elite publishers: Huawei, Qualcomm, and Purdue Pharma. Amazeen and Vargo conclude that agenda building is a less likely occurrence with sponsored content companies or brands.
In addition, seven brands showed significant agenda-cutting effects among premium publishers: Netflix, Nordstrom, Starz, Wells Fargo, Aetna, Oracle, and Qualcomm. The researchers also found a significantly higher agenda-cutting effects across the entire U.S. media landscape — 14 companies and brands.
Amazeen and Vargo acknowledge that the news media is not immune to internal and external forces in their newsrooms. Publishers often set guidelines for creative and narrative executions in content studios. Amazeen and Vargo suggest publishers monitor and develop measures to assess the additional news coverage or lack of for companies and brands of sponsored content.
When we talk about minority groups should we use BIPOC, POC, something different or nothing at all? It’s a question posed in America many times since the death of George Floyd in May 2020 – from Newsweek to The New York Times.
His murder sparked a similar debate across the pond over the United Kingdom’s equivalent acronym: Should BAME (black, Asian and minority ethnic) be used by British broadcasters?
Taking collective action
At the end of 2021, four of the UK’s major broadcasters formulated an answer. They committed to avoid using the collective term in their corporate communications, content and editorial news content. Instead, they would use more specific terms where available.
For Miranda Wayland, the BBC’s Head of Creative and Workforce Diversity and Inclusion, the departure from the catch-all term allows for a greater acknowledgement of the experience of people from different ethnic backgrounds.
“As a creative industry we are focused on increasing representation, so our content reflects society,” she said. “At the heart of representation is how we recognize people’s varied lived experiences and their identity. The more specific we are when describing someone’s heritage, the better we represent them. In turn, we create more inclusive and relevant content for our audiences.”
UK broadcasters – the BBC, ITV, Channel 4 and Channel 5/Viacom CBS UK – agreed to avoid “wherever possible” the BAME acronym following a report the Sir Lenny Henry Centre for Media Diversity. Commissioned by the BBC, the study stated that “A major concern, apparent in recent public responses to BAME, is that it homogenises culturally distinct social groups.”
A question of trust
Through interviews and audience research, the report’s authors found there was a lack of trust around the term BAME because of a belief that it has been used to hide failings in the representation of specific ethnic groups. They wrote: “Several interviewees illustrated this point by saying organisations are quick to announce hitting ‘BAME targets’ but what does that mean if there is still massive black under representation or east Asian representation.”
The researchers did acknowledge, however, that it would not be realistic to remove BAME terminology altogether because it is widely used in society. However, where BAME must be used, content-makers will strive to ensure that any use of the term is accompanied by an explanation. This will be achieved, for example, by stating that ”data for ethnic groups is unavailable.” Another solution is writing out the acronym in full – “black, Asian and minority ethnic” – to recognize the constituent groups that make up the collective term.
Sarita Malik, Professor of Media and Culture at Brunel University London and Academic Lead on the Report, said broadcasters need to acknowledge the importance of language as part of wider work to tackle racial disparities.
“Language is a really important issue for media and cultural organizations to look at when trying to tackle inequalities,” she said. “At the heart of the issue is a power dynamic; a power dynamic between those who have the power to label and those who are labelled. Our research identified a mostly negative sentiment towards the grouping of people under collective terms.”
She added that “Committing to use language in more culturally nuanced ways can help to deepen understandings of different ethnic groups. This is one of the ways in which trust can be built with audiences.”
Supporting cultural nuance
As Professor Malik observes, broadcasters need to give their content-makers the right support and resources so they can get their language right and add nuance to their work. At the BBC, the content-makers’ inclusion toolkit seeks to provide such support. Tools include Ipsos MORI’s Language Matters audience research, which echoes the findings of the BAME report by concluding that “specificity around identity is key”.
“In communicating, we often seek to oversimplify. But, when it comes to identity, ensuring the full nuances of someone’s identity are acknowledged as important,” said the Ipsos MORI researchers. “We see this when it comes to how ethnic and national identity interact with one another and how individuals navigate between these two aspects of their identity.”
Participants in the research succinctly illustrated the point. “I always say I’m Indian even though I am a British citizen. I am proud of my Heritage,” he said. Another explained: “My identity shouldn’t be defined by what ‘colour’ I am. I’m an individual and part of a diverse community with a diverse heritage.”
We are not the same
Understanding this type of nuance is at the heart of the BBC Audience’s BAME: We’re Not the Same report. It explores the culture, identity and heritage of the six largest ethnic minority groups in England and Wales – Indian, Pakistani, Black African, Black Caribbean, Bangladeshi and Chinese.
BBC Senior Audience Planner Helen Xa-Thomas began work on the report after noticing that content-makers had no tools to help them move away from “bucket terminology” and address the “nuance” within groups.
“All our identities are so multifaceted and complex. We are never just one entity of our identity,” she explained. “Labeling is a symptom of the shortcuts that we use as an industry. We all think very much demographic first and that can be problematic. For example, when we say ‘youth’ as if all young people are exactly the same.”
She continued: “It’s about understanding, culture and identity for different groups and making us more consciously aware of those differences. Because we are not the same.”
The BBC Audience’s report is backed by the Corporation’s Director of Creative Diversity, June Sarpong, who encouraged people “to grab a coffee and take a moment out to read this insightful BBC Audiences research”.
“This report starts to unpack ‘BAME’ because a ‘one size fits all’ approach doesn’t help us appreciate the complexity and richness of identities that fall within it,” she wrote in her foreword to the report. “One of the barriers we face when seeking to address the diversity deficit is the limits of our own perspective.”
She also pointed out that “The catch-all term of BAME may feel a like a convenient box for those interested in counting people. But when you fail to acknowledge the difference in people’s lived experience and history then people won’t feel like they count.”
Universal takeaways
As stated previously, four of the UK’s major broadcasters have committed to ensuring that people feel better represented by avoiding the use of BAME.
Marcus Ryder, Head of External Consultancies at Sir Lenny Henry Centre for Media Diversity, applauded the decision to adopt the report’s recommendation. He believes there are wider themes that can be taken from the research and applied by content-makers – trust, transparency and the need for bravery.
“As a Black person, when I see the Covid reports I am thinking ‘how does it affect Black people?’ When a journalist just stops short and says People of Colour, it feels as if they’re not representing me properly,’ he said. “So even if you don’t have the information you should acknowledge it as you’re acknowledging that question of how it affects me.
“Admit what you don’t know. If the story was ‘Covid affects People of Color or BAME more according to the latest statistics’ but there’s no breakdown, then say that they have not provided us with more detailed information as to how it affects individual specific races.”
Ryder also said content-makers need to ensure that they are not using BAME, or BIPOC or People of Color because they are “scared to use the term white”.
“Sometimes collective terms are used as a way to avoid using the word white and so we should also ensure that we aren’t just using a term as a way to avoid white,” he explained.
“Lots of studies have shown that white people often think of themselves as being raceless. If we want to have a serious conversation about race, then we need to ensure that we don’t just talk about race of non-white people.”
What each of the reports and research illustrate is that catch-all terminology erodes the trust of the audience, which could cause them to tune out (or worse, log off). As we address increasingly diverse audiences, there is an altogether reasonable expectation that our language, and its use, adapts.
Make it short. Show real stuff. This may seem obvious, but these are best practices in video length and content authenticity for Gen Z audiences.
Gen Z, born between 1998 and 2016, spends a lot of time watching videos on social media. And last year, Gen Z’s video consumption increased: Snapchat reported that Gen Z watched over an hour each day of video content on social media apps alone. They value video more than any other media platform, by a margin of roughly 2-to-1 over social, gaming, music or Google search, according to a recent study by DCN. They prefer video, specifically user-generated content, due to its relatability and personability.
Understanding that Gen Z viewers and consumers have different behaviors, values, and attitudes when it comes to video is important because it can impact your audience of the future, your strategy, and your revenue. It will also help you withstand shifts in viewer tastes and larger shifts in the media landscape. Building relationships with this generation of viewers, readers, consumers, starts now.
Video length on TikTok
Video length varies by platform, and there are a lot of platforms to choose from. Gen Z favors Instagram, Snapchat and TikTok, according to a Pew survey in 2021.
Video content on TikTok must be extremely short. In fact, 50 seconds is long, according to Erin Weaver, Group Nine Media’s Senior Director of Audience Development. For Gen Z-favored platforms Snapchat and TikTok, video length needs to be short and videos need to be fast-paced, according to Weaver. “On TikTok, I consider anything between 30 and 60 seconds to be almost the default. And then slightly longer is over one minute up to three minutes. We’ve seen some success with longer videos, as long as they’re really engaging and interesting.”
#DCYoungFly hitting the #CrankThat at the #HipHopAwards [https://www.tiktok.com/@bet]
Brittiany Cierra Taylor, director of audience development at BET, says she sees similar results. “Our audience development team has been trying out shorts and they’ve seen that they were amazing in getting new views, new viewers and from an ad perspective, we see more ads, more earned views. That shortness really is the key because we noticed that the sweet spot on TikTok is seven seconds where you see that jump that engagement,” she said.
“Our TikTok partners always encourage us to create shorter and more succinct videos, as they do tend to perform well on the platform,” says Kelsey Alpaio, an editor and producer with Harvard Business Review’s Ascend brand for young professionals, “But, that doesn’t mean long videos are off limits. The majority of our top-viewed videos are more than 50 seconds long. If people are interested in the content, they will stick around.”
Video length on YouTube
On YouTube, videos that are 2-4 minutes long work well for Harvard Business Review, but they also see success with videos that are longer, about 10-14 minutes each.
Scott LaPierre, Harvard Business Review’s senior editor for multimedia, says that for YouTube, trends around length are similar. Length is less important than topic and storytelling. LaPierre says HBR’s more authentic and honest videos on YouTube, which are casual, host- and personality-driven, perform about as well in the long run as their more traditional content. “Both have about an even number of breakout successes, and comparable average performers,” he says. “The video’s topic, and how compellingly it delivers on that topic are still the primary factors in the number of views and how long people watch, whether traditional or authentic in style.”
Short and medium-length videos at about two to nine minutes each work best on YouTube, for a broad reach. And longer (10-15 minutes) seems to work to deepen engagement with established fans, LaPierre said. “Shorter videos seem to have broader reach while longer videos seem to have deeper engagement. Long for us is around 10-15 minutes. Short is two to four. Most of our current video lineup is in the middle: six-to-nine-minute range. Anything over about 15 minutes does not perform great on our channel.” (Live video is a different conversation where lengths over 15 are more the norm.)
Optimize for story
“It really depends on the goal of the story and whatever length makes the storytelling complete,” says Zainab Khan, associate director of audience, video at The New York Times. “We might do a months-long investigation that merits a 12-minute video. What we see, because we edit our videos for pacing and storytelling, if a video is longer, we get more overall watch time. But we’re really rigorous about thinking about length so it fits the needs of the story. And in some cases, that means the best way to share a story means to do a quick 30-second snippet, showing viewers what’s happening on the ground.”
All of the digital content companies we spoke to said that storytelling trumps minutes and seconds. Video content should be as long as it needs to be, to tell an engaging story. LaPierre says, “Topic and storytelling generally trump length or style. So, my rule of thumb is: make it as short as possible, but no shorter.”
Content authenticity
Best practices for user-generated content are that video content must be low lit, not super polished, and not have a high production quality.Often, it is a selfie-style cell phone footage. It’s casual, host- and personality-driven. It is concise, engaging, and easy to produce. It shows people talking about what they care passionately about.
Harvard Business Review aims to make some of their videos in that user-generated style, LaPierre says. “For me, the best way to get authentic-feeling video is to have people talk about what they care passionately about,” he says.
Production values
Ascend Multimedia Producer Andy Robinson explains they try to find a sweet spot between having a polished feel and showing the real world. “My rule is, show the real stuff whenever possible. We’ve been leaning heavily on less-overly produced elements in our video content. Audiences can smell something that is highly produced, over scripted, over thought.”
Group Nine makes a point of putting people as the focal point of their UGC content, explains Weaver. “For PopSugar, a tutorial on applying makeup does a lot better than a product review or something that’s mostly focused on beauty products or a workout. You should see people doing the workouts, not so much like a description of the movements.”
At The New York Times, best practice for finding authenticity in a creator’s work is to have a deep understanding of the company’s values and to find common ground with their audience, Khan says. “It’s really important for us, when we want to build trust with our audience, we show our authentic selves. We literally put our reporters on screen in a way that helps the audience understand who is doing the reporting,” Khan says.
Gen Z has a bullshit detector
Gen Z’s desire for authenticity has been well documented. They want brands to be transparent, authentic and trustworthy. Gen Z audiences have spent their lives surrounded by digital technology. They’re incredibly discerning and know how to filter content that lacks the right tone, language, relevance or value. “What I love about Gen Z is that they hold companies more accountable,” Taylor says. “They’re doing the fact-checking, they’re doing the homework, they’re seeing if your staff resembles the world, if your content resembles the world year round. Is your message consistent and congruent in the content that you showed me? That’s actually one thing I love about them because it forces brands to be authentic.”
Authenticity is the way to grow audiences, Taylor explains. “I think that if you want to stay around, that is the basic component that audiences are resonating with. So, if you’re not going to be authentic, you’re not going to meet the KPIs you want, you’re not going to grow your audience, you’re not going to hit your revenue… So, from an audience perspective, a revenue perspective, authenticity is just the way to move forward.”
Be real, not trendy
“In the long term, if your identity and authenticity are dependent on a trend, you only last as long as that trend,” Khan says. “On the other hand, if your company has a handle on its core values, and what sets you apart from your peers and competitors, you can choose which trends to follow. And it means you can withstand shifts in the media and shifts in viewer taste.”
LaPierre says content authenticity connotes honesty, vulnerability, transparency, and relatability, which may not always have been top priorities for publishers. “And, we’ve seen some of the distrust in media that can result,” he says. “Show your flaws, show that your content is made by real people with real concerns that overlap with your audience’s, and show your work–it’s about building a trusting relationship over time.”
For their audience of the future, digital content companies need to put real intention behind the content they create and innovate constantly. As one expert put it, you need to think about who you’re talking to, and create content that is meaningful to them. It’s a lot of effort trying to please Gen Z, but if you’re not putting in the effort, you’re not going to get the results. This is your future audience, after all.
