ELI PARISER THE FILTER BUBBLE PDF

A filter bubble — a term coined by internet activist Eli Pariser — is a state of intellectual isolation [1] that allegedly 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. The bubble effect may have negative implications for civic discourse , according to Pariser, but contrasting views regard the effect as minimal [7] and addressable. It's super important. It's turned out to be more of a problem than I, or many others, would have expected. The term was coined by internet activist Eli Pariser circa and discussed in his book of the same name; according to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Pariser defined his concept of a filter bubble in more formal terms as "that personal ecosystem of information that's been catered by these algorithms".

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A filter bubble — a term coined by internet activist Eli Pariser — is a state of intellectual isolation [1] that allegedly 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.

The bubble effect may have negative implications for civic discourse , according to Pariser, but contrasting views regard the effect as minimal [7] and addressable. It's super important. It's turned out to be more of a problem than I, or many others, would have expected.

The term was coined by internet activist Eli Pariser circa and discussed in his book of the same name; according to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Pariser defined his concept of a filter bubble in more formal terms as "that personal ecosystem of information that's been catered by these algorithms". This process is not random, as it operates under a three-step process, per Pariser, who states, "First, you figure out who people are and what they like.

Then, you provide them with content and services that best fit them. Finally, you tune to get the fit just right. Your identity shapes your media. Search for a word like "depression" on Dictionary. Open—even for an instant—a page listing signs that your spouse may be cheating and prepare to be haunted with DNA paternity-test ads. Accessing the data of link clicks displayed through site traffic measurements determine that filter bubbles can be collective or individual.

As of , one engineer had told Pariser that Google looked at 57 different pieces of data to personally tailor a user's search results, including non-cookie data such as the type of computer being used and the user's physical location.

Other terms have been used to describe this phenomenon, including " ideological frames " [20] and "the figurative sphere surrounding you as you search the internet". Pariser's idea of the filter bubble was popularized after the TED talk he gave in May , in which he gives examples of how filter bubbles work and where they can be seen.

In a test seeking to demonstrate the filter bubble effect, Pariser asked several friends to search for the word "Egypt" on Google and send him the results. Comparing two of the friends' first pages of results, while there was overlap between them on topics like news and travel, one friend's results prominently included links to information on the then-ongoing Egyptian revolution of , while the other friend's first page of results did not include such links.

In The Filter Bubble , Pariser warns that a potential downside to filtered searching is that it "closes us off to new ideas, subjects, and important information", [32] and "creates the impression that our narrow self-interest is all that exists". He criticized Google and Facebook for offering users "too much candy, and not enough carrots".

A world constructed from the familiar is a world in which there's nothing to learn Many people are unaware that filter bubbles even exist. A filter bubble has been described as exacerbating a phenomenon that has been called splinternet or cyberbalkanization , [Note 1] which happens when the internet becomes divided up into sub-groups of like-minded people who become insulated within their own online community and fail to get exposure to different views.

This concern dates back to the early days of the publicly accessible internet, with the term "cyberbalkanization" being coined in In news media , echo chamber is a metaphorical description of a situation in which beliefs are amplified or reinforced by communication and repetition inside a closed system. By visiting an "echo chamber", people are able to seek out information which reinforces their existing views, potentially as an unconscious exercise of confirmation bias.

This may increase political and social polarization and extremism. The term is a metaphor based on the acoustic echo chamber, where sounds reverberate in a hollow enclosure. They are surrounded by those who acknowledge and follow the same viewpoints.

Barack Obama's farewell address identified a similar concept to filter bubbles as a "threat to [Americans'] democracy", i.

And increasingly we become so secure in our bubbles that we start accepting only information, whether it's true or not, that fits our opinions, instead of basing our opinions on the evidence that is out there.

There are conflicting reports about the extent to which personalized filtering is happening and whether such activity is beneficial or harmful. Analyst Jacob Weisberg, writing in June for Slate , did a small non-scientific experiment to test Pariser's theory which involved five associates with different ideological backgrounds conducting a series of searches, " John Boehner ", " Barney Frank ", " Ryan plan ", and " Obamacare ", and sending Weisberg screenshots of their results.

The results varied only in minor respects from person to person, and any differences did not appear to be ideology-related, leading Weisberg to conclude that a filter bubble was not in effect, and to write that the idea that most internet users were "feeding at the trough of a Daily Me " was overblown. There are reports that Google and other sites maintain vast "dossiers" of information on their users which might enable them to further personalize individual internet experiences if they chose to do so.

For instance, the technology exists for Google to keep track of users' past histories even if they don't have a personal Google account or are not logged into one.

A scientific study from Wharton that analyzed personalized recommendations also found that these filters can actually create commonality, not fragmentation, in online music taste.

A study from Internet Policy Review addressed the lack of a clear and testable definition for filter bubbles across disciplines; this often results in researchers defining and studying filter bubbles in different ways.

Similar views can be found in other academic projects which also address concerns with the definitions of filter bubbles and the relationships between ideological and technological factors associated with them. A study by researchers from Oxford, Stanford, and Microsoft examined the browsing histories of 1.

They selected 50, of those users who were active consumers of news, then classified whether the news outlets they visited were left- or right-leaning, based on whether the majority of voters in the counties associated with user IP addresses voted for Obama or Romney in the presidential election. They then identified whether news stories were read after accessing the publisher's site directly, via the Google News aggregation service, via web searches, or via social media.

The researchers found that while web searches and social media do contribute to ideological segregation, the vast majority of online news consumption consisted of users directly visiting left- or right-leaning mainstream news sites, and consequently being exposed almost exclusively to views from a single side of the political spectrum. Limitations of the study included selection issues such as Internet Explorer users skewing higher in age than the general internet population; Bing Toolbar usage and the voluntary or unknowing sharing of browsing history selecting for users who are less concerned about privacy; the assumption that all stories in left-leaning publications are left-leaning, and the same for right-leaning; and the possibility that users who are not active news consumers may get most of their news via social media, and thus experience stronger effects of social or algorithmic bias than those users who essentially self-select their bias through their choice of news publications assuming they are aware of the publications' biases.

While algorithms do limit political diversity, some of the filter bubble is the result of user choice. Shapiro suggests that online media isn't the driving force for political polarization. The data suggests that the younger demographic isn't any more polarized in than it had been when online media barely existed in The study highlights differences between age groups and how news consumption remains polarized as people seek information that appeals to their preconceptions.

Older Americans usually remain stagnant in their political views as traditional media outlets continue to be a primary source of news while online media is the leading source for the younger demographic. Although algorithms and filter bubbles weaken content diversity, this study reveals that political polarization trends are primarily driven by pre-existing views and failure to recognize outside sources.

A study from Germany utilized the Big Five Psychology model to test the effects of individual personality, demographics, and ideologies on user news consumption. The study also found a negative ideological association between media diversity and the degree to which users align with right-wing authoritarianism. Beyond offering different individual user factors that may influence the role of user choice, this study also raises questions and associations between the likelihood of users being caught in filter bubbles and user voting behavior.

The Facebook study found that it was "inconclusive" whether or not the algorithm played as big a role in filtering News Feeds as people assumed. These "friends" are often acquaintances with whom we would not likely share our politics without the internet. Facebook may foster a unique environment where a user sees and possibly interacts with content posted or re-posted by these "second-tier" friends. The study found that "24 percent of the news items liberals saw were conservative-leaning and 38 percent of the news conservatives saw was liberal-leaning.

Similarly, a study of Twitter 's filter bubbles by New York University concluded that "Individuals now have access to a wider span of viewpoints about news events, and most of this information is not coming through the traditional channels, but either directly from political actors or through their friends and relatives. Furthermore, the interactive nature of social media creates opportunities for individuals to discuss political events with their peers, including those with whom they have weak social ties".

Social bots have been utilized by different researchers to test polarization and related effects that are attributed to filter bubbles and echo chambers. One of the main findings was that after exposure to differing views provided by the bots self-registered republicans became more conservative, whereas self-registered liberals showed less ideological change, if none at all.

A different study from The People's Republic of China utilized social bots on Weibo —the largest social media platform in China—to examine the structure of filter bubbles in regards to their effects on polarization. One being where people with similar views form groups, share similar opinions, and block themselves from differing viewpoints opinion polarization and the other being where people do not access diverse content and sources of information information polarization.

By utilizing social bots instead of human volunteers and focusing more on information polarization rather than opinion-based, the researchers concluded that there are two essential elements of a filter bubble: a large concentration of users around a single topic and a uni-directional, star-like structure that impacts key information flows. For this study, 87 adults in various locations around the continental United States googled three key words at the exact same time: immigration, gun control, and vaccinations.

Even when in private browsing mode, most people saw results unique to them. Google included certain links for some that it did not include for other participants, and the News and Videos infoboxes showed significant variation. Results can differ, but usually for non-personalized reasons. When filter bubbles are in place they can create specific moments that scientists call 'Whoa' moments.

A 'Whoa' moment is when an article, ad, post, etc. Scientists discovered this term after a young woman was performing her daily routine, which included drinking coffee, when she opened her computer and noticed an advertisement for the same brand of coffee that she was drinking. Kind of a 'whoa' moment when the product you're drinking pops up on the screen in front of you. Which means advertisement algorithms target specific users based on their "click behavior" in order to increase their sale revenue.

In The Filter Bubble: What the Internet Is Hiding from You , [66] internet activist Eli Pariser highlights how the increasing occurrence of filter bubbles further emphasizes the value of one's bridging social capital as defined by Robert Putman. Indeed, while bonding capital corresponds on the one hand to the establishment of strong ties between like-minded people, thus reinforcing some sense of social homogeneity, bridging social capital on the other hand represents the creation of weak ties between people with potentially diverging interests and viewpoints, hence introducing significantly more heterogeneity.

Fostering one's bridging capital — for example by connecting with more people in an informal setting — can therefore be an effective way to reduce the influence of the filter bubble phenomenon. Users can in fact take many actions to burst through their filter bubbles, for example by making a conscious effort to evaluate what information they are exposing themselves to, and by thinking critically about whether they are engaging with a broad range of content.

Users can consciously avoid news sources that are unverifiable or weak. Websites such as allsides. Some additional plug-ins , such as Media Bias Fact Check, [75] aimed to help people step out of their filter bubbles and make them aware of their personal perspectives; thus, these media show content that contradicts with their beliefs and opinions.

For instance, Escape Your Bubble asks users to indicate a specific political party they want to be more informed about. Since web-based advertising can further the effect of the filter bubbles by exposing users to more of the same content, users can block much advertising by deleting their search history, turning off targeted ads, and downloading browser extensions.

The European Union is taking measures to lessen the effect of the filter bubble. The European Parliament is sponsoring inquiries into how filter bubbles affect people's ability to access diverse news. News aggregator apps scan all current news articles and direct you to different viewpoints regarding a certain topic. Users can also use a diversely-aware news balancer which visually shows the media consumer if they are leaning left or right when it comes to reading the news, indicating right-leaning with a bigger red bar or left-leaning with a bigger blue bar.

A study evaluating this news balancer found "a small but noticeable change in reading behavior, toward more balanced exposure, among users seeing the feedback, as compared to a control group". In light of recent concerns about information filtering on social media, Facebook acknowledged the presence of filter bubbles and has taken strides toward removing them. Now, the revamped strategy would flip this process and post articles from different perspectives on the same topic.

Facebook is also attempting to go through a vetting process whereby only articles from reputable sources will be shown. Similarly, Google, as of January 30, , has also acknowledged the existence of a filter bubble difficulties within its platform.

Because current Google searches pull algorithmically ranked results based upon "authoritativeness" and "relevancy" which show and hide certain search results, Google is seeking to combat this. By training its search engine to recognize the intent of a search inquiry rather than the literal syntax of the question, Google is attempting to limit the size of filter bubbles. As of now, the initial phase of this training will be introduced in the second quarter of Mozilla's Open Innovation team leads the initiative, striving to combat misinformation, with a specific focus on the product with regards to literacy, research and creative interventions.

As the popularity of cloud services increases, personalized algorithms used to construct filter bubbles are expected to become more widespread. Self-created content manifested from behavior patterns can lead to partial information blindness. Technologists, social media engineers, and computer specialists have also examined the prevalence of filter bubbles. Some scholars have expressed concerns regarding the effects of filter bubbles on individual and social well-being, i.

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Filter bubble

How could this happen when almost everyone they knew had backed the other side? The book laid out how our algorithmically personalised online lives were insulating us from opposing views, predicting how echo chambers could leave users sheltered from alternative opinions. Throughout the campaign, a slew of fabricated articles tapped into the prejudices of pro-Trump or pro-Clinton Facebook users by making up stories they wanted to believe. Filter bubbles may not have caused fake news, but they incubated them and helped them spread. Web users weighed in with ideas ranging from verified news pages to time-delayed re-shares , while others helped add structure, and in one case more attractive formatting. At last count the document clocked in at more than pages long. Facebook has since proposed its own solution — asking users to flag false stories, which are then assessed by third-party fact-checkers.

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But is it really an answer? This may be an era when we are increasingly entitled to our own facts — but should we also be entitled to our own search results? Google looks to your previous queries and the clicks that follow and refines its search results accordingly. If you click on gossip blogs like Gawker rather than Netflix after searching for the names of movie stars, links to Gawker may feature more prominently. Likewise, if you have hundreds of Facebook friends, you see relevant updates only from the closest of them; Facebook relies on your earlier interactions to predict what, and who, is most likely to interest you.

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