#EU #SocialMedia #TikTok #DSA #Algorithms #RecommendationEngines: "TikTok users in Europe will be able to switch off the personalized algorithm behind its For You and Live feeds as the company makes changes to comply with the EU’s Digital Services Act (DSA). According to TikTok, disabling this function will show users “popular videos from both the places where they live and around the world” instead of content based on their personal interests.

These changes relate to DSA rules that require very large online platforms to allow their users to opt out of receiving personalized content — which typically relies on tracking and profiling user activity — when viewing content recommendations. To comply, TikTok’s search feature will also show content that’s popular in the user’s region, and videos under the “Following” and “Friends” feeds will be displayed in chronological order when a non-personalized view is selected."

https://www.theverge.com/2023/8/4/23819878/tiktok-fyp-algorithm-eu-dsa-personalization-data-tracking

#SocialMedia #Twitter #Algorithms #ML #RecommendationEngines: "As social media continues to have a significant influence on public opinion, understanding the impact of the machine learning algorithms that filter and curate content is crucial. However, existing studies have yielded inconsistent results, potentially due to limitations such as reliance on observational methods, use of simulated rather than real users, restriction to specific types of content, or internal access requirements that may create conflicts of interest. To overcome these issues, we conducted a pre-registered controlled experiment on Twitter's algorithm without internal access. The key to our design was to, for a large group of active Twitter users, simultaneously collect (a) the tweets the personalized algorithm shows, and (b) the tweets the user would have seen if they were just shown the latest tweets from people they follow; we then surveyed users about both sets of tweets in a random order.

Our results indicate that the algorithm amplifies emotional content, and especially those tweets that express anger and out-group animosity. Furthermore, political tweets from the algorithm lead readers to perceive their political in-group more positively and their political out-group more negatively. Interestingly, while readers generally say they prefer tweets curated by the algorithm, they are less likely to prefer algorithm-selected political tweets. Overall, our study provides important insights into the impact of social media ranking algorithms, with implications for shaping public discourse and democratic engagement."
https://arxiv.org/abs/2305.16941

#SocialMedia #USA #Section230 #CDA #ContentModeration #RecommendationEngines: "When you do a search online, you ask, “Of the millions of websites out there, tell me the one, oh Google, or oh Bing, which you recommend to me as most relevant to my query.” So recommendation algorithms are what… Some people prefer a chronological feed. So I think that’s great, I think we should have those choices, but I actually do like the Facebook news feed and the Twitter feed, even the For You feed. The TikTok feed is of course entirely recommendation algorithms. It’s not really based largely on whom you follow, and certainly not on the chronological postings of whom you follow.

So recommendation algorithms are everywhere. We learned in this case that they’re used by Reddit and they’re used by… Wikipedia is using various algorithms in this process. Maybe not a recommendation, but doing lots of other… The dirty work behind the scenes is being done by automated algorithms. And if the way that the automated algorithm functions, sometimes accidentally promoting something wrong, maybe it’s some kind of terrible weapon or some self-harm that it might promote because it’s an automated algorithm, then if that leads to liability… boy, does that change the way that the internet works. And that’s why you saw an outpouring of briefs, as I said, from Reddit, which people think of as very much human-curated. Or Wikipedia, which again people think of as human-moderated and human-produced, to, of course, all the big tech platforms, including Microsoft, which wasn’t being sued, but still has services like GitHub and LinkedIn, which it said were at risk if this case proceeded in the way that the plaintiffs would’ve liked. So in this case, I think there was a lot at stake."

https://techpolicy.press/the-supreme-court-decides-a-final-word-on-gonzalez-v-google-and-twitter-v-taamneh-with-anupam-chander/

Today #Twitter released much of the code used for their recommendation algorithm https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

An machine-learning system relies on both an algorithm and training data so I wonder exactly what insights can be gained from what's been made public (I'm definitely not an expert in this area so I invite corrections and clarifications here). Regardless, it's an unusual level of transparency for a major social-media platform.

#SocialMedia #RecommendationEngines #RecommendationAlgorithms

#SocialMedia #Algorithms #RecommendationEngines: "I think a broader understanding of recommendation algorithms is sorely needed. Policymakers and legal scholars must understand these algorithms so that they can sharpen their thinking on platform governance; journalists must understand them so that they can explain them to readers and better hold platforms accountable; technologists must understand them so that the platforms of tomorrow may be better than the ones we have; researchers must understand them so that they can get at the intricate interplay between algorithms and human behavior. Content creators would also benefit from understanding them so that they can better navigate the new landscape of algorithmic distribution. More generally, anyone concerned about the impact of algorithmic platforms on themselves or on society may find this essay of interest.

I hope to show you that social media algorithms are simple to understand. In addition to the mathematical principles of information cascades (which are independent of any platform), it’s also straightforward to understand what recommendation algorithms are trained to do, and what inputs they use."

https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms

#Music #Streaming #Algorithms #RecommendationEngines: "In commissioning this literature review as the first stage in that research, the CDEI asked us to include in our considerations how and to what degree existing research has addressed a number of issues relevant to the concerns above, with a focus on “algorithmically-driven music recommendation systems”, in particular:

the question of “bias” in music streaming algorithms: how might different groups of artists and consumers be affected by algorithms?

the question of diversity: positive and negative impacts of algorithms on musical diversity

questions of transparency, opacity and oversight

These are therefore the main issues we seek to address here. A distinctive feature of the review is that we seek to put academic computer science research and “critical” social science and humanities research (notably a sub-field known as critical algorithm studies) into dialogue with each other, to a much greater extent than has been evident in existing scholarship."

https://www.gov.uk/government/publications/research-into-the-impact-of-streaming-services-algorithms-on-music-consumption/the-impact-of-algorithmically-driven-recommendation-systems-on-music-consumption-and-production-a-literature-review

@gvwilson the don't have to. They can ghost you whever they like by not showing your posts to anyone. The mouse you criticize the platform owner or any of their big advertisers or customers the lower your ranking in feeds. Whoever controls the algorithm, controls your (online) life.

#RecommendationEngines #Search #Ranking #Algorithms #Influence

@siderea I think what can be done locally would be a good start to reducing things like "don't show me the same boost more than once".

I'm certain that the extent of what can be done locally with reasonable cache sizes is a meaningful change to what we would see for anyone whose followings have more than a couple hundred posts + boosts in a day.

And the complexity escalates exponentially if we need to carefully manage local cache sizes to support even simple models. Probably the most that's feasible (while still complex) would be thresholds or weighting for "number of posts + boosts to see from each follow" combined with "rank by reaction velocity since posting" — which allows a client to recognize that it needs to request more from the server to fill that queue. But again, you can see how for more than a couple hundred follows, this rapidly hits gigabytes of local caching and requires list preparation ON the server.

For comparison: few would consider Netflix recommendations to be toxic — and yet the list of rows and each horizontal row of movies must be prepared and cached on Netflix servers ready for a client to request it. That's on the order of 10k titles, each with a short list of tags — much less data than Mastodon handles.

Does that give you an idea of the technical challenges?

#MastodonRanking
#AlgorithmDrivenTimelines
#ChronologicalTimelines
#MastodonDesign
#RecommendationEngines

I love that I search for #paddle hoping to find other #canoe and #kayak #paddlemaking enthusiasts and stumble on kink content. Apparently a "paddle" has different connotations to different people...

I wonder if platforms like this will ever evolve a more robust #recommendationengines that looks beyond brittle hashtags...