Searching for "fake"

Information Overload Fake News Social Media

Information Overload Helps Fake News Spread, and Social Media Knows It

Understanding how algorithm manipulators exploit our cognitive vulnerabilities empowers us to fight back

https://www.scientificamerican.com/article/information-overload-helps-fake-news-spread-and-social-media-knows-it/

a minefield of cognitive biases.

People who behaved in accordance with them—for example, by staying away from the overgrown pond bank where someone said there was a viper—were more likely to survive than those who did not.

Compounding the problem is the proliferation of online information. Viewing and producing blogs, videos, tweets and other units of information called memes has become so cheap and easy that the information marketplace is inundated. My note: folksonomy in its worst.

At the University of Warwick in England and at Indiana University Bloomington’s Observatory on Social Media (OSoMe, pronounced “awesome”), our teams are using cognitive experiments, simulations, data mining and artificial intelligence to comprehend the cognitive vulnerabilities of social media users.
developing analytical and machine-learning aids to fight social media manipulation.

As Nobel Prize–winning economist and psychologist Herbert A. Simon noted, “What information consumes is rather obvious: it consumes the attention of its recipients.”

attention economy

Nodal diagrams representing 3 social media networks show that more memes correlate with higher load and lower quality of information shared

 Our models revealed that even when we want to see and share high-quality information, our inability to view everything in our news feeds inevitably leads us to share things that are partly or completely untrue.

Frederic Bartlett
Cognitive biases greatly worsen the problem.

We now know that our minds do this all the time: they adjust our understanding of new information so that it fits in with what we already know. One consequence of this so-called confirmation bias is that people often seek out, recall and understand information that best confirms what they already believe.
This tendency is extremely difficult to correct.

Making matters worse, search engines and social media platforms provide personalized recommendations based on the vast amounts of data they have about users’ past preferences.

pollution by bots

Nodal diagrams representing 2 social media networks show that when more than 1% of real users follow bots, low-quality information prevails

Social Herding

social groups create a pressure toward conformity so powerful that it can overcome individual preferences, and by amplifying random early differences, it can cause segregated groups to diverge to extremes.

Social media follows a similar dynamic. We confuse popularity with quality and end up copying the behavior we observe.
information is transmitted via “complex contagion”: when we are repeatedly exposed to an idea, typically from many sources, we are more likely to adopt and reshare it.

Twitter users with extreme political views are more likely than moderate users to share information from low credibility sources

In addition to showing us items that conform with our views, social media platforms such as Facebook, Twitter, YouTube and Instagram place popular content at the top of our screens and show us how many people have liked and shared something. Few of us realize that these cues do not provide independent assessments of quality.

programmers who design the algorithms for ranking memes on social media assume that the “wisdom of crowds” will quickly identify high-quality items; they use popularity as a proxy for quality. My note: again, ill-conceived folksonomy.

Echo Chambers
the political echo chambers on Twitter are so extreme that individual users’ political leanings can be predicted with high accuracy: you have the same opinions as the majority of your connections. This chambered structure efficiently spreads information within a community while insulating that community from other groups.

socially shared information not only bolsters our biases but also becomes more resilient to correction.

machine-learning algorithms to detect social bots. One of these, Botometer, is a public tool that extracts 1,200 features from a given Twitter account to characterize its profile, friends, social network structure, temporal activity patterns, language and other features. The program compares these characteristics with those of tens of thousands of previously identified bots to give the Twitter account a score for its likely use of automation.

Some manipulators play both sides of a divide through separate fake news sites and bots, driving political polarization or monetization by ads.
recently uncovered a network of inauthentic accounts on Twitter that were all coordinated by the same entity. Some pretended to be pro-Trump supporters of the Make America Great Again campaign, whereas others posed as Trump “resisters”; all asked for political donations.

a mobile app called Fakey that helps users learn how to spot misinformation. The game simulates a social media news feed, showing actual articles from low- and high-credibility sources. Users must decide what they can or should not share and what to fact-check. Analysis of data from Fakey confirms the prevalence of online social herding: users are more likely to share low-credibility articles when they believe that many other people have shared them.

Hoaxy, shows how any extant meme spreads through Twitter. In this visualization, nodes represent actual Twitter accounts, and links depict how retweets, quotes, mentions and replies propagate the meme from account to account.

Free communication is not free. By decreasing the cost of information, we have decreased its value and invited its adulteration. 

half truths fake news

++++++++++++
more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=fake+news

CIA analysis guide fake news

https://www.slj.com/?reviewDetail=true-or-false-a-cia-analysts-guide-to-spotting-fake-news

++++++++++++
more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=fake+news

identifying fake news by 90%

Software developed by University College London & UC Berkeley can identify ‘fake news’ sites with 90% accuracy from r/Futurology

Machine learning tool deUC Berveloped to detect fake news domains when they register

http://www.businessmole.com/tool-developed-by-university-college-london-can-identify-fake-news-sites-when-they-are-registered/

Al is not lost though, as academics from UCL and several other institutions have developed a tool that may help us separate the fact from fiction. They have designed a machine learning tool which can cite domains that were created to spread what has now commonly become known as ‘fake news’.

+++++++++
more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=fake+news

Spain tackles fake news

https://english.elpais.com/politics/2020-11-09/spain-to-monitor-online-fake-news-and-give-a-political-response-to-disinformation-campaigns.html

While the text does not mention specific cases, Russian interference has been proven in the 2016 election campaign in the United States, which saw Donald Trump victorious, as well as the Brexit referendum in the United Kingdom the same year, which saw voters narrowly decide they wanted their country to leave the European Union.

the text relies on the classification of the European Commission: “Verifiably false or misleading information created, presented and disseminated for economic gain or to intentionally deceive the public.” This includes electoral processes, but also sectors such as health, environment or security. The text underlines that the current coronavirus pandemic has been accompanied by an “unprecedented infodemic,” i.e. a proliferation of fake news.

The document recognizes that the “news media, digital platforms, academic world, technology sector, NGOs and society in general play an essential role in the fight against disinformation, with actions such as its identification and not contributing to its spread, the promotion of activities that raise awareness and training or the development of tools to avoid its propagation.”

++++++++++++
more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=fake+news

Disinformation and the Cost of Fake News

+++++++++++++++
more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=%23fakenews

deepfake Putin

Deepfake Putin is here to warn Americans about their self-inflicted doom. AI-generated synthetic media is being used in a political ad campaign—not to disrupt the election, but to save it. from r/technology

https://www.technologyreview.com/2020/09/29/1009098/ai-deepfake-putin-kim-jong-un-us-election/

They then worked with a deepfake artist who used an open-source algorithm to swap in Putin’s and Kim’s faces. A post-production crew cleaned up the leftover artifacts of the algorithm to make the video look more realistic. All in all the process took only 10 days. Attempting the equivalent with CGI likely would have taken months, the team says. It also could have been prohibitively expensive.

++++++++++++++++
more on deepfake in this IMS blog
https://blog.stcloudstate.edu/ims?s=deepfake

recognizing deepfake

Reuters releases guide to recognizing deepfake profile photos from r/technology

https://graphics.reuters.com/CYBER-DEEPFAKE/ACTIVIST/nmovajgnxpa/index.html

GAN A generative adversarial network is the name given to dueling computer programs that run through a process of trial and error…  One program, the generator, sequentially fires out millions of attempts at a face; the second program, the discriminator, tries to sniff out whether the first program’s face is a fake. If the discriminator can’t tell, Li said, a deepfake is produced.

+++++++++++++
more on deepfake in this IMS blog
https://blog.stcloudstate.edu/ims?s=deepfake

fake net neutrality comments

Judge orders FCC to hand over IP addresses linked to fake net neutrality comments. from r/technology

Judge Orders FCC to Hand Over IP Addresses Linked to Fake Net Neutrality Comments

The Times’ lawsuit follows reporting by Gizmodo that exposed multiple attempts by the FCC to manufacture stories about hackers attacking its comment system. In reality, the Electronic Comment Filing System (ECFS) crashed, both in 2015 and 2017, after Last Week Tonight host John Oliver instructed millions of his viewers to flood the agency with pro-net neutrality comments.

For over a year, the FCC claimed to have proof that distributed denial-of-service (DDoS) attacks were behind the comment system issues. In August 2018, however, FCC Chairman Ajit Pai finally admitted that wasn’t true. After an inspector general report found no evidence of an attack, Pai sought to pin the blame on his staff—and, for some reason, former President Barack Obama.

Pai stated in an agency memo in 2018 that it was a “fact” that Russian accounts were behind the half-million comments. His attorneys, meanwhile, were arguing the exact opposite in court.

+++++++++++++++++++
more on net neutrality in this IMS blog
https://blog.stcloudstate.edu/ims?s=net+neutrality

1 2 3 4 15