In January, Kendra Calhoun, a postdoctoral researcher in linguistic anthropology at UCLA, and Alexia Fawcett, a doctoral student in linguistics at UC Santa Barbara, gave a presentation about language on TikTok. They outlined how, by self-censoring words in the captions of TikToks, new algospeak code words emerged.

“It makes me feel like I need a disclaimer because I feel like it makes you seem unprofessional to have these weirdly spelled words in your captions,” she said, “especially for content that's supposed to be serious and medically inclined.”

But algorithmic content moderation systems are more pervasive on the modern Internet, and often end up silencing marginalized communities and important discussions.

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.

Man kann die ganze Situation nämlich auch einmal zum Anlass nehmen, darüber nachzudenken, ob man das Ganze wirklich braucht. Ist der Nutzen der sozialen Medien so hoch, dass er den Preis rechtfertigt? Das ist eine Frage, die ich mir stelle, seit ich meinen persönlichen Twitter-Account stillgelegt habe, aber so verkehrt fühlt es sich zumindest für mich nicht an, nicht mehr auf Twitter, Mastodon & Co. vertreten zu sein. Vielleicht hatte ein solcher Dienst auch einfach seine Zeit, und vielleicht überschätzen wir die Relevanz von sozialen Medien, und vielleicht wäre es gut, davon mehr Abstand zu nehmen.

One can find utility in asking questions of their own note box, but why not also leverage the utility of a broader audience asking questions of it as well?!

“She is likely our earliest Black female ethnographic filmmaker,” says Strain, who also teaches documentary history at Wesleyan University.

Ryan Randall @ryanrandall@hcommons.socialEarnest but still solidifying #pkm take:The ever-rising popularity of personal knowledge management tools indexes the need for liberal arts approaches. Particularly, but not exclusively, in STEM education.When people widely reinvent the concept/practice of commonplace books without building on centuries of prior knowledge (currently institutionalized in fields like library & information studies, English, rhetoric & composition, or media & communication studies), that's not "innovation."Instead, we're seeing some unfortunate combination of lost knowledge, missed opportunities, and capitalism selectively forgetting in order to manufacture a market.

social media platform

The SWW aimed to mimic the average conditions and nutrient proportions of conventional domestic wastewater.

is zettelkasten gamification of note-taking? .t3_zkguan._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; }

Books and Presentations Are Playlists, so let's create a NeoBook this way.

nalyze the content of 69,907 headlines pro-duced by four major global media corporations duringa minimum of eight consecutive months in 2014. In or-der to discover strategies that could be used to attractclicks, we extracted features from the text of the newsheadlines related to the sentiment polarity of the head-line. We discovered that the sentiment of the headline isstrongly related to the popularity of the news and alsowith the dynamics of the posted comments on that par-ticular news

"Queer people built the Fediverse," she said, adding that four of the five authors of the ActivityPub standard identify as queer. As a result, protections against undesired interaction are built into ActivityPub and the various front ends. Systems for blocking entire instances with a culture of trolling can save users the exhausting process of blocking one troll at a time. If a post includes a “summary” field, Mastodon uses that summary as a content warning.

Investigating social structures through the use of network or graphs Networked structures Usually called nodes ((individual actors, people, or things within the network) Connections between nodes: Edges or Links Focus on relationships between actors in addition to the attributes of actors Extensively used in mapping out social networks (Twitter, Facebook) Examples: Palantir, Analyst Notebook, MISP and Maltego

Drawing from negativity bias theory, CFM, ICM, and arousal theory, this study characterizes the emotional responses of social media users and verifies how emotional factors affect the number of reposts of social media content after two natural disasters (predictable and unpredictable disasters). In addition, results from defining the influential users as those with many followers and high activity users and then characterizing how they affect the number of reposts after natural disasters

Using actual fake-news headlines presented as they were seen on Facebook, we show that even a single exposure increases subsequent perceptions of accuracy, both within the same session and after a week. Moreover, this “illusory truth effect” for fake-news headlines occurs despite a low level of overall believability and even when the stories are labeled as contested by fact checkers or are inconsistent with the reader’s political ideology. These results suggest that social media platforms help to incubate belief in blatantly false news stories and that tagging such stories as disputed is not an effective solution to this problem.

. Furthermore, our results add to the growing body of literature documenting—at least at this historical moment—the link between extreme right-wing ideology and misinformation8,14,24 (although, of course, factors other than ideology are also associated with misinformation sharing, such as polarization25 and inattention17,37).

. We also find evidence of “falsehood echo chambers”, where users that are more often exposed to misinformation are more likely to follow a similar set of accounts and share from a similar set of domains. These results are interesting in the context of evidence that political echo chambers are not prevalent, as typically imagined

And finally, at the individual level, we found that estimated ideological extremity was more strongly associated with following elites who made more false or inaccurate statements among users estimated to be conservatives compared to users estimated to be liberals. These results on political asymmetries are aligned with prior work on news-based misinformation sharing

Interestingly, the fraction of low-credible URLs coming from U.S. dropped from 74% in the vax devel-opment period to 55% in the vax rollout. This large decrease can be directly ascribed to Twitter’s moderationpolicy: 46% of cross-border retweets of U.S. users linking to low-credible websites in the vax developmentperiod came from accounts that have been suspended following the U.S. Capitol attack (see Figure 8 (a), Ap-pendix).

Considering the behavior of users in no-vax communities,we find that they are more likely to retweet (Figure 3(a)), share URLs (Figure 3(b)), and especially URLs toYouTube (Figure 3(c)) than other users. Furthermore, the URLs they post are much more likely to be fromlow-credible domains (Figure 3(d)), compared to those posted in the rest of the networks. The differenceis remarkable: 26.0% of domains shared in no-vax communities come from lists of known low-credibledomains, versus only 2.4% of those cited by other users (p < 0.001). The most common low-crediblewebsites among the no-vax communities are zerohedge.com, lifesitenews.com, dailymail.co.uk (consideredright-biased and questionably sourced) and childrenshealthdefense.com (conspiracy/pseudoscience)

Therefore, although the social bot individual is “small”, it has become a “super spreader” with strategic significance. As an intelligent communication subject in the social platform, it conspired with the discourse framework in the mainstream media to form a hybrid strategy of public opinion manipulation.

There were 120,118 epidemy-related tweets in this study, and 34,935 Twitter accounts were detected as bot accounts by Botometer, accounting for 29%. In all, 82,688 Twitter accounts were human, accounting for 69%; 2495 accounts had no bot score detected.In social network analysis, degree centrality is an index to judge the importance of nodes in the network. The nodes in the social network graph represent users, and the edges between nodes represent the connections between users. Based on the network structure graph, we may determine which members of a group are more influential than others. In 1979, American professor Linton C. Freeman published an article titled “Centrality in social networks conceptual clarification“, on Social Networks, formally proposing the concept of degree centrality [69]. Degree centrality denotes the number of times a central node is retweeted by other nodes (or other indicators, only retweeted are involved in this study). Specifically, the higher the degree centrality is, the more influence a node has in its network. The measure of degree centrality includes in-degree and out-degree. Betweenness centrality is an index that describes the importance of a node by the number of shortest paths through it. Nodes with high betweenness centrality are in the “structural hole” position in the network [69]. This kind of account connects the group network lacking communication and can expand the dialogue space of different people. American sociologist Ronald S. Bert put forward the theory of a “structural hole” and said that if there is no direct connection between the other actors connected by an actor in the network, then the actor occupies the “structural hole” position and can obtain social capital through “intermediary opportunities”, thus having more advantages.

We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.

I want to insist on an amateur internet; a garage internet; a public library internet; a kitchen table internet.

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

Okay, so flashback to the 1920s and the emergence of something called the public interest mandate, basically when radio was new, a ton of people wanted to broadcast the demand for space on the dial outstripped supply. So to narrow the field, the federal government says that any station using the public airwaves needs to serve the public interest. So what do they mean by the public interest? Yeah, right? It's like super vague, right? But the FCC clarified what it meant by public interest in the years following World War Two, They had seen how radio could be used to promote fascism in Europe, and they didn't want us radio stations to become propaganda outlets. And so in 1949, the FCC basically says to stations in order to serve the public, you need to give airtime to coverage of current events and you have to include multiple perspectives in your coverage. This is the basis of what comes to be known as the fairness doctrine.

my best guess is it’s the moderation

I'd love it to be normal and everyday to not assume that when you post a message on your social network, every person is reading it in a similar UI, either to the one you posted from, or to the one everyone else is reading it in.

Musk appears to be betting that the spectacle is worth it. He’s probably correct in thinking that large swaths of the world will not deem his leadership a failure either because they are ideologically aligned with him or they simply don’t care and aren’t seeing any changes to their corner of the Twitterverse.

A lot has changed about our news media ecosystem since 2007. In the United States, it’s hard to overstate how the media is entangled with contemporary partisan politics and ideology. This means that information tends not to flow across partisan divides in coherent ways that enable debate.

I often think back to MySpace’s downfall. In 2007, I penned a controversial blog post noting a division that was forming as teenagers self-segregated based on race and class in the US, splitting themselves between Facebook and MySpace. A few years later, I noted the role of the news media in this division, highlighting how media coverage about MySpace as scary, dangerous, and full of pedophiles (regardless of empirical evidence) helped make this division possible. The news media played a role in delegitimizing MySpace (aided and abetted by a team at Facebook, which was directly benefiting from this delegitimization work).

Humans didn’t evolve to thrive in frictionless social networks with high fanout and velocity, and arguably we shouldn’t.

The trust one must place in the creator of a blocklist is enormous, because the most dangerous failure mode isn’t that it doesn’t block who it says it does, but that it blocks who it says it doesn’t and they just disappear.

“The damage commercial social media has done to politics, relationships and the fabric of society needs undoing.

As users begin migrating to the noncommercial fediverse, they need to reconsider their expectations for social media — and bring them in line with what we expect from other arenas of social life. We need to learn how to become more like engaged democratic citizens in the life of our networks.

I have about fourteen or sixteen weeks to do this, so I'm breaking the course into an "intro" section that covers some basic stuff like affordances, and other insights into how tech functions. There's a section on AI which is nothing but critical appraisals on AI from a variety of areas. And there's a section on Social Media, which is the most well formed section in terms of readings.

The TTRG (time to reply guy) was getting so fast, that I can’t actually remember the last time I tweeted something helpful like a design or development tip. I just couldn’t be arsed, knowing some dickhead would be around to waste my time with whataboutisms and “will it scale”?

The Post analyzed data from ProPublica’s Represent tool, which tracks congressional Twitter activity.

11/30 Youth Collaborative

If more Americans were like TV Tropes’ users—that is, if they could spot the recurring motifs in purported political plots—might they also be better at separating fact from fiction?

As part of the Election Integrity Partnership, my team at the Stanford Internet Observatory studies online rumors, and how they spread across the internet in real time.

In late 2006, Eno released 77 Million Paintings, a program of generative video and music specifically for home computers. As its title suggests, there is a possible combination of 77 million paintings where the viewer will see different combinations of video slides prepared by Eno each time the program is launched. Likewise, the accompanying music is generated by the program so that it's almost certain the listener will never hear the same arrangement twice.

The notable exception: social media companies. Gen Zers are more likely to trust social media companies to handle their data properly than older consumers, including millennials, are.

Looking forward to many social media alternatives: Blue Sky, Matrix, and many others. 

Any migration is likely to face many of the challenges previous platform migrations have faced: content loss, fragmented communities, broken social networks and shifted community norms.

By asking participants about their experiences moving across these platforms – why they left, why they joined and the challenges they faced in doing so – we gained insights into factors that might drive the success and failure of platforms, as well as what negative consequences are likely to occur for a community when it relocates.

"This is a job market that just won't quit. It's challenging the rules of economics," said Becky Frankiewicz,  chief commercial officer of hiring company ManpowerGroup in an email after the data was released. "The economic indicators are signaling caution, yet American employers are signaling confidence."

That could create even more burdens for businesses because hiking interest rates tends to create higher rates on consumer and business loans, which slows the economy by forcing employers to cut back on spending.

DHS’s mission to fight disinformation, stemming from concerns around Russian influence in the 2016 presidential election, began taking shape during the 2020 election and over efforts to shape discussions around vaccine policy during the coronavirus pandemic. Documents collected by The Intercept from a variety of sources, including current officials and publicly available reports, reveal the evolution of more active measures by DHS. According to a draft copy of DHS’s Quadrennial Homeland Security Review, DHS’s capstone report outlining the department’s strategy and priorities in the coming years, the department plans to target “inaccurate information” on a wide range of topics, including “the origins of the COVID-19 pandemic and the efficacy of COVID-19 vaccines, racial justice, U.S. withdrawal from Afghanistan, and the nature of U.S. support to Ukraine.”

A recent writer has called attention to apassage in Paxson's presidential address before the American Historical Associationin 1938, in which he remarked that historians "needed Cheyney's warning . . . not towrite in 1917 or 1918 what might be regretted in 1927 and 1928."

Mastodon

By teaching them all to read, we have left them atthe mercy of the printed word.

Edgerly noted that disinformation spreads through two ways: The use of technology and human nature.Click-based advertising, news aggregation, the process of viral spreading and the ease of creating and altering websites are factors considered under technology.“Facebook and Google prioritize giving people what they ‘want’ to see; advertising revenue (are) based on clicks, not quality,” Edgerly said.She noted that people have the tendency to share news and website links without even reading its content, only its headline. According to her, this perpetuates a phenomenon of viral spreading or easy sharing.There is also the case of human nature involved, where people are “most likely to believe” information that supports their identities and viewpoints, Edgerly cited.“Vivid, emotional information grabs attention (and) leads to more responses (such as) likes, comments, shares. Negative information grabs more attention than (the) positive and is better remembered,” she said.Edgerly added that people tend to believe in information that they see on a regular basis and those shared by their immediate families and friends.

Direkte Kanäle > Social Media: Bei Instagram, Facebook und TikTok entscheiden Algorithmen, welche deiner Inhalte für User sichtbar werden. Dem entgegen stehen direkte Distributionswege: Eine neue Podcast-Episode landet unmittelbar im Podcatcher, der Newsletter im Posteingang, die SMS oder Push Notification auf dem Smartphone. Klarer Win! Trotzdem bleiben die Socials wichtig, um neue Zielgruppen auf sich aufmerksam zu machen und erste Bande zu knüpfen.

Trolls, in this context, are humans who hold accounts on social media platforms, more or less for one purpose: To generate comments that argue with people, insult and name-call other users and public figures, try to undermine the credibility of ideas they don’t like, and to intimidate individuals who post those ideas. And they support and advocate for fake news stories that they’re ideologically aligned with. They’re often pretty nasty in their comments. And that gets other, normal users, to be nasty, too.

So, bots are computer algorithms (set of logic steps to complete a specific task) that work in online social network sites to execute tasks autonomously and repetitively. They simulate the behavior of human beings in a social network, interacting with other users, and sharing information and messages [1]–[3]. Because of the algorithms behind bots’ logic, bots can learn from reaction patterns how to respond to certain situations. That is, they possess artificial intelligence (AI). 

Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed, a technique known as creating “follow trains.”

We observed an overall increase in the amount of negative information as it passed along the chain—known as the social amplification of risk.

We confuse popularity with quality and end up copying the behavior we observe.

“Limited individual attention and online virality of low-quality information,” By Xiaoyan Qiu et al., in Nature Human Behaviour, Vol. 1, June 2017

Running this simulation over many time steps, Lilian Weng of OSoMe found that as agents' attention became increasingly limited, the propagation of memes came to reflect the power-law distribution of actual social media: the probability that a meme would be shared a given number of times was roughly an inverse power of that number. For example, the likelihood of a meme being shared three times was approximately nine times less than that of its being shared once.

One of the first consequences of the so-called attention economy is the loss of high-quality information.

After looking at various studies fromthe 1960s until the early 1980s, Barry S. Stein et al. summarises:“The results of several recent studies support the hypothesis that