In this respect, we join Fitzpatrick (2011) in exploring “the extent to which the means of media production and distribution are undergoing a process of radical democratization in the Web 2.0 era, and a desire to test the limits of that democratization”

Zeynep Tufekci recently wrote in the Times. “YouTube may be one of the most powerful radicalizing instruments of the 21st century.”

“Even the creators don’t always understand why it recommends one video instead of another,” says Guillaume Chaslot, an ex-YouTube engineer who worked on the site’s algorithm.

According to YouTube chief product officer Neal Mohan, 70 percent of views on YouTube are from recommendations—so the site’s algorithms are largely responsible for amplifying RT’s propaganda hundreds of millions of times.

These algorithms are invisible, but they have an outsized impact on shaping individuals’ experience online and society at large.

he gained popularity, particularly among young men, by promoting what he presented as a hyper-masculine, ultra-luxurious lifestyle.

TikTok offers an online resource center for creators seeking to learn more about its recommendation systems, and has opened multiple transparency and accountability centers where guests can learn how the app’s algorithm operates.

Unlike other mainstream social platforms, the primary way content is distributed on TikTok is through an algorithmically curated “For You” page; having followers doesn’t guarantee people will see your content. This shift has led average users to tailor their videos primarily toward the algorithm, rather than a following, which means abiding by content moderation rules is more crucial than ever.

[Rose] I trained instagram into thinking I have cats. I get a lot of cat adverts which are just cute pictures of cats now, 10/10 would recommend!

Alas, lawmakers are way behind the curve on this, demanding new "online safety" rules that require firms to break E2E and block third-party de-enshittification tools: https://www.openrightsgroup.org/blog/online-safety-made-dangerous/ The online free speech debate is stupid because it has all the wrong focuses: Focusing on improving algorithms, not whether you can even get a feed of things you asked to see; Focusing on whether unsolicited messages are delivered, not whether solicited messages reach their readers; Focusing on algorithmic transparency, not whether you can opt out of the behavioral tracking that produces training data for algorithms; Focusing on whether platforms are policing their users well enough, not whether we can leave a platform without losing our important social, professional and personal ties; Focusing on whether the limits on our speech violate the First Amendment, rather than whether they are unfair: https://doctorow.medium.com/yes-its-censorship-2026c9edc0fd

But there's another side to this playlistification of feeds: playlists and other recommendation algorithms are chokepoints: they are a way to durably interpose a company between a creator and their audience. Where you have chokepoints, you get chokepoint capitalism: https://chokepointcapitalism.com/

I removed the in-feed upvote button, making posts only up-votable at the bottom of the post itself. This increases the vote quality (if not the quantity).

use the number of views as a factor in determining the score of a post. Essentially using the “upvote rate” instead of pure upvote number. Score = (Upvotes - Views) / (Time in hours + 4) ^ Gravity Or removing time from consideration entirely, decaying posts the more they are read. Score = Upvotes / Views ^ Gravity These, however, have a bias against longer posts (although this is the case with the all these algorithms in general, but exacerbated here). Since longer posts (by virtue of them being long) may take time to read or are saved to read later, and may get a lot of views initially which actually degrade the score despite people finding the content valuable. It’s an interesting idea though and could be used for platforms where all posts are of a similar digestibility.

I dislike the separation of Trending and Newest. This is one of the main reasons for false negatives as new articles don’t receive many (if any) views. I’m thinking about randomly interspersing new articles in the trending feed to give them the potential of getting their first few votes. This (as ever) has an effect on quality, so has to be done with care.

The Hacker News algorithm This algorithm is fairly straight forward (although there’s some magic going on under the surface when it comes to moderation, shadow banning, and post pinning). This is slightly simplified, but in a nutshell: Score = Upvotes / Time since submission ^ Gravity Where Gravity = 1.7 As upvotes accumulate the score rises but is counterbalanced by the time since submission. Gravity makes it exponentially more difficult to rank as time goes by.

Once a post goes viral on Twitter, Hacker News, Reddit, or anywhere else off-platform, it has the potential to form a “Katamari ball” where it gets upvotes because it has upvotes (which means it gets more upvotes, because it has more upvotes, which means…well…you get it). This is also known as "the network effect", but I feel a Katamari ball better illustrates it.

The most common way is to log the number of upvotes (or likes/downvotes/angry-faces/retweets/poop-emojis/etc) and algorithmically determine the quality of a post by consensus.

This came in the context of weighing what she stood to gain and lose in leaving a staff job at BuzzFeed. She knew the worth of what editors, fact-checkers, designers, and other colleagues brought to a piece of writing. At the same time, she was tired of working around the “imperatives of social media sharing.” Clarity and concision are not metrics imposed by the Facebook algorithm, of course — but perhaps such concerns lose some of their urgency when readers have already pledged their support.

“It was 2017, I would say, when Twitter started really cracking down on bots in a way that they hadn’t before — taking down a lot of bad bots, but also taking down a lot of good bots too. There was an appeals process [but] it was very laborious, and it just became very difficult to maintain stuff. And then they also changed all their API’s, which are the programmatic interface for how a bot talks to Twitter. So they changed those without really any warning, and everything broke.

He and his fellow bot creators had been asking themselves over the years, “what do we do when the platform [Twitter] becomes unfriendly for bots?”

Since most of our feeds rely on either machine algorithms or human curation, there is very little control over what we actually want to see.

We have to endlessly scroll and parse a ton of images and headlines before we can find something interesting to read.

…and they are typically sorted: chronologically: newest items are displayed firstthrough data: most popular, trending, votesalgorithmically: the system determines what you see through your consumption patterns and what it wants you to seeby curation: humans determine what you seeby taxonomy: content is displayed within buckets of categories, like Wikipedia Most media entities employ a combination of the above.

First is that it actually lowers paid acquisition costs. It lowers them because the Facebook Ads algorithm rewards engaging advertisements with lower CPMs and lots of distribution. Facebook does this because engaging advertisements are just like engaging posts: they keep people on Facebook. 

Posting a new algorithm, poem, or video on the web makes it a vailable, but unless appropriate recipients notice it, the originator has little chance to influence them.

The current mass media such as t elevision, books, and magazines are one-directional, and are produced by a centralized process. This can be positive, since respected editors can filter material to ensure consistency and high quality, but more widely accessible narrowcasting to specific audiences could enable livelier decentralized discussions. Democratic processes for presenting opposing views, caucusing within factions, and finding satisfactory compromises are productive for legislative, commercial, and scholarly pursuits.

An internal message characterizing Zuckerberg’s reasoning says he wanted to avoid new features that would get in the way of “meaningful social interactions.” But according to Facebook’s definition, its employees say, engagement is considered “meaningful” even when it entails bullying, hate speech, and reshares of harmful content.

At the time, Facebook was already weighting the reactions other than “like” more heavily in its algorithm—meaning posts that got an “angry” reaction were more likely to show up in users’ News Feeds than posts that simply got a “like.” Anger-inducing content didn’t spread just because people were more likely to share things that made them angry; the algorithm gave anger-inducing content an edge. Facebook’s Integrity workers—employees tasked with tackling problems such as misinformation and espionage on the platform—concluded that they had good reason to believe targeting posts that induced anger would help stop the spread of harmful content.

Facebook has dismissed the concerns of its employees in manifold ways. One of its cleverer tactics is to argue that staffers who have raised the alarm about the damage done by their employer are simply enjoying Facebook’s “very open culture,” in which people are encouraged to share their opinions, a spokesperson told me.

Fukuyama's answer is no. Middleware providers will not see privately shared content from a user's friends. This is a good answer if our priority is privacy. It lets my cousin decide which companies to trust with her sensitive personal information. But it hobbles middleware as a tool for responding to her claims about vaccines. And it makes middleware providers far less competitive, since they will not be able to see much of the content we want them to curate.

Facebook deploys tens of thousands of people to moderate user content in dozens of languages. It relies on proprietary machine-learning and other automated tools, developed at enormous cost. We cannot expect [End Page 169] comparable investment from a diverse ecosystem of middleware providers. And while most providers presumably will not handle as much content as Facebook does, they will still need to respond swiftly to novel and unpredictable material from unexpected sources. Unless middleware services can do this, the value they provide will be limited, as will users' incentives to choose them over curation by the platforms themselves.

The First Amendment precludes lawmakers from forcing platforms to take down many kinds of dangerous user speech, including medical and political misinformation.

Fukuyama's work, which draws on both competition analysis and an assessment of threats to democracy, joins a growing body of proposals that also includes Mike Masnick's "protocols not platforms," Cory Doctorow's "adversarial interoperability," my own "Magic APIs," and Twitter CEO Jack Dorsey's "algorithmic choice."

One of the reasons for this situation is that the very media we have mentioned are so designed as to make thinking seem unnecessary (though this is only an appearance). The packag­ing of intellectual positions and views is one of the most active enterprises of some of the best minds of our day. The viewer of television, the listener to radio, the reader of magazines, is presented with a whole complex of elements-all the way from ingenious rhetoric to carefully selected data and statistics-to make it easy for him to "make up his own mind" with the mini­mum of difficulty and effort. But the packaging is often done so effectively that the viewer, listener, or reader does not make up his own mind at all. Instead, he inserts a packaged opinion into his mind, somewhat like inserting a cassette into a cassette player. He then pushes a button and "plays back" the opinion whenever it seems appropriate to do so. He has performed ac­ceptably without having had to think.

Others are asking questions about the politics of weblogs – if it’s a democratic medium, they ask, why are there so many inequalities in traffic and linkage?

One person writing a tweet would still qualify for free-speech protections—but a million bot accounts pretending to be real people and distorting debate in the public square would not.

In this respect, we join Fitzpatrick (2011) in exploring “the extent to which the means of media production and distribution are undergoing a process of radical democratization in the Web 2.0 era, and a desire to test the limits of that democratization”

In 2017, Chris Cox, Facebook’s longtime chief product officer, formed a new task force to understand whether maximizing user engagement on Facebook was contributing to political polarization. It found that there was indeed a correlation, and that reducing polarization would mean taking a hit on engagement. In a mid-2018 document reviewed by the Journal, the task force proposed several potential fixes, such as tweaking the recommendation algorithms to suggest a more diverse range of groups for people to join. But it acknowledged that some of the ideas were “antigrowth.” Most of the proposals didn’t move forward, and the task force disbanded. Since then, other employees have corroborated these findings. A former Facebook AI researcher who joined in 2018 says he and his team conducted “study after study” confirming the same basic idea: models that maximize engagement increase polarization. They could easily track how strongly users agreed or disagreed on different issues, what content they liked to engage with, and how their stances changed as a result. Regardless of the issue, the models learned to feed users increasingly extreme viewpoints. “Over time they measurably become more polarized,” he says.

“When you’re in the business of maximizing engagement, you’re not interested in truth. You’re not interested in harm, divisiveness, conspiracy. In fact, those are your friends,” says Hany Farid, a professor at the University of California, Berkeley who collaborates with Facebook to understand image- and video-based misinformation on the platform.

The few people who are willing to defend these sites unconditionally do so from a position of free-speech absolutism. That argument is worthy of consideration. But there’s something architectural about the site that merits attention, too: There are no algorithms on 8kun, only a community of users who post what they want. People use 8kun to publish abhorrent ideas, but at least the community isn’t pretending to be something it’s not. The biggest social platforms claim to be similarly neutral and pro–free speech when in fact no two people see the same feed. Algorithmically tweaked environments feed on user data and manipulate user experience, and not ultimately for the purpose of serving the user. Evidence of real-world violence can be easily traced back to both Facebook and 8kun. But 8kun doesn’t manipulate its users or the informational environment they’re in. Both sites are harmful. But Facebook might actually be worse for humanity.

Every time you click a reaction button on Facebook, an algorithm records it, and sharpens its portrait of who you are.

Meanwhile, politicians from the two major political parties have been hammering these companies, albeit for completely different reasons. Some have been complaining about how these platforms have potentially allowed for foreign interference in our elections.3 3. A Conversation with Mark Warner: Russia, Facebook and the Trump Campaign, Radio IQ|WVTF Music (Apr. 6, 2018), https://www.wvtf.org/post/conversation-mark-warner-russia-facebook-and-trump-campaign#stream/0 (statement of Sen. Mark Warner (D-Va.): “I first called out Facebook and some of the social media platforms in December of 2016. For the first six months, the companies just kind of blew off these allegations, but these proved to be true; that Russia used their social media platforms with fake accounts to spread false information, they paid for political advertising on their platforms. Facebook says those tactics are no longer allowed—that they've kicked this firm off their site, but I think they've got a lot of explaining to do.”). Others have complained about how they’ve been used to spread disinformation and propaganda.4 4. Nicholas Confessore & Matthew Rosenberg, Facebook Fallout Ruptures Democrats’ Longtime Alliance with Silicon Valley, N.Y. Times (Nov. 17, 2018), https://www.nytimes.com/2018/11/17/technology/facebook-democrats-congress.html (referencing statement by Sen. Jon Tester (D-Mont.): “Mr. Tester, the departing chief of the Senate Democrats’ campaign arm, looked at social media companies like Facebook and saw propaganda platforms that could cost his party the 2018 elections, according to two congressional aides. If Russian agents mounted a disinformation campaign like the one that had just helped elect Mr. Trump, he told Mr. Schumer, ‘we will lose every seat.’”). Some have charged that the platforms are just too powerful.5 5. Julia Carrie Wong, #Breaking Up Big Tech: Elizabeth Warren Says Facebook Just Proved Her Point, The Guardian (Mar. 11, 2019), https://www.theguardian.com/us-news/2019/mar/11/elizabeth-warren-facebook-ads-break-up-big-tech (statement of Sen. Elizabeth Warren (D-Mass.)) (“Curious why I think FB has too much power? Let's start with their ability to shut down a debate over whether FB has too much power. Thanks for restoring my posts. But I want a social media marketplace that isn't dominated by a single censor. #BreakUpBigTech.”). Others have called attention to inappropriate account and content takedowns,6 6. Jessica Guynn, Ted Cruz Threatens to Regulate Facebook, Google and Twitter Over Charges of Anti-Conservative Bias, USA Today (Apr. 10, 2019), https://www.usatoday.com/story/news/2019/04/10/ted-cruz-threatens-regulate-facebook-twitter-over-alleged-bias/3423095002/ (statement of Sen. Ted Cruz (R-Tex.)) (“What makes the threat of political censorship so problematic is the lack of transparency, the invisibility, the ability for a handful of giant tech companies to decide if a particular speaker is disfavored.”). while some have argued that the attempts to moderate discriminate against certain political viewpoints.

I literally couldn’t remember when I’d last looked at my RSS subscriptions. On the surface, that might seem like a win: Instead of painstakingly curating my own incoming news, I can effortlessly find an endless supply of interesting, worthwhile content that the algorithm finds for me. The problem, of course, is that the algorithm isn’t neutral: It’s the embodiment of Facebook and Twitter’s technology, data analysis, and most crucial, business model. By relying on the algorithm, instead of on tags and RSS, I’m letting an army of web developers, business strategists, data scientists, and advertisers determine what gets my attention. I’m leaving myself vulnerable to misinformation, and manipulation, and giving up my power of self-determination.

Schemas aren't neutral

Third, content collapse puts all types of information into direct competition. The various producers and providers of content, from journalists to influencers to politicians to propagandists, all need to tailor their content and its presentation to the algorithms that determine what people see. The algorithms don’t make formal or qualitative distinctions; they judge everything by the same criteria. And those criteria tend to promote oversimplification, emotionalism, tendentiousness, tribalism — the qualities that make a piece of information stand out, at least momentarily, from the screen’s blur.

The biggest drawback of algorithmic feeds is that you might be looking at irrelevant content. When you see something on your timeline and want to comment, you will have to check the timestamp to see if your comment is still relevant or not.

Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.

As people start following more and more accounts on a social network, they reach a point where the number of candidate stories exceeds their capacity to see them all. Even before that point, the sheer signal-to-noise ratio may decline to the point that it affects engagement. Almost any network that hits this inflection point turns to the same solution: an algorithmic feed.

The social media browser plugins. Here’s the killer feature. Create at least one (could be many competing) browser plugins that enable you to (a) select feeds and then (b) display them alongside a user’s Twitter, Facebook, etc., feeds. (This could be an adaptation of Greasemonkey.) In other words, once this feature were available, you could tell your friends: “I’m not on Twitter. But if you want to see my Tweet-like posts appear in your Twitter feed, then simply install this plugin and input my feed address. You’ll see my posts pop up just as if they were on Twitter. But they’re not! And we can do this because you can control how any website appears to you from your own browser. It’s totally legal and it’s actually a really good idea.” In this way, while you might never look at Twitter or Facebook, you can stay in contact with your friends who are still there—but on your own terms.

While the NTK Network does not have a large audience of its own, its content is frequently picked up by popular conservative outlets, including Breitbart.

My relationship is a lot healthier with blogs that I visit when I please. This is another criticism I have with RSS as well—I don’t want my favorite music blog sending me updates every day, always in my face. I just want to go there when I am ready to listen to something new. (I also hope readers to my blog just stop by when they feel like obsessing over the Web with me.)