Twitter is poison to American political discourse. Can't we find a more worthy pastime?
Tag: Twitter
👓 Saturday, November 17, 2018 | Scripting News
So what does a Like mean here on Scripting News? It's a way to tell me that you saw what I wrote and found it likeable. It doesn't mean you necessarily agree. You're also registering your presence to other people who read this blog. Maybe it's more like a ping? Hmmm. I know the Like icon doesn't show up in your feed reader (maybe that can change) but it may be worth a trip to my blog if you want to say hi to me and others who read this blog. That's what it means. #
The UI isn’t completely transparent. Am I liking something that was syndicated to Twitter from Dave’s site and also thereby indicating a like for something that exists on Twitter? Or is it just using my Twitter identity and username and saving it on that particular permalink without creating a like on my actual Twitter account that’s related to something in Dave’s account? Based on some Twitter searches, I’m guessing it’s the latter.
This is also somewhat reminiscent of my experiment last year: Adding Simple Twitter Response Buttons to WordPress Posts, though my version allowed people to retweet and reply and kept copies of the data on both my site as well as on Twitter.
👓 Kevin Hart Steps Down as Oscar Host | Variety
Just 48 hours after agreeing to host the 91st Academy Awards, Kevin Hart unceremoniously stepped down late Thursday night on social media. The turn of events followed outcry over previous anti-gay tweets, and comments Hart made during stand-up routines nearly 10 years ago. Some of the tweets were feverishly deleted throughout the day on Thursday, leading to an Instagram video from the comedian that only made matters worse for him.
Reply to Vika on permashort citations
https://indieweb.org/permashortcitation
👓 Can You Conquer the Toughest Disney Challenge? Parkeology Challenge Official Rules | Parkeology
Official Rules for the toughest Disney challenge around -- the Parkeology Challenge. Can you ride all Disney World rides in one day? (formerly known as WDW46 / WDW47 / WDW49).
👓 The Parkeology Challenge | Parkeology
The Parkeology Challenge is to ride all Disney World or Disneyland rides in one day across all the theme parks. Can you do it? Sign up today!
👓 ‘HAPPY THANKSGIVING TO ALL!’: Rhetorical bedlam erupts as President Trump speaks to the world from Mar-a-Lago | Washington Post
PALM BEACH, Fla. — President Trump’s Thanksgiving began, as his days often do, with an all-caps tweet: “HAPPY THANKSGIVING TO ALL!” Minutes later, he tweeted of potential “bedlam, chaos, injury and death,” a harbinger of what would be a frenetic Thanksgiving morning.
Highlights, Quotes, Annotations, & Marginalia from Linked: The New Science Of Network by Albert-László Barabási
Highlights, Quotes, Annotations, & Marginalia
Guide to highlight colors
Yellow–general highlights and highlights which don’t fit under another category below
Orange–Vocabulary word; interesting and/or rare word
Green–Reference to read
Blue–Interesting Quote
Gray–Typography Problem
Red–Example to work through
…the high barriers to becoming a Christian had to be abolished. Circumcision and the strict food laws had to be relaxed.
make it easier to create links!
But when you add enough links such that each node has an average of one link, a miracle happens: A unique giant cluster emerges.
Random network theory tells us that as the average number of links per node increases beyond the critical one, the number of nodes left out of the giant cluster decreases exponentially.
If the network is large, despite the links’ completely random placement, almost all nodes will have approximately the same number of links.
seminal 1959 paper of Erdős and Rényi to bookmark
“On Random Graphs. I” (PDF). Publicationes Mathematicae. 6: 290–297.
In Igy irtok ti, or This is How You Write, Frigyes Karinthy
But there is one story, entitled “Lancszemek,” or “Chains,” that deserves our attention
Karinthy’s 1929 insight that people are linked by at most five links was the first published appearance of the concept we know today as “six degrees of separation.”
He [Stanley Milgram] did not seem to have been aware of the body of work on networks in graph theory and most likely had never heard of Erdős and Rényi. He is known to have been influenced by the work of Ithel de Sole Pool of MIT and Manfred Kochen of IBM, who circulated manuscripts about the small world problem within a group of colleagues for decades without publishing them, because they felt they had never “broken the back of the problem.”
Think about the small world problem of published research.
We don’t have a social search engine so we may never know the real number with total certainty.
Facebook has fixed this in the erstwhile. As of 2016 it’s down to 3.57 degrees of separation
social network
google the n-gram of this word to see it’s incidence over time. How frequent was it when this book was written? It was apparently a thing beginning in the mid 1960’s.
Mark Newman, a physicist at the Santa Fe Institute… had already written several papers on small worlds that are now considered classics.
Therefore, Watts and Strogatz’s most important discovery is that clustering does not stop at the boundary of social networks.
To explain the ubiquity of clustering in most real networks, Watts and Strogatz offered an alternative to Erdős and Rényi’s random network model in their 1998 study published in Nature.
Watts, D. J.; Strogatz, S. H. (1998). “Collective dynamics of ‘small-world’ networks” (PDF). Nature. 393 (6684): 440–442. Bibcode:1998Natur.393..440W. doi:10.1038/30918. PMID 9623998
The most intriguing result of our Web-mapping project was the complete absence of democracy, fairness, and egalitarian values on the Web. We learned that the topology of the Web prevents us from seeing anything but a mere handful of the billion documents out there.
Do Facebook and Twitter subvert some of this effect? What types of possible solutions could this give to the IndieWeb for social networking models with healthier results?
On the Web, the measure of visibility is the number of links. The more incoming links pointing to your Webpage, the more visible it is. […] Therefore, the liklihood that a typical document links to your Webpage is close to zero.
The hubs are the strongest argument against the utopian vision of an egalitarian cyberspace. […] In a collective manner, we somehow create hubs, Websites to which everyone links. They are very easy to find, no matter where you are on the Web. Compared to these hubs, the rest of the Web is invisible.
Every four years the United States inaugurates a new social hub–the president.
But every time an 80/20 rule truly applies, you can bet that there is a power law behind it. […] Power laws rarely emerge in systems completely dominated bya roll of the dice. Physicists have learned that most often they signal a transition from disorder to order.
If the disorder to order is the case, then what is the order imposed by earthquakes which apparently work on a power law distribution?
Leo Kadanoff, a physicist at the University of Illinois at Urbana, had a sudden insight: In the vicinity of the critical point we need to stop viewing atoms separately. Rather, they should be considered communities that act in unison. Atoms must be replaced by boxes of atoms such that within each box all atoms behave as one.
#phase transitions
Kenneth Wilson […] submitted simultaneously on June 2, 1971, and published in November of the same year by Physical Review B, turned statistical physics around. The proposed an elegant and all-encompassing theory of phase transitions. Wilson took the scaling ideas developed by Kadanoff and molded them into a powerful theory called renormalization. The starting point of his approach was scale invariance: He assumed that in the vicinity of the critical point the laws of physics applied in an identical manner at all scales, from single atoms to boxes containing millions of identical atoms acting in unison. By giving rigorous mathematical foundation to scale invariance, his theory spat out power laws each time he approached the critical point, the place where disorder makes room for order.
The random model of Erdős and Rényi rests on two simple and often disregarded assumptions. First, we start with an inventory of nodes. Having all the nodes available from the beginning, we assume that the number of nodes is fixed and remains unchanged throughout the network’s life. Second, all nodes are equivalent. Unable to distinguish between the nodes, we link them randomly to each other. These assumptions were unquestioned in over forty years of network research.
Both in the Erdős-Rényi and Watts-Strogatz models assumed that we have a fixed number of nodes that are wired together in some clever way. The networks generated by these models are therefore static, meaning that the number of nodes remains unchanged during the network’s life. In contrast, our examples suggested that for real networks the static hypothesis is not appropriate. Instead, we should incorporate growth into our network models.
It demonstrated, however, that growth alone cannot explain the emergence of power laws.
They are hubs. The better known they are, the more links point to them. The more links they attract, the easier it is to find them on the Web and so the more familiar we are with them. […] The bottom line is that when deciding where to link on the Web, we follow preferential attachment: When choosing between two pages, one with twice as many links as the other, about twice as many people link to the more connected page. While our individual choices are highly unpredictable, as a group we follow strict patterns.
The model is very simple, as growth and preferential attachment lead to an algorithm defined by two straightforward rules:
A. Growth: For each given period of time we add a new node to the network. This step underscores the fact that networks are assembled one node at a time.
B. Preferential attachment: We assume that each new node connects to the existing nodes with two links. The probability that it will chose a given node is proportional to the numver of links the chosen node has. That is, given the choice between two nodes, one with twice as many links as the other, it is twice as likely that the new node will connect to the more connected node.
The how and why remain for each are of application though.
In Hollywood, 94 percent of links are internal, formed when two established actors work together for the first time.
These shifts in thinking created a set of opposites: static versus growing, random versus scale-free, structure versus evolution.
[…] Does the presence of power laws imply that real networks are the result of a phase transition from disorder to order? The answer we’ve arrived at is simple: Networks are not en route from a random to an ordered state. Neither are they at the edge of randomness and chaos. Rather, the scale-free topology is evidence of organizing principles acting at each stage of the network formation process. There is little mystery here, since growth and preferential attachment can explain the basic features of the networks see in nature. No matter how large and complex a network becomes, as long as preferential attachment and growth are present it will maintain its hub-dominated scale-free topology.
The introduction of fitness does not eliminate growth and preferential attachment, the two basic mechanisms governing network evolution. It changes, however, what is considered attractive in a competitive environment. In the scale-free model, we assumed that a node’s attractiveness was determined solely by it’s number of links. In a competitive environment, fitness also plays a role: Nodes with higher fitness are linked to more frequently. A simple way to incorporate fitness into the scal-free model is to assume that preferential attachment is driven by the product of the node’s fitness and the number of links it has. Each new node decides where to link by comparing the fitness connectivity product of all available nodes and linking with a higher probability to those that have a higher product and therefore are more attractive.
Bianconi’s calculation s first confirmed our suspicion that in the presence of fitness the early bird is not necessarily the winner. Rather, fitness is in the driver’s seat, making or breaking the hubs.
But there was a indeed a precise mathematical mapping between the fitness model of a Bose gas. According to this mapping, each node in the network corresponds to an energy level in the Bose gas.
…in some networks, the winner can take all. Just as in a Bose-Einstein condensate all particles crowd into the the lowest energy level, leaving the rest of the energy levels unpopulated, in some networks the fittest node could theoretically grab all the links, leaving none for the rest of the nodes. The winner takes all.
But even though each system, from the Web to Hollywood, has a unique fitness distribution, Bianconi’s calculation indicated that in terms of topology all networks fall into one of only two possible categories. […] The first category includes all networks in which, despite the fierce competition for links, the scale-free topology survives. These networks display a fit-get-rich behavior, meaning that the fittest node will inevitably grow to beome the biggest hub. The winner’s lead is never significant, however. The largest hub is closely followed by a smaller one, which acquires almost as many links as the fittest node. Ata any moment we have a hierarchy of nodes whose degree distribution follows a power law. In most complex networks, the power laws and the fight for links thus are not antagonistic but can coexist peacefully.
In […] the second category, the winner takes all, meaning tht the fittest node grabs all the links, leaving very little for the rest of the nodes. Such networks develop a star topology. […] A winner-takes-all network is not scale-free.
…the western blackout highlighted an often ignored property of complex networks: vulnerability due to interconnectivity
Yet, if the number of removed nodes reaches a critical point, the system abruptly breaks into tiny unconnected islands.
Computer simulations we performed on networks generated by the scale-free model indicated that a significant fraction of nodes can be randomly removed from any scale-free network without its breaking apart.
…percolation theory, the field of physics that developed a set of tools that now are widely used in studies of random networks.
…they set out to calculate the fraction of nodes that must be removed from an arbitrarily chosen network, random or scale-free, to break it into pieces. On one hand, their calculation accounted for the well-known result that random networks fall apart after a critical number of nodes have been removed. On the other hand, they found that for scale-free networks the critical threshold disapears in cases where the degree exponent is smaller or equal to three.
Disable a few of the hubs and a scale-free network will fall to pieces in no time.
If, however, a drug or an illness shuts down the genes encoding the most connected proteins, the cell will not survive.
Obviously, the likelihood that a local failure will handicap the whole system is much higher if we perturb the most-connected nodes. This was supported by the findings of Duncan Watts, from Columbia University, who investigated a model designed to capture the generic features of cascading failures, such as power outages, and the opposite phenomenon, the cascading popularity of books, movies, and albums, which can be described within the same framework.
If a new product passes the crucial test of the innovators, based on their recommendation, the early adopters will pick it up.
What, if any, role is played by the social network in the spread of a virus or an innovation?
In 1954, Elihu Katz, a researcher at the Bureau of Applied Social Research at columbia University, circulated a proposal to study the effect of social ties on behavior.
When it came to the spread of tetracyclin, the doctors named by three or more other doctors as friends were three times more likely to adopt the new drug than those who had not been named by anybody.
Hubs, often referred to in marketing as “opinion leaders,” “power users,” or “influencers,” are individuals who communicate with more people about a certain product than does the average person.
Aiming to explain the disappearance of some fads and viruses and the spread of others, social scientists and epidemiologists developed a very useful tool called the threshold model.
any relation to Granovetter?
…critical threshold, a quantity determined by the properties of the network in which the innovation spreads.
For decades, a simple but powerful paradigm dominated our treatment of diffusion problems. If we wanted to estimate the probability that an innovation would spread, we needed only to know it’s spreading rate and the critical threshold it faced. Nobody questioned this paradigm. Recently, however, we have learned that some viruses and innovations are oblivious to it.
On the Internet, computers are not connected to each other randomly.
In scale-free networks the epidemic threshold miraculously vanished!
Hubs are among the first infected thanks to their numerous sexual contacts. Once infected, they quickly infect hundreds of others. If our sex web formed a homogeneous, random, network, AIDS might have died out long ago. The scale-free topology at AIDS’s disposal allowed the virus to spread and persist.
As we’ve established, hubs play a key role in these processes. Their unique role suggest a bold but cruel solution: As long as resources are finite we should treat only the hubs. That is, when a treatment exists but there is not enough money to offer it to everybody who needs it, we should primarily give it to the hubs. (Pastor-Satorras and Vespignani; and Zoltan Dezso)
Are we prepared to abandon the less connected patients for the benefit of the population at large?
They [Michalis Faloutsos, Petros Faloutsos, and Christos Faloutsos] found that the connectivity distribution of the Internet routers follows a power law. In their seminar paper “On Power-Law Relationship of the Internet Topology” they showed that the Internet […] is a scale-free network.
Routers offering more bandwidth likely have more links as well. […] This simple effect is a possible source of preferential attachment. We do not know for sure whether it is the only one, but preferential attachment is unquestionably present on the Internet.
After many discussions and tutorials on how computers communicate, a simple but controversial idea emerged: parasitic computing.
Starting from any page (on the Internet), we can reach only about 24 percent of all documents.
If you want to go from A to D, you can start from node A, then go to node B, which has a link to node C, which points to D. But you can’t make a round-trip.
Not necessarily the case with bidirectional webmentions.
[Cass] Sustein fears that by limiting access to conflicting viewpoints, the emerging online universe encourages segregation and social fragmentation. Indeed, the mechanisms behind social and political isolation on the Web are self-reinforcing.
Looks like we’ve known this for a very long time! Sadly it’s coming to a head in the political space of 2016 onward.
Communities are essential components of human social history. Granovetter’s circles of friends, the elementary building blocks of communities, pointed to this fact. […]
early indications that Facebook could be a thing…
One reason is that there are no sharp boundaries between various communities. Indeed, the same Website can belong simultaneously to different groups. For example, a physicist’s Webpage might mix links to physics, music, and mountain climbing, combining professional interests with hobbies. In which community should we place such a page? The size of communities also varies a lot. For example, while the community interested in “cryptography” is small and relatively easy to locate, the one consisting of devotees of “English literature” is much harder to identify and fragmented into many subcommunities ranging from Shakespeare enghusiasts to Kurt Vonnegut fans.
Search for this type of community problem is an NP complete problem. This section may be of interest to Brad Enslen and Kicks Condor. Cross reference research suggested by Gary Flake, Steve Lawrence, and Lee Giles from NEC.
Such differences in the structure of competing communities have important consequences for their ability to market and organize themselves for a common cause.
He continues to talk about how the pro-life movement is better connected and therefore better equipped to fight against the pro-choice movement.
Code–or software–is the bricks and mortar of cyberspace. The architecture is what we build, using the code as building blocks. The great architects of human history, from Michelangelo to Frank Lloyd Wright, demonstrated that, whereas raw materials are limited, the architectural possibilities are not. Code can curtail behavior, and it does influence architecture. It does not uniquely determine it, however.
Added on November 3, 2018 at 5:26 PM
Yes, we do have free speech on the Web. Chances are, however, that our voices are too weak to be heard. pages with only a few incoming links are impossible to find by casual browsing. Instead, over and over we are steered toward the hubs. It is tempting to believe that robots can avoid this popularity-driven trap.
Facebook and Twitter applications? Algorithms help to amplify “unheard” voices to some extent, but gamifying the reading can also get people to read more (crap) than they were reading before because it’s so easy.
Your ability to find my Webpage is determined by one factor only: its position on the Web.
Facebook takes advantage of this with their algorithm
Thus the Web’s large-scale topology–that is, its true architecture–enforces more severe limitations on our behavior and visibilityon the Web than government or industry could ever achieve by tinkering with the code. Regulations come and go, but the topology and the fundamental natural laws governing it are time invariant. As long as we continue to delegate to the individual the choice of where to link, we will not be able to significantly alter the Web’s large-scale topology, and we will have to live with the consequences.
hmmm?
After selling Alexa to Amazon.com in 1999
Brewster Kahle’s Alexa Internet company is apparently the root of the Amazon Alexa?
To return to our car analogy, it is…
Where before? I don’t recall this at all. Did it get removed from the text?
ref somewhere about here… personalized medicine
After researching the available databases, we settled on a new one, run by the Argonne National Laboratory outside Chicago, nicknamed “What Is There?” which compiled the matabolic network of forty-three diverse organisms.
…for the vast majority of organisms the ten most-connected molecules are the same. Adenosine triphosphate (ATP) is almost always the biggest hub, followed closely by adenosine diphosphate (ADP) and water.
A key prediction of the scale-free model is that nodes with a large number of links are those that have been added early to the network. in terms of metabolism this would imply that the most connected molecules should be the oldest ones within the cell. […] Therefore, the first mover advantage seems to pervade the emergence of life as well.
Comparing the metabolic network of all forty-three organisms, we found that only 4 percent of the molecules appear in all of them.
Developed by Stanley Fields in 1989, the two-hybrid method offers a relatively rapid semiautomated technique for detecting protein-protein interactions.
They [the results of work by Oltvai, Jeong, Barabasi, Mason (2000)] demonstrated that the protein interaction network has a scale-free topology.
…the cell’s scale-free topology is a result of a common mistake cells make while reproducing.
In short, it is now clear that the number of genes is not proportional to our perceived complexity.
We have learned that a sparse network of a few powerful directors controls all major appointments in Fortune 1000 companies; […]
Regardless of industry and scope, the network behind all twentieth century corporations has the same structure: It is a tree, where the CEO occupies the root and the bifurcating branches represent the increasingly specialized and nonoverlapping tasks of lower-level managers and workers. Responsibility decays as you move down the branches, ending with the drone executors of orders conceived at the roots.
Only for completely top down , but what about bottom up or middle out?
We have gotten to the point that we can produce anything that we can dream of. The expensive question now is, what should that be?
It is a fundamental rethinking of how to respond to the new business environment in the postindustrial era, dubbed the information economy.
This is likely late, but certainly an early instance of “information economy” in popular literature.
Therefore, companies aiming to compete in a fast-moving marketplace are shifting from a static and optimized tree into a dynamic and evolving web, offering a more malleable, flexible command structure.
While 79 percent of directors serve on only one board, 14 percent serve on two, and about 7 percent serve on three or more.
Indeed, the number of companies that entered in partnership with exactly k other institutions, representing the number of links they have within the network, followed a power law, the signature of a scale-free topology.
Makes me wonder if the 2008 economic collapse could have been predicted by “weak” links?
As research, innovation, product development, and marketing become more and more specialized and divorced from each other, we are converging to a network economy in which strategic alliances and partnerships are the means for survival in all industries.
This is troubling in the current political climate where there is little if any trust or truth being spread around by the leader of the Republican party.
As Walter W. Powell writes in Neither Market nor Hierarchy: Network Forms of Organization, “in markets the standard strategy is to drive the hardest possible bargain on the immediate exchange. In networks, the preferred option is often creating indebtedness and reliance over the long haul.” Therefore, in a network economy, buyers and suppliers are not competitors but partners. The relationship between them is often very long lasting and stable.
Trump vs. Trump
The stability of these links allows companies to concentrate on their core business. If these partnerships break down, the effects can be severe. Most of the time failures handicap only the partners of the broken link. Occasionally, however, they send ripples through the whole economy. As we will see next, macroeconomic failures can throw entire nations into deep financial disarray, while failures in corporate partnerships can severly damage the jewels of the new economy.
In some sense this predicts the effects of the 2008 downturn.
outsourcing
early use of the word?
A me attitude, where the companies immediate financial balance is the only factor, limits network thinking. Not understanding how the actions of one node affect other nodes easily cripples whole segments of the network.
Hierarchical thinking does not fit a network economy.
We must help eliminate the need and desire of the nodes to form links to terrorist organizations by offering them a chance to belong to more constructive and meaningful webs.
And for poverty and gangs as well as immigration.
“Their work has a powerful philosophy: “revelation through concealment.” By hiding the details they allow us to focus entirely on the form. The wrapping sharpens our vision, making us more aware and observant, turning ordinary objects into monumental sculptures and architectural pieces.
not too dissimilar to the font I saw today for memory improvement
👓 Words We’re Watching: ‘Ratioed’ | Merriam-Webster
A quantitative measure of how little they like your take.
👓 Acting Attorney General Matt Whitaker's old tweets are really something | Mashable
'Dave Matthews is the Jimmy Buffett of our time.'
👓 Twitter to remove ‘like’ tool in a bid to improve the quality of debate | Telegraph
Twitter is planning to remove the ability to "like" tweets in a radical move that aims to improve the quality of debate on the social network.
Deplatforming and making the web a better place
The particular design question I’m personally looking at is roughly:
How can we reshape the web and social media in a way that allows individuals and organizations a platform for their own free speech and communication without accelerating or amplifying the voices of the abhorrent fringes of people espousing broadly anti-social values like virulent discrimination, racism, fascism, etc.?
In some sense, the advertising driven social media sites like Facebook, Twitter, et al. have given the masses the equivalent of not simply a louder voice within their communities, but potential megaphones to audiences previously far, far beyond their reach. When monetized against the tremendous value of billions of clicks, there is almost no reason for these corporate giants to filter or moderate socially abhorrent content. Their unfiltered and unregulated algorithms compound the issue from a societal perspective. I look at it in some sense as the equivalent of the advent of machine guns and ultimately nuclear weapons in 20th century warfare and their extreme effects on modern society.
The flip side of the coin is also potentially to allow users the ability to better control and/or filter out what they’re presented on platforms and thus consuming, so solutions can relate to both the output as well as the input stages.
Comments and additions to the page (or even here below) particularly with respect to positive framing and potential solutions on how to best approach this design hurdle for human communication are more than welcome.
Deplatforming
Deplatforming or no platform is a form of banning in which a person or organization is denied the use of a platform (physical or increasingly virtual) on which to speak.
In addition to the banning of those with socially unacceptable viewpoints, there has been a long history of marginalized voices (particularly trans, LGBTQ, sex workers, etc.) being deplatformed in systematic ways.
The banning can be from any of a variety of spaces ranging from physical meeting spaces or lectures, journalistic coverage in newspapers or television to domain name registration, web hosting, and even from specific social media platforms like Facebookor Twitter. Some have used these terms as narrowly as in relation to having their Twitter “verified” status removed.
“We need to puncture this myth that [deplatforming]’s only affecting far-right people. Trans rights activists, Black Lives Matterorganizers, LGBTQI people have been demonetized or deranked. The reason we’re talking about far-right people is that they have coverage on Fox News and representatives in Congress holding hearings. They already have political power.” — Deplatforming Works: Alex Jones says getting banned by YouTube and Facebook will only make him stronger. The research says that’s not true. in Motherboard 2018-08-10
Examples
Glenn Beck
Glenn Beck parted ways with Fox News in what some consider to have been a network deplatforming. He ultimately moved to his own platform consisting of his own website.
Reddit Communities
Reddit has previously banned several communities on its platform. Many of the individual users decamped to Voat, which like Gab could potentially face its own subsequent deplatforming.
Milo Yiannopoulos
Milo Yiannopoulos, the former Breitbart personality, was permanently banned from Twitter in 2016 for inciting targeted harassment campaigns against actress Leslie Jones. He resigned from Breitbart over comments he made about pedophilia on a podcast. These also resulted in the termination of a book deal with Simon & Schuster as well as the cancellation of multiple speaking engagements at Universities.
The Daily Stormer
Neo-Nazi site The Daily Stormer was deplatformed by Cloudflare in the wake of 2017’s “Unite the Right” rally in Charlottesville. Following criticism, Matthew Prince, Cloudflare CEO, announced that he was ending the Daily Stormer’s relationship with Cloudflare, which provides services for protecting sites against distributed denial-of service (DDoS) attacks and maintaining their stability.
Alex Jones/Infowars
Alex Jones and his Infowars were deplatformed by Apple, Spotify, YouTube, and Facebook in late summer 2018 for his Network’s false claims about the Newtown shooting.
- Deplatforming Works in Motherboard
Gab
Gab.ai was deplatformed from PayPal, Stripe, Medium †, Apple, and Google as a result of their providing a platform for alt-right and racist groups as well as the shooter in the Tree of Life Synagogue shooting in October 2018
Gab.com is under attack. We have been systematically no-platformed by App Stores, multiple hosting providers, and several payment processors. We have been smeared by the mainstream media for defending free expression and individual liberty for all people and for working with law enforcement to ensure that justice is served for the horrible atrocity committed in Pittsburgh. Gab will continue to fight for the fundamental human right to speak freely. As we transition to a new hosting provider Gab will be inaccessible for a period of time. We are working around the clock to get Gab.com back online. Thank you and remember to speak freely.
—from the Gab.ai homepage on 2018-10-29
- Gab, the Social Media Site for the Alt-Right, Gets Deplatformed
- Microsoft threatened to stop hosting the alt-right’s favorite social network in Quartz 2018-08-10
History
- Face the Racist Nation from On The Media | WNYC Studios on 2018-08-31 includes a segment about deplatforming racist groups from news coverage in the early 1900’s.
- ‘By Whatever Means Necessary’: The Origins of the ‘No Platform Policy’ on Hatful of History on 2015-11-03
Articles
- Deplatforming Works: Alex Jones says getting banned by YouTube and Facebook will only make him stronger. The research says that’s not true. in Motherboard 2018-08-10
- Study finds Reddit’s controversial ban of its most toxic subreddits actually worked TechCrunch on 2017-09-11
- The case for quarantining extremist ideas by Joan Donovan and Danah Boyd in The Guardian on 2018-06/01
Research
- You Can’t Stay Here: The Efficacy of Reddit’s 2015 Ban Examined Through Hate Speech Proc. ACM Hum.-Comput. Interact., Vol. 1, No. 2, Article 31. Publication date: November 2017.
See Also
- web hosting
- why
- shadow banning
- NIPSA
- demonitazition – a practice (particularly leveled at YouTube) of preventing users and voices from monetizing their channels. This can have a chilling effect on people who rely on traffic for income to support their work (see also 1)
🔖 The Ambivalent Internet: Mischief, Oddity, and Antagonism Online by Whitney Phillips and Ryan M. Milner
This book explores the weird and mean and in-between that characterize everyday expression online, from absurdist photoshops to antagonistic Twitter hashtags to deceptive identity play.
Whitney Phillips and Ryan M. Milner focus especially on the ambivalence of this expression: the fact that it is too unwieldy, too variable across cases, to be essentialized as old or new, vernacular or institutional, generative or destructive. Online expression is, instead, all of the above. This ambivalence, the authors argue, hinges on available digital tools. That said, there is nothing unexpected or surprising about even the strangest online behavior. Ours is a brave new world, and there is nothing new under the sun – a point necessary to understanding not just that online spaces are rife with oddity, mischief, and antagonism, but why these behaviors matter.
The Ambivalent Internet is essential reading for students and scholars of digital media and related fields across the humanities, as well as anyone interested in mediated culture and expression.
👓 Deplatforming Works | Motherboard
Alex Jones says getting banned by YouTube and Facebook will only make him stronger. The research says that's not true.
👓 How Students Engage with News: Five Takeaways for Educators, Journalists, and Librarians | Project Information Literacy Research Institute
Abstract: The News Study research report presents findings about how a sample of U.S. college students gather information and engage with news in the digital age. Results are included from an online survey of 5,844 respondents and telephone interviews with 37 participants from 11 U.S. colleges and universities selected for their regional, demographic, and red/blue state diversity. A computational analysis was conducted using Twitter data associated with the survey respondents and a Twitter panel of 135,891 college-age people. Six recommendations are included for educators, journalists, and librarians working to make students effective news consumers. To explore the implications of this study’s findings, concise commentaries from leading thinkers in education, libraries, media research, and journalism are included.
Perhaps there are some interesting segments and even references relevant to the topics of education and IndieWeb for Greg McVerry‘s recent project?
As I read this, I can’t help but think of some things I’ve seen Michael Caulfield writing about news and social media over the past several months. As I look, I notice that he’s already read and written a bit about a press release for this particular paper. I’ll have to take a look at his take on it tomorrow. I’m particularly interested in any insights he’s got on lateral reading and fake news above and beyond his prior thoughts.
Perhaps I missed it hiding in there reading so late at night, but another potentially good source for this paper’s recommended section would be Caulfield’s book Web Literacy for Student Fact-Checkers.
Highlights, Quotes, Annotations, & Marginalia
The purpose of this study was to better understand the preferences, practices, and motivations of young news consumers, while focusing on what students actually do, rather than what they do not do. ❧
October 22, 2018 at 08:28PM
YouTube (54%), Instagram (51%) or Snapchat (55%) ❧
I’m curious to know which sources in particular they’re using on these platforms. Snapchat was growing news sources a year ago, but I’ve heard those sources are declining. What is the general quality of these sources?
For example, getting news from television can range from PBS News Hour and cable news networks (more traditional sources) to comedy shows like Stephen Colbert and The Daily Show with Trevor Noah which have some underlying news in the comedy, but are far from traditional sources.
October 22, 2018 at 08:35PM
Some students (28%) received news from podcasts in the preceding week. ❧
October 22, 2018 at 08:38PM
news is stressful and has little impact on the day-to-day routines —use it for class assignments, avoid it otherwise.” While a few students like this one practiced news abstinence, such students were rare. ❧
This sounds a bit like my college experience, though I didn’t avoid it because of stressful news (and there wasn’t social media yet). I generally missed it because I didn’t subscribe directly to publications or watch much television. Most of my news consumption was the local college newspaper.
October 22, 2018 at 08:46PM
But on the Web, stories of all kinds can show up anywhere and information and news are all mixed together. Light features rotate through prominent spots on the “page” with the same weight as breaking news, sports coverage, and investigative pieces, even on mainstream news sites. Advertorial “features” and opinion pieces are not always clearly identified in digitalspaces. ❧
This difference is one of the things I miss about reading a particular newspaper and experiencing the outlet’s particular curation of their own stories. Perhaps I should spend more time looking at the “front page” of various news sites?
October 22, 2018 at 08:57PM
Some (36%) said they agreed that the threat of “‘fake news’ had made them distrust the credibility of any news.” Almost half (45%) lacked confidence with discerning “real news” from “fake news,” and only 14% said they were “very confident” that they could detect “fake news.” ❧
These numbers are insane!
October 22, 2018 at 09:04PM
As a matter of recourse, some students in the study “read the news laterally,” meaning they used sources elsewhere on the Internet to compare versions of a story in an attempt to verify its facts, bias, and ultimately, its credibility.25 ❧
This reminds me how much I miss the old daily analysis that Slate use to do for the day’s top news stories in various outlets in their Today’s Papers segment.
October 22, 2018 at 09:15PM
Some respondents, though not all, did evaluate the veracity of news they shared on social media. More (62%) said they checked to see how current an item was, while 59% read the complete story before sharing and 57% checked the URL to see where a story originated (Figure 7). Fewer read comments about a post (55%) or looked to see how many times an item was tweeted or shared (39%). ❧
I’m not sure I believe these self-reported numbers at all. 59% read the complete story before sharing?! 57% checked the URL? I’ll bet that not that many could probably define what a URL is.
October 22, 2018 at 10:00PM
information diet ❧
October 22, 2018 at 11:02PM
At the tactical level, there are likely many small things that could be tested with younger audiences to help them better orient themselves to the crowded news landscape. For example, some news organizations are more clearly identifying different types of content such as editorials, features, and backgrounders/news analysis.57More consistent and more obvious use of these typological tags would help all news consumers, not just youth, and could also travel with content as itis posted and shared in social media. News organizations should engage more actively with younger audiences to see what might be helpful. ❧
October 22, 2018 at 11:37PM
When news began moving into the first digital spaces in the early 1990s, pro-Web journalists touted the possibilities of hypertext links that would give news consumers the context they needed. Within a couple of years, hypertext links slowly began to disappear from many news stories. Today, hypertext links are all but gone from most mainstream news stories. ❧
October 22, 2018 at 11:38PM
“Solutions journalism’ is another promising trend that answers some of the respondents’ sense of helplessness in the face of the barrage of crisis coverage.62 ❧
October 22, 2018 at 11:40PM