#datamining — Public Fediverse posts

Join me Tuesday for my next Python Data Science & AI Full Throttle! https://deitel.com/PYDSFT

O'Reilly Media Pearson #deitel #python #machinelearning #deeplearning #NLP #datamining #ApacheSpark #BigData #IoT #GenAI

@danmac
If my understanding of big data is correct and not about #AISlop #LLM hoovering, then the data centres with need are ‘boutique’ for curated datasets dealing with very narrow applications in health, academic and commercial research, medecine, and biology, etc… Those we can accommodate and ‘federate’ when needed. The data mining tools, while computer intensive, don’t need them either. So, yeah… piss off and no, we’re not goiong to miss out on anything, especially when the #bubble bursts… soonish.

#DataCentres #BigData #DataMining #PatternMatching

@danmac
If my understanding of big data is correct and not about #AISlop #LLM hoovering, then the data centres with need are ‘boutique’ for curated datasets dealing with very narrow applications in health, academic and commercial research, medecine, and biology, etc… Those we can accommodate and ‘federate’ when needed. The data mining tools, while computer intensive, don’t need them either. So, yeah… piss off and no, we’re not goiong to miss out on anything, especially when the #bubble bursts… soonish.

#DataCentres #BigData #DataMining #PatternMatching

@danmac
If my understanding of big data is correct and not about #AISlop #LLM hoovering, then the data centres with need are ‘boutique’ for curated datasets dealing with very narrow applications in health, academic and commercial research, medecine, and biology, etc… Those we can accommodate and ‘federate’ when needed. The data mining tools, while computer intensive, don’t need them either. So, yeah… piss off and no, we’re not goiong to miss out on anything, especially when the #bubble bursts… soonish.

#DataCentres #BigData #DataMining #PatternMatching

@danmac
If my understanding of big data is correct and not about #AISlop #LLM hoovering, then the data centres with need are ‘boutique’ for curated datasets dealing with very narrow applications in health, academic and commercial research, medecine, and biology, etc… Those we can accommodate and ‘federate’ when needed. The data mining tools, while computer intensive, don’t need them either. So, yeah… piss off and no, we’re not goiong to miss out on anything, especially when the #bubble bursts… soonish.

#DataCentres #BigData #DataMining #PatternMatching

@danmac
If my understanding of big data is correct and not about #AISlop #LLM hoovering, then the data centres with need are ‘boutique’ for curated datasets dealing with very narrow applications in health, academic and commercial research, medecine, and biology, etc… Those we can accommodate and ‘federate’ when needed. The data mining tools, while computer intensive, don’t need them either. So, yeah… piss off and no, we’re not goiong to miss out on anything, especially when the #bubble bursts… soonish.

#DataCentres #BigData #DataMining #PatternMatching

Palantir Is Helping Trump’s IRS Conduct “Massive-Scale” Data Mining https://theintercept.com/2026/04/24/palantir-irs-contract-data/ #uspoli #TrumpRegime #Palantir #DataMining #PrivacyRights #RegulateTech #TechRegulation #CivilRights

@deFractal #AbuseOfPower #DataMining #Capitalism #TechBros #JustPlainGreed

@tobru #Linux #DataMining #AbuseOfPower

@dalias I blocked and reported. It seems to me that mining folks' posts would be a violation of some server terms (I believe it is a violation of Kolektiva's terms). Many folks have their posts set to auto-delete, so this definitely seems like Seldo's product would be recording posts that would otherwise disappear. I also noticed that some folks from Kolektiva are following that account.

#LLMs #Fediverse #Datamining #AutoDelete #Safety #Security

@dalias I blocked and reported. It seems to me that mining folks' posts would be a violation of some server terms (I believe it is a violation of Kolektiva's terms). Many folks have their posts set to auto-delete, so this definitely seems like Seldo's product would be recording posts that would otherwise disappear. I also noticed that some folks from Kolektiva are following that account.

#LLMs #Fediverse #Datamining #AutoDelete #Safety #Security

@dalias I blocked and reported. It seems to me that mining folks' posts would be a violation of some server terms (I believe it is a violation of Kolektiva's terms). Many folks have their posts set to auto-delete, so this definitely seems like Seldo's product would be recording posts that would otherwise disappear. I also noticed that some folks from Kolektiva are following that account.

#LLMs #Fediverse #Datamining #AutoDelete #Safety #Security

@dalias I blocked and reported. It seems to me that mining folks' posts would be a violation of some server terms (I believe it is a violation of Kolektiva's terms). Many folks have their posts set to auto-delete, so this definitely seems like Seldo's product would be recording posts that would otherwise disappear. I also noticed that some folks from Kolektiva are following that account.

#LLMs #Fediverse #Datamining #AutoDelete #Safety #Security

@dalias I blocked and reported. It seems to me that mining folks' posts would be a violation of some server terms (I believe it is a violation of Kolektiva's terms). Many folks have their posts set to auto-delete, so this definitely seems like Seldo's product would be recording posts that would otherwise disappear. I also noticed that some folks from Kolektiva are following that account.

#LLMs #Fediverse #Datamining #AutoDelete #Safety #Security

📣Der Märzvortrag unseres #DHELab nächste Woche am

📅 Fr, 27.3.2026 | 12-13 Uhr | Online

Mit Max Zeterberg (@subugoe) & Lasse Clausen (@unigoettingen) widmet sich der #DigitaleEdition des #Pädagogen Klaus Mollenhauer, ihrer Entstehung und den Nutzungsmöglichkeiten:
➡️ https://bbf.dipf.de/de/aktuell/termine/dhelab-vortrag-2026-03

#histed #textmining #datamining #DigitalHistory #DH #OpenData #FDM #history #histodons #OpenAccess @dipf_aktuell

https://www.europesays.com/ch-fr/31143/ Ken Griffey Jr.’s Winning Run : des codes de triche SNES cachés ont été découverts après 30 ans, permettant aux joueurs rétro de débloquer 4 équipes secrètes #baseball #caché #CheatCodes #DataMining #équipesD'expansion #équipesSecrètes #InformationsSurDesOrdinateursPortatifs #KenGriffeyJr #N64 #Nintendo #nouvelles #rapport #Rare #RétroGaming #revues #Science #ScienceAndTechnology #Sciences #SciencesEtTechnologies #SNES #Suisse #Technologies #Technology #test

#AIBots may lead to the end of the internet as we know it

In recent weeks, #OpenDemocracy’s website has been repeatedly brought down by an army of bots. We’re not the only ones

Matthew Linares
20 February 2026

Excerpt: "Slater explained that 'the traffic often arrives through anonymous residential IPs', referring to residential proxy networks that route internet traffic through intermediary servers using IP addresses assigned by internet service providers to real homeowners. This, he said, makes it 'hard to distinguish ‘normal users’ from automated collection'. [That's not right and needs to be changed!!!]

" 'We're being forced into permanent defence mode. #ResidentialProxyNetworks let #AIScrapers hide in plain sight, rotate identities, and extract data at scale. That shifts real costs onto projects that exist to serve people, not feed training pipelines."

Read more:
https://www.opendemocracy.net/en/ai-chatbots-scraper-bots-chatgpt-website-offline-change-internet/

#AISucks #AI #DataMining #Internet #Websites #TechNews #AI #ArtificialIntelligence #BigTech #TechBros

@dipf_aktuell

📣Der Märzvortrag unseres #DHELab am
📅 27.3.2026 | 12-13 Uhr | Online
mit Max Zeterberg (@subugoe) & Lasse Clausen (@unigoettingen) widmet sich der #DigitaleEdition des #Pädagogen Klaus Mollenhauer, ihrer Entstehung und den Nutzungsmöglichkeiten:
➡️ bbf.dipf.de/de/aktuell/t...

#histed #textmining #datamining #DigitalHistory #DH #history #histodons @dipf_aktuell

Pluralistic: Doctors' union may yet save the NHS from Palantir (12 Feb 2026)

https://web.brid.gy/r/https://pluralistic.net/2026/02/12/palantir-is-ice/

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.

In order to watch the Super Bowl, as well as get access to the Jurassic World movies to satisfy my kiddo's current obsession with all things dinosaur, I did a month's subscription to Peacock. In installing the Peacock app onto my Android phone running a "degoogled" crDrojd rom, TrackerControl detected a hilarious level of 10 embedded known trackers. Without the protection of a blocker like TrackerControl, this supposed streaming service is also a data mining extravaganza, sending tons of info back to all kinds of 3rd parties.

Hört mein Handy mit?
Mehr dazu bei https://www.wienerzeitung.at/a/hoert-mein-handy-wirklich-mit
a-fsa.de/d/3Mm
Link zu dieser Seite: https://www.a-fsa.de/de/articles/9400-20260107-hoert-mein-handy-mit.html
Link im Tor-Netzwerk: http://a6pdp5vmmw4zm5tifrc3qo2pyz7mvnk4zzimpesnckvzinubzmioddad.onion/de/articles/9400-20260107-hoert-mein-handy-mit.html
Tags: #Mikrofon #Kamera #Smartphone #Handy #Palantir #Siri #Alexa #Lauschangriff #Überwachung #Videoüberwachung #Rasterfahndung #DataMining #Unschuldsvermutung #Verhaltensänderung #Polizei #Geheimdienste #Hacking #Verbraucherdatenschutz #Spam #Pishing #Noscript #uBlock

✨Do you already know Scimago Graphica?

I consistently incorporate this tool into my workshops, and I can tell you it’s a powerful platform that allows you to create all kinds of visualizations. It’s very easy to use, freely accessible, and quite intuitive.

I invite you to read my article on Medium about this tool
🔗 https://medium.com/@vespinozag/meet-scimago-graphica-a-new-way-to-explore-visually-communicate-and-make-sense-of-data-861fb7226dbc

👇Here I’m sharing a set of visualizations I created with this tool

#NetworkScience #DataMining #Datascience

Why “public AI”, built on open source software, is the way forward for the EU

A quarter of a century ago, I wrote a book called “Rebel Code”. It was the first – and is still the only – detailed history of the origins and rise of free software and open source, based on interviews with the gifted and generous hackers who took part. Back then, it was clear that open source represented a powerful alternative to the traditional proprietary approach to software […]

#ai #aiAct #cdsm #china #cloudComputing #copyrightDirective #dataMining #eu #freeSoftware #openSource #openSourceInitiative #paulKeller #publicAi #rebelCode #research #startups #supercomputers #tdm #textMining #us #ventureCapital

https://walledculture.org/why-public-ai-built-on-open-source-software-is-the-way-forward-for-the-eu/

美國通貨膨脹率(CPI年增率)從1948年1月到2025年7月的數據建模。

左上：直方圖

這是一般對數據分布的表現方式，但這樣的表現方式並沒有告訴我們任何有關機率模型的數學式。

右上：以分11組的美國通貨膨脹率組中點和對應的相對次數，進行AI數據分析的AI-based piecewise linear regression建模，得到兩條數學式。整體的準確率達0.83%。

左下：分更多組的美國通貨膨脹率組中點和對應的相對次數。精準建模後，產生31條直線方程式。整體的準確率達0.94%。

右下：運用改良式適合度檢定，為美國通貨膨脹率從45種機率分布中測定最小卡方值的分布。這個Gumbel結果並無P值小於0.05。再根據Gumbel分布的第一個參數為條件，模擬生成美國通貨膨脹率的分布情況。

@academicchatter @econometrics @ida

#AI #economywatch #economy #inflation #CPI #USA #datamining #datascience #bigdata #MathAI #math #mathematics

Palantir verbieten!
Keine US Überwachungssoftware in Deutschland
Mehr dazu bei https://www.a-fsa.de/events/3912-20250903.html
und https://www.swr.de/swraktuell/radio/proteste-gegen-palantir-riesige-blackbox-100.html
a-fsa.de/d/3K6
Link zu dieser Seite: https://www.a-fsa.de/de/articles/9270-20250904-palantir-verbieten.html
Link im Tor-Netzwerk: http://a6pdp5vmmw4zm5tifrc3qo2pyz7mvnk4zzimpesnckvzinubzmioddad.onion/de/articles/9270-20250904-palantir-verbieten.html
Tags: #Palantir #Digitalisierung #Überwachungssoftware #Privatheit #Arbeitnehmerdatenschutz #Verbraucherdatenschutz #Rasterfahndung #DataMining #Lauschangriff #Überwachung #CloudAct

Sätze, die ich echt nicht mehr hören kann:

"Von mir aus können die alles mitlesen, ich habe nichts zu verbergen. "

"Mein Leben ist eh langweilig, ich bin entweder arbeiten oder zu Hause."

Herr, wann wolltest Du jetzt eigentlich endlich mal dieses Hirn regnen lassen, von dem immer alle reden? 🤷‍♀️

#Datensouveränität #Datenschutz #Datenklau #Meta #Überwachung #KI #InformationelleSelbstbestimmung #Datamining

Want to use #ai for your business? Need expert insights at a self-help price? Meet the experienced consultant for the DIY budget.
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https://datatofu.wordpress.com/#DIY
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Tags: #python #dataanalyst #data #datamining #datascience #datainfrastructure #r #reels #repost #cloudcomputing #statistics #status #linux #entreprenuer #bigdata #bigdataanalytics

La DGFiP paye Cash son opacité sur l'intelligence artificielle ! https://solidairesfinancespubliques.org/le-syndicat/dossiers/ia-a-la-dgfip/6761-la-dgfip-paye-cash-son-opacite-sur-lintelligence-artificielle.html #Intelligenceartificielle(IA) #dgfip
#intelligencearticielle
#datamining
#servicepublic
#bercy

@data @datadon 🧵

In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

"The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

https://dpoblog.eu/cjeu-and-the-powers-of-edpb by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

#advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

@data @datadon 🧵

In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

"The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

https://dpoblog.eu/cjeu-and-the-powers-of-edpb by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

#advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

@[email protected] @[email protected] 🧵

In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

"The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

https://dpoblog.eu/cjeu-and-the-powers-of-edpb by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

#advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

@data @datadon 🧵

In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

"The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

https://dpoblog.eu/cjeu-and-the-powers-of-edpb by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

#advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

@data @datadon 🧵

In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

"The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

https://dpoblog.eu/cjeu-and-the-powers-of-edpb by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

#advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

Tired of wasting hours on data entry? ⏳ Let (Un)Perplexed Spready automate those tasks with AI integration! Spend less time crunching numbers and more time making decisions. Discover how: https://matasoft.hr/qtrendcontrol/index.php/un-perplexed-spready

#AI #Spreadsheets #DataExtraction #DataLabeling #DataAnotation #DataCategorization #DataClassification #SmartData #AItools #ProductComparison #SmartSpreadsheets #DataStandardization #ProductivityTools #AIProductivity #LLM #BI #MDM #BusinessIntelligence #DataMining #ProductivityHacks

Join the spreadsheet revolution! 🥳 (Un)Perplexed Spready is changing the way people work with data.  Claim your free evaluation license and experience the power of AI in your formulas. 🚀  Download now: https://matasoft.hr/qtrendcontrol/index.php/un-perplexed-spready

#AI #Spreadsheets #DataExtraction #DataLabeling #DataAnotation #DataCategorization #DataClassification #SmartData #AItools #ProductComparison #SmartSpreadsheets #DataStandardization #ProductivityTools #SpreadsheetRevolution #AIProductivity #LLM #BI #MDM #DataMining

We're #hiring!

Two(!) full #professorships open in our department at WU Vienna (Vienna University of Economics and Business) under two complementary focus topics:

1) #Foundations of contemporary #InformationSystems, where we look for candidates who complement and strengthen the existing research at our department in areas such as:

· #ArtificialIntelligence: #AI Systems and Architectures
· #DataMining and #MachineLearning
· #DistributedSystems and #Decentralization
· #DistributedLedgers
· #Cloud and #Virtualisation
· #IoT and #EdgeComputing
· #DataGovernance for AI

2) #OperationsManagement with a focus on #DigitalTransformation, where the candidate’s expertise falls within one of the following research areas:

· #behavioural #operations
· AI application to #process improvements
· integrated #supplymanagement and #demandmanagement
· #ProductionPlanning and control
· #SupplyChain planning and control
· circular supply chains and sustainable supply chain management
· #tokenization in supply chains and new product development

Details at the link below... Please get in touch, if you want to know more!

https://www.wu.ac.at/en/isom/events/isom-news/detail/open-positions-2-full-professorships-of-business-administration-and-informaton-systems

Georgia State University: Data-Mining Project Uncovers Enslaved People’s Histories. “[Professor Elizabeth J.] West, her former graduate research assistant Joshua Jackson (Ph.D. ’22) and former student John Washington (B.A. ’22) were central to creating the Data Mining and Mapping Antebellum Georgia (DMMAG) project. The project aims to build a comprehensive, searchable database of named […]

https://rbfirehose.com/2025/03/23/georgia-state-university-data-mining-project-uncovers-enslaved-peoples-histories/

Our colleague Hidir Arras from patent4science research is co-organizing the 6th PatentSemTech Workshop at #SIGIR2025 in the beautiful city of Padua, Italy! Call for Papers is open 'til April 23: http://ifs.tuwien.ac.at/patentsemtech/

Submit your cutting-edge research, case studies, and demos exploring #AI, #NLP, and #TextMining innovations applied to #IP and related domains.

@fiz_karlsruhe #informationextraction #datamining #ir

Enteric Fermentation in 2022

Livestock digestion emits too much methane:
* Too many bovines in India, Pakistan, Brazil, United States, China;
* Too many sheep and pigs in China.

(The bubble sizes depend on the amount of methane sent in 2022.)

#GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

Enteric Fermentation in 2022

Livestock digestion emits too much methane:
* Too many bovines in India, Pakistan, Brazil, United States, China;
* Too many sheep and pigs in China.

(The bubble sizes depend on the amount of methane sent in 2022.)

#GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

Enteric Fermentation in 2022

Livestock digestion emits too much methane:
* Too many bovines in India, Pakistan, Brazil, United States, China;
* Too many sheep and pigs in China.

(The bubble sizes depend on the amount of methane sent in 2022.)

#GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

Enteric Fermentation in 2022

Livestock digestion emits too much methane:
* Too many bovines in India, Pakistan, Brazil, United States, China;
* Too many sheep and pigs in China.

(The bubble sizes depend on the amount of methane sent in 2022.)

#GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

Enteric Fermentation in 2022

Livestock digestion emits too much methane:
* Too many bovines in India, Pakistan, Brazil, United States, China;
* Too many sheep and pigs in China.

(The bubble sizes depend on the amount of methane sent in 2022.)

#GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

@data @datadon

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

Back in 2011, two writers at Slate tried to build a robot version of @kottke. The resulting article is a throwback to the state of NLP and data mining at the time.

https://kottke.org/11/09/robottke-robot-kottke

#kottke #blogging #nlp #nlg #datamining #textgeneration #automation

Your regular reminder not to use M$ Windows

https://digitalmarketreports.com/news/25091/microsoft-recall-feature-on-windows-11-not-removable-after-all/

#Windoze #Windows #Recall #Privacy #GDPR #DataMining