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In recent years, YouTube has focused their safety policies on demonetizing creators that participate in off-platform behaviors or create content that may be considered harmful, even if they do not explicitly violate the platform’s rules. (Some examples include David Dobrik and Dan Bongino, the latter of whom was eventually banned.) However, a deep dive under the hood of the platform shows that these creators can easily use the platform to direct people to make money in other ways.

In a new paper, a team from Cornell Tech in collaboration with the Swiss Federal Institute of Technology Lausanne (EPFL) recommends that, if YouTube wants to truly impact these creators, they develop a shared database of demonetized users in conjunction with Patreon, Twitch, and other alternative monetization sites, to prevent them from using each other’s platforms.

The Cornell Tech team reviewed 71 million videos on YouTube that were published by more than 136,000 popular content creators with more than 10,000 subscribers to understand how creators, including channels that distribute problematic content, employ alternative monetization strategies that could allow them to circumvent the effects of any “demonetization” by YouTube.

In their new paper the researchers found that, when compared to random channels of similar activity, popularity, age, and with similar content fringe content creators are:

  • more likely to adopt alternative monetization
  • use alternative monetization methods more frequently
  • more likely to diversify their alternative monetization efforts

 

“We found that channels that establish alternative monetization strategies actually become more productive on the platform,” said Cornell Tech doctoral researcher Yiqing Hua, co-lead author on the new paper alongside Cornell Tech professors Thomas Ristenpart and Mor Naaman. A collaboration with Manoel Horta Ribeiro and Robert West of the Swiss Federal Institute of Technology in Lausanne, the paper will be presented this November at the annual ACM Conference on Computer-Supported Cooperative Work And Social Computing.

YouTube monetization flowchart

The researchers learned that creators who produce problematic content thrive from the attention they get from their supporters through alternative monetization. Looking at even just a small sliver of the overall YouTube analytics, Hua found that at least a dozen fringe channels have made more than $100,000 on Patreon alone.

While the problem is not limited to YouTube and Patreon, the two platforms have an outsized influence in this space. The new paper shows that 61 percent of fringe channels use an alternative monetization strategy, compared with 18 percent of channels overall.

The team generally found that the practice of demonetization on YouTube is less effective because of the opportunities to employ alternative monetization strategies, citing Alex Jones’ InfoWars YouTube channel as a high-profile example. Before the channel’s ban in late 2018, the channel featured over 30,000 videos and gathered more than two million subscribers. Despite being demonetized during this period, Jones still managed to amass millions of dollars each year through affiliate links and alternative monetization strategies. This paper shows that many fringe content creators benefit from alternative monetization and are able to maintain an income while producing content.

“We were surprised to discover how much money these creators are making from alternative monetization platforms,” Hua said. “Creators make money on YouTube through engagement, including number of views and minutes watched, but fringe creators that are demonetized focus on ensuring their fans and followers want to support and pay for their work.”

However, the researchers suggested that alternative monetization should not be banned from the platform, as these strategies also empower creators who are often in a vulnerable position when YouTube policies become ambiguous. Alternative monetization also allows for different incentives that may encourage higher-quality content compared to the ad-revenue model.

This research was funded in part by the Siegel Family Endowment and by the National Science Foundation.






By Tom Fleischman

When most people think of augmented reality (AR) or virtual reality (VR), they likely think of gaming headsets and Meta Quest 2, or a similar diversion from the real world.

But what if AR/VR could be used to improve the telehealth experience, help detect cracks in roads and bridges, or calibrate mobile-phone cameras to better recognize faces of color?

These and many other applications are exactly what Abe Davis, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, and Harald Haraldsson, director of the XR Collaboratory at Cornell Tech, will explore with a two-year, $1.8 million grant from Meta, formerly known as Facebook, and Spark AR, which powers AR experiences for Meta’s apps and devices.

Cornell Bowers CIS will receive $675,000 in funding; the XR Collaboratory will receive $1.125 million. The grant will support research, teaching and diverse undergraduate opportunities in the field of augmented reality at both Cornell Bowers CIS and Cornell Tech.

“As a new faculty, you’re still figuring out how to how to best enable the research that you want to do,” said Davis, who arrived at Cornell in 2020 after a one-year postdoc assignment at Cornell Tech.

“This gift helps tremendously with that,” Davis said. “And it creates a clear opportunity to pursue this line of research, which is something that I’m really excited about but requires equipment and more people to be involved.”

“This will unlock new activities for us at the New York City campus,” Haraldsson said, “and also allows us to continue the work in XR (augmented, virtual and mixed reality) that we’ve been doing for the last three years.”

“At Meta, we’re working to usher in a truly dynamic, useful augmented reality future that will change the way people interact with their surroundings, and each other,” said Sue Young, head of AR Glasses at Reality Labs at Meta. “Support for pioneering efforts like Cornell’s is key to helping realize this vision fully and empowering the next-generation of XR builders, creators and researchers.”

Part of Davis’ goal with the grant is to expand AR/VR offerings on the Ithaca campus, similar to what Haraldsson is doing at Cornell Tech.

Projects at XRC have included remote collaboration for health care applications; locomotion techniques for VR; surface reflectance capture using AR headsets; and novel input techniques for VR.

“There’s a lot of interest in AR and VR among students [in Ithaca],” Davis said. “And Harald and the folks at Cornell Tech have built a nice infrastructure at Cornell Tech, which we’d really like to replicate in Ithaca, so that we can get more students involved.”

Haraldsson hopes the grant will enable expansion of the XR Collaboratory’s work in three key areas – instructional, project and community activities.

“We have already connected a community of XR researchers, faculty and students up in Ithaca, here at Cornell Tech and also at Weill Cornell Medicine,” Haraldsson said. “And this gift will help us accelerate activities in each of our categories.”

Diversity and inclusion are a major point of emphasis, both investigators said. Part of that, they said, is making sure everyone is starting from the same general knowledge base, and giving a boost to those who need it.

“Sometimes in computer science, you assume that the students know how to use all the development tools, such as how to do complex debugging, but that is not always the case, even at the master’s level,” Haraldsson said. “And this grant will help us to provide an additional layer of instruction, to be more inclusive of students with different backgrounds in terms of computer science, to get everybody on the same page.”

One line of research that Davis hopes to explore has to do with the mobile phone cameras that produce the pictures that fuel social media. Devices are calibrated to recognize a limited range of colors, Davis said, and white, Anglo-Saxon faces have long served as the benchmark for camera design.

As a result, he said, most cameras fail to faithfully capture nonwhite faces. The ability for people to calibrate their devices in a more personal way is one research avenue Davis hopes to pursue.

“I believe part of this shared interest with Meta,” he said, “is being able to build systems that are more customizable to the needs of individual users.”

This story originally appeared in the Cornell Chronicle.


By Jess Campitiello

When we think of having our own handy multi-purpose robots, we tend to picture something out of Star Wars or The Jetsons — something futuristic and far out of reach. The one robot that’s actually entered our world, the Roomba, has only one function (to clean) and is just about the least intelligent it can be at doing so.

However, the useful technology of the ‘future’ may not be as far off as we think. Robots can realistically become our best day-to-day allies, but how? Maria Bauza Villalonga, PhD student at MIT, hosted a Seminar @ Cornell Tech to help answer this question.

Right now, there are two broad categories of robots: industry robots and home robots.

Industry robots — such as those used to manufacture automobiles — are extremely precise in the tasks that they perform; however, each robot is designed to do one task, and one task only. They have narrow manipulation skills, function properly under very specific conditions, and engineers must be brought in to program each robot to do its own task. This means that although they do their jobs well, small changes in manufacturing can cause old models to become unusable and un-reusable.

Home robots, on the other hand, have simple yet imprecise skills. These models are capable of working successfully in many different locations with varying conditions and environments, but they do their tasks with little precision. For example, a basic Roomba’s movements are completely random, running over the same spot multiple times and bumping into anything in their paths. Even with newer, high-tech models featuring optical sensors and laser emitters, their movements “appeared confused” as they moved “in fits and starts, constantly pivoting in different directions.” While they are versatile and user-friendly, they are far from being as efficient as industry machines.

Generality vs Precision chart

How can we marry these two ideas to create a robot that is both general and precise? Bauza posited three things a model must do in order to achieve successful “precise robotic generalization”.

  1. Actively learning about what matters
    When it comes to robots interacting with the world around them, recognizing object shapes is key. Using both visual and tactile sensors to recognize unique shapes, a robot can identify objects without having any real experience with them. A hurdle to overcome here is finding a way to be able to do this efficiently regardless of an object’s pose, or position, relative to the machine. The ultimate goal is for the robot to reconfigure an object’s position only once before effectively completing the task it was programmed to perform with it.
  2. Making sense of its observations
    Once an object is successfully identified, the robot should know how to properly handle it. This requires the robot to understand the language of forces. For instance, the robot’s gripper must exert enough strength to counteract gravity and pick up the object, but not too much to damage it.
  3. Continuously updating its knowledge
    By running the robot through simulations and using it in real-world applications, it will continuously improve its performance and task efficiency. While current approaches to this require a researcher to step in to gather data and make adjustments, an ideal system would close the loop between simulation and real-world data while cutting out the need for researcher intervention.

By focusing on these three key points, Bauza argues that we can get robots to be very precise, skillful, multi-purpose, and adaptive without having to rely on trained operators to program them.

Jess Campitiello is the Digital Communications Assistant at Cornell Tech.