YouTube Monetization and Censorship by Proxy: A Machine Learning Prospective: Review

Authors:

As consumption of digital content has climbed, so has censorship of the content. The censorship has only increased with companies more sensitive to the type of content that they tie their advertising to on digital platforms. Demonetization of videos is a primary way content is censored on YouTube. The goal of the paper is to understand whether a set of YouTube characteristics (i.e., attributes) can predict whether changing attribute values will lead to the censorship (demonization) of a video. The methodology is helpful to free speech advocates who may believe content is being unfairly or unlawfully censored.

The aim of the study:

In this article analyzing some of the scrutinize and challenges the YouTube monetization process faces. 

The first problem is Flawed Monetization Policy. Youtubers must follow all the community guidelines, terms of service, copyright policy, Google Ad-sense program policy, channel monetization policy, and ad-friendly content guidelines and even if YouTube can still limit the monetization or remove it on a video-by-video basis.

The second one is Biased Algorithm. YouTube guarantees that any patterns that indicate any inadvertent biases or falsifications associated with its algorithm are concealed from public view.

The third problem is Undisclosed Data. YouTube does not share the results of this censorship algorithm at all, which means that the authors cannot guess which videos will be actively promoted and which will lose monetization for nothing.

This research is aimed to learning algorithm censor content through demonetization with a proxy and machine learning. This article uses machine learning techniques to gain insight into YouTube’s demonization algorithm, which is said to be a means of censoring content.

In the beginning the authors describe all possible methods of content moderation on YouTube platform. These methods include the following five:

• Spam and Deceptive Practices
• Sensitive Content
• Violent or Dangerous Content
• Regulated Goods (e.g., firearms)
• Copyrighted Content

Monetization is one of the few ways for content creators to make money on the platform. It is important to note that the decision as to where to monetize a video appears to be automated by YouTube. The demonetization of videos and content on YouTube suppresses that content and drastically affects a user’s ability to find the content and negatively impacts a content creator in trying to get their content to an audience. 

In the next part, the authors describe what content is most often blocked without a reason. The authors tell us that the most popular content for monetization removal is so-called harmful or dangerous content. Administrators and machine learning can put any content they don’t like in violation of this category. However, the authors can dispute this decision. Also, demonetization can act as an indirect means for censorship of content on the YouTube platform.

Methodology:

On this picture shows an overview of the proposed methodology and consists of all steps.

Pic 1.

The authors start with the process of data collection. They pre-classified videos from left-wing, moderate and right-wing by the transparency.tube — this is webpage, that employed machine learning techniques to classify YouTube channels according to their content. There are taken about 400 different videos: 

• Randomly selected videos from ten channels from each category on Transparency.Tube.
• The same number of “Left” and “Right” channels were selected in order to balance the dataset.
• Five most recent videos from each channel.
• Ensured a broad coverage of video types, i.e., videos that varied in duration, long-form, recorded podcasts, short clips, and live streams.

Then, the set of features listed in this picture and user comments from 400 seed YouTube videos used YouTube’s own API.

Tab 1.

The authors used Random Forest, Liner Regression, and SVM machine learning algorithms to preprocess the metadata. They cannot be applied specifically to the text of the comments collected under the video, because many comments were written from cell phones and contain typos, slang words, jargon and abbreviations.

Therefore, four stages of pretreatment were done:  

• Noun, verb and adjective extraction — Part-of-speech tagging (POS) function of the Natural Language Toolkit, NLTK4.
• Stop-word extraction — stop-words in comments are removed.
• Lemmatization — The Natural Language Toolkit (NLTK) will not count different forms of a word (test, testing, tests, tested).
• Vectorization — Script was run to turn the array into a single string with spaces separating each tag.

Then they do next stage of pre-processing. It called Auto-Labelling — the script looked for embedded HTML tags when playing a video in the dataset to determine if advertisements were present and/or played, then the video was deemed to be monetized by YouTube.

In the main analysis they would find if the set of attributes can act as a proxy where change in the features values can fall into one of the two classes: Monetized or De-Monetized. In the author’s methodology, as shown in the picture 1, they chose four training methods: C 4.5 – simple decision tree classifier, Random Forest – advanced decision tree classifier, Linear Regression (LR), and Support Vector Machine (SVM). They built the model this way: All of the machine learning machines take as input the attributes listed in the table 1. Most of the attributes were chosen based on YouTube’s statements about what factors are considered by the platform when monetizing.

Results:

As a result of the study, the logistic regression (LR) model achieved an accuracy of about 70% in predicting whether a video would make money. The two most important characteristics were the length of the channel and the number of subscribers. This is because the longer the video, the more ads YouTube can place and the more likely it is to make money. It’s the same with the number of subscribers. Channels with a large number of subscribers effectively feed YouTube’s monetization algorithm and win its favors.

The SVM (support vector machine) model also achieved a prediction accuracy of around 70% when converting video into cash. The two important factors were the number of subscribers to the channel and the number of views of the channel, two highly correlated variables.

The Random Forest (RF) model showed the highest prediction accuracy of 85%. The four predictive models generated suggest that the political ideology of YouTube channels – left, right or center-right is irrelevant to YouTube’s algorithms that determine the monetization of each video. This conclusion undermines repeated accusations that YouTube “censors” right-wing content.

Limitations:

• The study was conducted in the USA on videos and channels aimed primarily at U.S. audiences.
• YouTube’s monetization algorithm is designed with advertisers in mind, which can act as a form of indirect censorship towards creators.
• A video is considered “monetized” if it contains advertising. The article does not highlight the frequency of advertising in the video.

My opinion:

The main contribution of the article to analyzing YouTube censorship is to answer the question of there is a wrongful restriction of right-wing political content. It is crucial to know that YouTube is a fair platform, and everyone can do his videos without any unfair restrictions. It is very important, that there is a website with the freedom content. 

However, the as we understand the research has made with the USA segment of YouTube, so in other regions may be another conclusion of this research. It would be a great point for the future works to analyze not so democratic regions. For example, some governments can create requests to YouTube to remove content that contradicts general policy in the country. And in many cases YouTube goes along with totalitarian and authoritarian states and removes these videos. 

Another real case in point is the complete removal of monetization in the Russian Federation after the war with Ukraine began. YouTube has disabled monetization completely, which means that the system of promoting videos on the platform has become much worse. This affects all independent media and free journalists, who have seen their salaries cut many times over, which means they will not be able to truthfully report on the crimes of the Russian Federation.

Vasily Levenstam

Sources: 

1. Anthony Zappin, Haroon Malik, Elhadi M. Shakshuki, David A. Dampier, YouTube Monetization and Censorship by Proxy: A Machine Learning Prospective, Procedia Computer Science, Volume 198, 2022, Pages 23-32, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2021.12.207
2. Webstite: https://transparency.tube/

NFT way to make money or just another SCAM

In this article I want to talk about the potential of NFT, how you can make money and what you need to do for that. Also, I would like to tell you what the pitfalls may be and why you should not take these methods as an easy and quick way to earn money.

Let’s start with the definition of NFT. An NFT is a digital asset that represents real-world objects like art, music, in-game items and videos. They are bought and sold online, frequently with cryptocurrency, and they are generally encoded with the same underlying software as many cryptos.

It’s becoming apparent that NFT is going mainstream in 2021 and early 2022. From every corner we begin to hear a combination of these three letters. Around the world, bloggers, celebrities and athletes, as well as local trendsetters are trying to jump on the hype wave and release their blockchain product. Famous rap singer Snoop Dogg revealed that he is the Cozomo de’ Medici who has amassed a collection of 247 NFTs, including nine CryptoPunks. DappRadar values it at $17.7 million. NFT is already used by Deadmau5, Richie Hawtin, Daft Punk, Steve Aoki, Grimes, Mike Shinoda, The Weeknd, Charli XCX, 3LAU and many other artists. Nevertheless, many Internet users have a vague idea of what NFT is. And the more unclear are the ways to make money on it.

First way

The first way to make money is through NFT giveaways or, to put it in English, NFT Airdrops. On the Internet, of course, you will encounter the term airdrop. We used the word giveaway to simplify your understanding. In simple words, NFTs are literally giving away to lucky people for fulfilling certain conditions. In order not to create chaos in your head, we will derive the following types of giveaways.

NFT holder airdrop. NFT holder airdrop. NFT projects can give away different stuff to attract the attention of the community and investors. But not just for fun. The only requirement is to be an NFT holder. Here is an example. One of the most popular NFT projects Bored Apes pleased their holders. The owners of “bored apes” received “serums” which, when taken, turned their monkeys into unique and different mutants. The marketplace had an offer of ETH 269 (about $900,000) to the owner of the monkey who received the free serum.

NFT platform airdrop. Gifts from trading platforms. If you’re an early user of a blockchain-based marketplace, rest assured that the developers won’t forget about you and prepare a gift. It doesn’t even matter if you’re buying, selling, or creating something on the marketplace. For example, huge platforms like Rarible and Supergreater have done giveaways in their time. If you think the train has sailed and new trading platforms aren’t coming to market, you’re very much mistaken. There is a huge demand for NFTs right now, and that’s why new venues are popping up more and more often.

The next type is giveaways for everyone. NFT projects in the early stages resort to this method to attract attention to themselves. In order to get the long-awaited NFT for nothing, you need to meet certain conditions, which are usually limited to subscribing to the project’s Twitter and Discord. Here’s an example of perhaps the biggest giveaway. The CryptoPunks project in 2017 arranged a giveaway of 5,000 NFT copies absolutely free, users only had to pay for “minting” objects. P.S. Minting is the process of creating an NFT and writing it into a blockchain. Thus, the lucky ones got their copies. The cheapest punk now costs $100,000 on the market, and the most expensive $90.5 million.

Second way

The next way to make money is to create your own NFT. This method is hardly suitable for beginners. Every person in the field has thought of creating his or her own product based on blockchain. However, there are millions of works lying on the platforms, which did not find a response from investors.

Third way

The third way to make money is “play to earn”. That’s right, blockchain technology allows gamers around the world to do what they love and make huge amounts of money in the process. Each blockchain-based game has its own ecosystem and economy. Players can earn various NFT items for their achievements in the game, which can later be sold on secondary markets. In addition, traders buy and sell in-game items on secondary markets, locking in profits. Perhaps the most successful project in this area is Axie Infinity. Total sales of in-game items amount to $1,381,429,245. Tweets from gamers who have gambled their way into homes in the Philippines are strolling around the Internet. And a million gamers today have steady earnings thanks to this game. Axie also has its own AXS tokens, which the developers also gave away to users. One player was so lucky that $2 million worth of tokens were sent to his account.

Fourth way

The fourth way to make money – trading NFT on marketplaces. Here everything is very simple – buy cheaper, sell higher. Pure speculation. At this method I do not see the point to stop.

Fifth way

The fifth way to make money is steaking. Steaking is a way of passive income in which holders pledge their NFTs, thereby ensuring that the blockchain works. Investors get their well-deserved rewards for doing so. It is de facto passive income from NFTs.

Sixth way

And the last way to make money is through tweets. I don’t want to refer this method to the airdrop I mentioned earlier. Twitter giveaways are typical for projects that have small budgets and are at the very beginning of their journey. The chances that the NFT you win will give you get money are very small. However, all you need to do with these giveaways is to retweet, subscribe to the project’s channel, subscribe to discord, and tag three friends. You literally have nothing to lose by participating in this giveaway.

Thus, there are many ways to make money from NFT. This sphere is actively developing. Therefore, you should not lose the opportunity to make a lot of money and change your life for the better.

One of the problems with the NFT is the copyright issue. NFT itself does not protect content creators from theft and loss of revenue. It is physically impossible to steal the original token, but nothing prevents you from copying the file to which the auctioned NFT is attached and creating your own token. Gian M. Volpicelli, senior writer at WIRED, write in his article, that “80% of tokens created on OpenSea’s NFT marketplace are plagiarism, scam, and spam.” According to Wired, OpenSea is hostage to its own success. In December 2020, the company allowed all users to mint tokens for free, and later also cancelled the mandatory verification of NFT collections before listing on the marketplace.

In conclusion, I would like to mention that the decision to invest in such high-risk investment instruments is solely your decision. Many people earned millions of dollars on NFT; at the same time a lot of people lost their money entering this risky business.

References:
https://www.wired.com/story/opensea-nfts-twitter/
https://www.forbes.com/advisor/investing/nft-non-fungible-token/
https://twitter.com/SnoopDogg/status/1440038460417474567/

Vasily Levenstam