Searching for "fake news"

fake news society schools

https://www.edsurge.com/news/2022-05-05-are-we-doomed-to-a-culture-where-fake-news-wins-not-if-schools-can-help-it

this infographic from the European Association for Viewers Interests which took me on a tour of ten types of misleading news—propaganda, clickbait, sponsored content, satire and hoax, error, partisan, conspiracy theory, pseudoscience, misinformation and bogus information.

  • recognize our own biases
  • “nonsense detectors
  • we examine and analyze the authority of the original source
  • we should triangulate the information

research on fake news

Why do Americans share so much fake news? One big reason is they aren’t paying attention, new research suggests

a new study in Nature

Lack of attention was the driving factor behind 51.2% of misinformation sharing among social media users who participated in an experiment conducted by a group of researchers from MIT, the University of Regina in Canada, University of Exeter Business School in the United Kingdom and Center for Research and Teaching in Economics in Mexico.

 

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

Fake News

From the US election to the Covid-19 crisis, every major event is now accompanied by a wave of fake news. But is this…

Posted by Le Monde diplomatique in English on Tuesday, February 2, 2021

https://mondediplo.com/2021/01/13fakenews

free e-book

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

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

1 2 3 13