In order to ensure that this thread continues to meet TL standards and follows the proper guidelines, we will be enforcing the rules in the OP more strictly. Be sure to give them a re-read to refresh your memory! The vast majority of you are contributing in a healthy way, keep it up!
NOTE: When providing a source, explain why you feel it is relevant and what purpose it adds to the discussion if it's not obvious. Also take note that unsubstantiated tweets/posts meant only to rekindle old arguments can result in a mod action.
On March 09 2018 07:42 Velr wrote: I just got invited to a womens demo next saturday.
And uninvited because they only want "women, transwomen and people with feminist identity" 2 minutes later.
Thanks USA... That shit grew on your turf.
I feel like that quote means they want cis-women, non-cis-women, and people who support women/ gender equity/ sex equity to be at that pro-women conference. Doesn't seem weird to me.
I just heard Steve Bannon face palming from northern Canada.
Everything about that was just terrible.
First there is absolutely no message, it's literally just a compilation of video game violence. Like they just uploaded it to the white house youtube so they could show Trump what they were working on from his phone and the unlisted link got leaked.
How do you make a compilation like that without GTA? Was there a similar push when horror movies became a thing that they were inspiring serial killers?
On March 09 2018 12:16 {CC}StealthBlue wrote: We might start to see some not so random car bombs going off in Saudi Arabia if this keeps up.
That's going to make the people making the "we support a despotic anti-democratic regime, because we don't want them to have nukes like the ones we oppose, for the sake of democracy. Not because we'll support pure evil if it acts in our interests" argument look pretty silly.
ESA members generally treat this sort of video violence with some degree of care. Like using appropriate YouTube thumbnails and gating the content behind an age wall and a whole bunch of other warnings.
I tried checking but it didn’t seem like the Trump Administration put an age wall behind that YouTube video. The thumbnail was also of some guy getting their brains blown out in Sniper Elite. As usual, they went in guns blazing only to do a whole lot of collateral damage because they didn’t use their collective brains.
And yes, there was definitely a pushback against horror movies. I dunno about the US but there was a period in UK cinema that the authorities went after so called video nasties.
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
I mean I rummaged through the jargon already, but I'm not sure what your point is?
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
I mean I rummaged through the jargon already, but I'm not sure what your point is?
That they weren't cherry picking stories or reaching for conclusions. That quote the article took (and you commented on) looks pretty dumb unless you see the associated graphs (or read the full paragraph): + Show Spoiler +
There's a lot of statistical jargon that I don't really care to dig into, but barring things like "rumour cascades" and "Complementary cumulative distribution functions", it really is just numbers comparisons that are very heavily weighted toward falsehoods.
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
I mean I rummaged through the jargon already, but I'm not sure what your point is?
That they weren't cherry picking stories or reaching for conclusions. That quote the article took (and you commented on) looks pretty dumb unless you see the associated graphs (or read the full paragraph): + Show Spoiler +
There's a lot of statistical jargon that I don't really care to dig into, but barring things like "rumour cascades" and "Complementary cumulative distribution functions", it really is just numbers comparisons that are very heavily weighted toward falsehoods.
I didn't mean they were cherry picking, just that they (the article especially) aren't being clear to the layman how much data they really looked at or the potential impact of it's sources. The article is definitely reaching for conclusions.
I dug through way more of that study and found as I suspected what they were saying and what was being extracted for the article aren't quite the same thing.
For the sake of keeping this relatively simple, explain to me (anyone) what it means when they say:
Stories determined to be true rarely went wider than 1,000 people
I feel like this has an obvious interpretation, but that doesn't make sense on it's face, so this must mean more than it's literal translation. What does it mean?
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
I mean I rummaged through the jargon already, but I'm not sure what your point is?
That they weren't cherry picking stories or reaching for conclusions. That quote the article took (and you commented on) looks pretty dumb unless you see the associated graphs (or read the full paragraph): + Show Spoiler +
There's a lot of statistical jargon that I don't really care to dig into, but barring things like "rumour cascades" and "Complementary cumulative distribution functions", it really is just numbers comparisons that are very heavily weighted toward falsehoods.
I don't know why it'd be a surprising result tbh.
Most people don't read much beyond the title of anything that ever gets posted, and I'd wager that a majority of circulated stories start out with only a small handful of very active social media enthusiasts, who are much more likely to share if it has a more outlandish claim or ridiculous story(more likely to be false), and once it catches on, it's basically never going to be stopped until the story burns out on its own.
The much less interesting truth of most things is informative, but doesn't quite pique the interest the same way that XXX gave YYY a blowjob in the office does.
Stories determined to be true rarely went wider than 1,000 people
I feel like this has an obvious interpretation, but that doesn't make sense on it's face, so this must mean more than it's literal translation. What does it mean?
It's actually a bit hard to dumb down, and I'm not sure I'm getting entirely what "rumour cascades" actually amount to. But lets say you heard a rumour from a "friend of a friend of a friend".
Each friend is a person who is telling the story, as opposed to a person just retweeting. That 1,000 people is the number of people retweeting that one friend.
I think the "cascade" is basically a person actively spreading the rumour (like a new tweet or discussion) as opposed to just a retweet. So a true rumour hits more than 1,000 people total, but each "cascade" rarely hits more than 1,000.
And by percentage comparison, false rumours basically spread further and cascade more than true rumours.
On March 09 2018 17:02 GreenHorizons wrote: For the sake of keeping this relatively simple, explain to me (anyone) what it means when they say:
Stories determined to be true rarely went wider than 1,000 people
I feel like this has an obvious interpretation, but that doesn't make sense on it's face, so this must mean more than it's literal translation. What does it mean?
It's actually a bit hard to dumb down, and I'm not sure I'm getting entirely what "rumour cascades" actually amount to. But lets say you heard a rumour from a "friend of a friend of a friend".
Each friend is a person who is telling the story, as opposed to a person just retweeting. That 1,000 people is the number of people retweeting that one friend.
I think the "cascade" is basically a person actively spreading the rumour (like a new tweet or discussion) as opposed to just a retweet. So a true rumour hits more than 1,000 people total, but each "cascade" rarely hits more than 1,000.
And by percentage comparison, false rumours basically spread further and cascade more than true rumours.
Which I guess is why this seems largely meaningless to me. It's basically useless for determining the depth and sincerity with which the root of the rumor true or false is held in public awareness.
So a popular joke that ends up getting 'debunked' on snopes for clicks turns into evidence that false stories spread faster than true ones with a study like this. Satire will be picked up as people sharing 'false stories' in this study and more stuff like that. I mean Onion articles could be captured in this as "false stories" and them being more popular than a story about what actually happened being shared less (because they've heard that story on every other possible media source/platform), inferring that less people are aware of the truth than the lie that's a popular meme/joke on twitter.
I mean I can think of basic/boring news stories that are far more widely known than some of the most viral twitter rumors, so something like "This marks the 18th year at war in a row" might not be as widely shared on twitter as "Obama personally executes Donald Trump in Guantanamo and replaces him with lizard clone" and the one that ends up on snopes is the backstory for the lizard clone, not confirmation that we are going on 18 years at 'war' with terrorism.
I mean I'm far from being able to tell them how to get a clear picture math or variable wise, but this is generally pretty useless information without any potential practical application.
False stories consistently are shared "farther, faster, deeper and more broadly, in every category,” Aral said, and are 70 percent more likely to be passed along than true stories.
Those differences held true in the speed of re-tweets, the number of re-tweets for each story and how many people re-tweeted it.
Stories determined to be true rarely went wider than 1,000 people, but the top 1 percent of false stories were routinely passed on to exponentially more people -- between 1,000 and 100,000.
"Although the inclusion of bots accelerated the spread of both true and false news, it affected their spread roughly equally," said Aral, adding that this means the spread of fake news appears to be "a human phenomenon."
Seems to be a lot wrong with the presentation of an old saying about a lie making it halfway around the world before the truth gets it's boots on.
Maybe I'm missing something but did this article really compare the avg among (their selection of) true news sharing to the top 1% of fake news without even blinking?Then presumed that shares=awareness/understanding/agreement. As if many shares don't come from people making fun of it/trolling?
Also "Stories determined to be true rarely went wider than 1,000 people".... what? What is that even supposed to mean?
I can get behind the idea that lies can be effective and spread faster than truth but this article is going to throw it's back out with all that reaching.
And this is why you should go to the linked study if you want the full and proper analysis and datasets.
Here we investigate the differential diffusion of true, false, and mixed (partially true, partially false) news stories using a comprehensive data set of all of the fact-checked rumor cascades that spread on Twitter from its inception in 2006 to 2017. The data include ~126,000 rumor cascades spread by ~3 million people more than 4.5 million times. We sampled all rumor cascades investigated by six independent fact-checking organizations (snopes.com, politifact.com, factcheck.org, truthorfiction.com, hoax-slayer.com, and urbanlegends.about.com) by parsing the title, body, and verdict (true, false, or mixed) of each rumor investigation reported on their websites and automatically collecting the cascades corresponding to those rumors on Twitter.
Looks like they took an existing collection of fact-checked Twitter rumours and did a lot of number crunching on them.
I mean I rummaged through the jargon already, but I'm not sure what your point is?
I didn't just "rummage" through it. I read it and understood it. Their findings are quite fascinating (and hence why it's published in Science, one of the absolute top scientific journals) and very robustly analyzed. At first glance it seems like there are all kinds of biases that could explain the issue without even worrying about whether a rumor was false or not, the most important of which is that there are simply more true or stories than false stories, and a lot of those are just not interesting, then your ratios are off: you'd have to control for *interesting* true vs. false stories. They control for that and find that false rumors may very well spread more than true rumors because they are more novel.
The authors don't follow through, but it's quite obvious: the truth is often boring, whereas people don't usually bother making boring stuff up. And that is (part of?) why false rumors spread more than true rumors: a false rumor is more likely to be *juicy* than a true rumor. They did not analyze how much of the spread this novelty factor explains, in fact, they state:
The emotions expressed in reply to falsehoods may illuminate additional factors, beyond novelty, that inspire people to share false news. Although we cannot claim that novelty causes retweets or that novelty is the only reason why false news is retweeted more often, we do find that false news is more novel and that novel information is more likely to be retweeted.
If you're interested in doing an MSc in sociometrics, following up on this would be a good topic for a thesis
On March 09 2018 17:02 GreenHorizons wrote: For the sake of keeping this relatively simple, explain to me (anyone) what it means when they say:
Stories determined to be true rarely went wider than 1,000 people
I feel like this has an obvious interpretation, but that doesn't make sense on it's face, so this must mean more than it's literal translation. What does it mean?
It's actually a bit hard to dumb down, and I'm not sure I'm getting entirely what "rumour cascades" actually amount to. But lets say you heard a rumour from a "friend of a friend of a friend".
Each friend is a person who is telling the story, as opposed to a person just retweeting. That 1,000 people is the number of people retweeting that one friend.
I think the "cascade" is basically a person actively spreading the rumour (like a new tweet or discussion) as opposed to just a retweet. So a true rumour hits more than 1,000 people total, but each "cascade" rarely hits more than 1,000.
And by percentage comparison, false rumours basically spread further and cascade more than true rumours.
Which I guess is why this seems largely meaningless to me. It's basically useless for determining the depth and sincerity with which the root of the rumor true or false is held in public awareness.
So a popular joke that ends up getting 'debunked' on snopes for clicks turns into evidence that false stories spread faster than true ones with a study like this. Satire will be picked up as people sharing 'false stories' in this study and more stuff like that. I mean Onion articles could be captured in this as "false stories" and them being more popular than a story about what actually happened being shared less (because they've heard that story on every other possible media source/platform), inferring that less people are aware of the truth than the lie that's a popular meme/joke on twitter.
I mean I can think of basic/boring news stories that are far more widely known than some of the most viral twitter rumors, so something like "This marks the 18th year at war in a row" might not be as widely shared on twitter as "Obama personally executes Donald Trump in Guantanamo and replaces him with lizard clone" and the one that ends up on snopes is the backstory for the lizard clone, not confirmation that we are going on 18 years at 'war' with terrorism.
I mean I'm far from being able to tell them how to get a clear picture math or variable wise, but this is generally pretty useless information without any potential practical application.
Yes. Small steps. Understanding of complex phenomena hardly ever all comes at once in a giant "Eureka" moment. I'm sorry scientific inquiry is too slow for you. Maybe watch a cat video on youtube?
EPT
![[image loading]](https://d2ufo47lrtsv5s.cloudfront.net/content/sci/359/6380/1146/F2.large.jpg)
