background preloader

How to Burst the "Filter Bubble" that Protects Us from Opposing Views

How to Burst the "Filter Bubble" that Protects Us from Opposing Views
The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world. Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm. This is an insidious problem. Much social research shows that people prefer to receive information that they agree with instead of information that challenges their beliefs. This problem is compounded when social networks recommend content based on what users already like and on what people similar to them also like. This is the filter bubble—being surrounded only by people you like and content that you agree with. And the danger is that it can polarise populations creating potentially harmful divisions in society. It’s certainly a start.

https://www.technologyreview.com/s/522111/how-to-burst-the-filter-bubble-that-protects-us-from-opposing-views/

Related:  Network Science

US Military Scientists Solve the Fundamental Problem of Viral Marketing Viral messages begin life by infecting a few individuals and then start to spread across a network. The most infectious end up contaminating more or less everybody. Just how and why this happens is the subject of much study and debate. Network scientists know that key factors are the rate at which people become infected, the “connectedness” of the network and how the seed group of individuals, who first become infected, are linked to the rest. It is this seed group that fascinates everybody from marketers wanting to sell Viagra to epidemiologists wanting to study the spread of HIV.

ReseauPetri2 Réseaux de Petri Compléments Blocage Filter bubble A filter bubble is a result of a personalized search in which a website algorithm selectively guesses what information a user would like to see based on information about the user (such as location, past click behavior and search history) and, as a result, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles. Prime examples are Google Personalized Search results and Facebook's personalized news stream. The term was coined by internet activist Eli Pariser in his book by the same name; according to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Concept[edit]

The Global Brain Institute The GBI uses scientific methods to better understand the global evolution towards ever-stronger connectivity between people, software and machines. By developing concrete models of this development, we can anticipate both its promises and its perils. That would help us to steer a course towards the best possible outcome for humanity.

Pearltrees Radically Redesigns Its Online Curation Service To Reach A Wider Audience Pearltrees, the Paris-based online curation service that launched in late 2009, was always known for its rather quirky Flash-based interface that allowed you to organize web bookmarks, photos, text snippets and documents into a mindmap-like structure. For users who got that metaphor, it was a very powerful service, but its interface also presented a barrier to entry for new users. Today, the company is launching a radical redesign that does away with most of the old baggage of Pearltrees 1.0.

Escape your search engine Filter Bubble! DuckDuckGo does not collect or share personal information. That is our privacy policy in a nutshell. The rest of this page tries to explain why you should care. Last updated on 04/11/12. New research to uncover nuances of networks Feb. 20, 2013 9:01 a.m. When a species disappears from a region, the rest of the ecosystem may flourish or collapse, depending on the role that species played. When a storm rolls across the coast, the power grid might reconfigure itself quickly or leave cities dark for days. A snowstorm might mean business as usual in a hardy city and a severe food shortage in another, depending on the distribution strategies of residents. Each of these systems is a kind of network, with thousands of members and relationships linking them. Understanding how networks behave is key to ensuring their functioning.

Virala nyheter: Hur nyheter sprids och bemöts i sociala medier Detta är en Master-uppsats från Göteborgs universitet/Institutionen för journalistik, medier och kommunikation Sammanfattning: AbstractBakgrund: När människor tar del av nyheter via sociala medier som Twitter och Facebook ärdet möjligt för andra användare i de sociala medierna att påverka uppfattningen av innehållet.Det kan exempelvis ske genom att lyfta fram, tona ned, omtolka eller omgestalta nyheterna.Hur detta sker och vad det får för konsekvenser för hur människor uppfattar nyheterna hartidigare inte undersökts. Dessutom saknas det kunskaper om vilka nyheter från massmediernasom sprids i social medier, i synnerhet i Sverige.Syfte: Beskriva och jämföra vilka nyheter som sprids i sociala medier (Facebook och Twitter)samt undersöka de psykologiska orsakerna till varför de sprids vidare.Metod: Artiklar (N = 89 450) från de tolv största svenska nyhetssajterna under två månader ibörjan av 2014 undersöktes hur de spreds på Facebook och Twitter med en kvantitativinnehållsanalys.

Math algorithm tracks crime, rumours, epidemics to source (Phys.org) -- A team of EPFL scientists has developed an algorithm that can identify the source of an epidemic or information circulating within a network, a method that could also be used to help with criminal investigations. Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees. Small world experiment The "six degrees of separation" model The small-world experiment comprised several experiments conducted by Stanley Milgram and other researchers examining the average path length for social networks of people in the United States. The research was groundbreaking in that it suggested that human society is a small-world-type network characterized by short path-lengths. The experiments are often associated with the phrase "six degrees of separation", although Milgram did not use this term himself. Historical context of the small-world problem[edit] Mathematician Manfred Kochen and political scientist Ithiel de Sola Pool wrote a mathematical manuscript, "Contacts and Influences", while working at the University of Paris in the early 1950s, during a time when Milgram visited and collaborated in their research.

Related: