A filter bubble is a term coined by the Internet activist Eli Pariser to refer to a state of intellectual isolation that can result from personalized searches when 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. As a result, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles. The choices made by these algorithms are not transparent. Prime examples include Google Personalized Search results and Facebook's personalized news-stream. The bubble effect may have negative implications for civic discourse, according to Pariser, but contrasting views regard the effect as minimal and addressable. The results of the U.S. presidential election in 2016 have been associated with the influence of social media platforms such as Twitter and Facebook, and as a result have called into question the effects of the "filter bubble" phenomenon on user exposure to fake news and echo chambers, spurring new interest in the term, with many concerned that the phenomenon may harm democracy and well-being by making the effects of misinformation worse.(Taken from Wikipedia)
Presented by Eli Pariser at a TED Talk in Long Beach, CA in February 2011.
Since the content seen by individual social media users is influenced by algorithms that produce filter bubbles, users of social media platforms are more susceptible to confirmation bias and may be exposed to biased, misleading information. Confirmation Bias is the tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses (Taken from Wikipedia).
The video below explains what confirmation bias is and provides examples.
The video below shares several ways to avoid or lessen confirmation bias.
Other psychology terms related to beliefs and social influence:
Definitions taken from Wikipedia
