User:Gzjthomas10/sandbox

 Filter Bubble page edit 

Similar Concepts Edit:

[Include opening header for Echo Chamber]

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.

Another emerging term for this echoing and homogenizing effect on the Internet within social communities is cultural tribalism.

Barack Obama's farewell address identified a similar concept to filter bubbles as a "threat to [Americans'] democracy", i.e., the "retreat into our own bubbles, ...especially our social media feeds, surrounded by people who look like us and share the same political outlook and never challenge our assumptions... 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."

Reactions Edit:

Media Reactions
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 2011 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. Weisberg asked Google to comment, and a spokesperson stated that algorithms were in place to deliberately "limit personalization and promote variety". Book reviewer Paul Boutin did a similar experiment to Weisberg's among people with differing search histories, and again found that the different searchers received nearly identical search results. Interviewing programmers at Google off the record journalist Per Grankvist found that user data used to play a bigger role in determining search results but that Google, through testing, found that the search query is by far the best determinator on what results to display.

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. One report stated that Google had collected "10 years' worth" of information amassed from varying sources, such as Gmail, Google Maps, and other services besides its search engine,[not in citation given] although a contrary report was that trying to personalize the Internet for each user was technically challenging for an Internet firm to achieve despite the huge amounts of available data.[citation needed] Analyst Doug Gross of CNN suggested that filtered searching seemed to be more helpful for consumers than for citizens, and would help a consumer looking for "pizza" find local delivery options based on a personalized search and appropriately filter out distant pizza stores.[not in citation given] Organizations such as the Washington Post, The New York Times, and others have experimented with creating new personalized information services, with the aim of tailoring search results to those that users are likely to like or agree with.

Academia Studies and Reactions
A scientific study from Wharton that analyzed personalized recommendations also found that these filters can actually create commonality, not fragmentation, in online music taste. Consumers reportedly use the filters to expand their taste rather than to limit it. Harvard law professor Jonathan Zittrain disputed the extent to which personalization filters distort Google search results, saying that "the effects of search personalization have been light". Further, Google provides the ability for users to shut off personalization features if they choose, by deleting Google's record of their search history and setting Google to not remember their search keywords and visited links in the future.

A study by researchers from Oxford, Stanford, and Microsoft examined the browsing histories of 1.2 million U.S. users of the Bing Toolbar add-on for Internet Explorer between March and May 2013. They selected 50,000 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 2012 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).

Platform Studies
While algorithms do limit political diversity, some of the filter bubble is the result of user choice. In a study by data scientists at Facebook, they found that for every four Facebook friends that share ideology, users have one friend with contrasting views. No matter what Facebook's algorithm for its News Feed is, people are simply more likely to befriend/follow people who share similar beliefs. The nature of the algorithm is that it ranks stories based on a user's history, resulting in a reduction of the "politically cross-cutting content by 5 percent for conservatives and 8 percent for liberals". However, even when people are given the option to click on a link offering contrasting views, they still default to their most viewed sources. "[U]ser choice decreases the likelihood of clicking on a cross-cutting link by 17 percent for conservatives and 6 percent for liberals." A cross-cutting link is one that introduces a different point of view than the user’s presumed point of view, or what the website has pegged as the user’s beliefs.

Thus 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. The study also found that “individual choice,” or confirmation bias, likewise affected what gets filtered out of News Feeds. Some social scientists criticized this conclusion though, because the point of protesting the filter bubble is that the algorithms and individual choice work together to filter out News Feeds. They also criticized Facebook’s small sample size, which is about “9% of actual Facebook users”, and the fact that the study results are “not reproducible” due to the fact that the study was conducted by “Facebook scientists” who had access to data that Facebook does not make available to outside researchers.

Though the study found that only about 15-20% of the average user’s Facebook friends subscribe to the opposite side of the political spectrum, Julia Kaman from Vox theorized that this could have potentially positive implications for viewpoint diversity. 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.” "Liberals tend to be connected to fewer friends who share information from the other side, compared with their conservative counterparts"  This interplay has the ability to provide diverse information and sources that could moderate users’ views.

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”. According to these studies, social media may be diversifying information and opinions users come into contact with, though there is much speculation around filter bubbles and their ability to create deeper political polarization.

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. appears on your computer that is in relation to a current action or current use of an object. 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. “Sat down and opened up Facebook this morning while having my coffee, and there they were two ads for Nespresso. Kind of a ‘whoa’ moment when the product you're drinking pops up on the screen in front of you.” ‘Whoa’ moments occur when people are “found.” Which means advertisement algorithms target specific users based on their ‘click behavior’ in order to increase their sale revenue. ‘Whoa’ moments can also ignite discipline in users to stick to a routine and commonality with a product.

Several designers have developed tools to counteract the effects of filter bubbles (see § Counter measures). Swiss radio station SRF voted the word filterblase (the German translation of filter bubble) word of the year 2016.

Media Company Countermeasures edit:

By media companies[edit]
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. In January 2017, Facebook removed personalization from its Trending Topics list in response to problems with some users not seeing highly talked-about events there. Facebook’s strategy is to reverse the Related Articles feature that it had implemented in 2013, which would post related news stories after the user read a shared article. 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. Along with the founder of Craigslist and a few others, Facebook has invested $14 million into efforts "to increase trust in journalism around the world, and to better inform the public conversation”. The idea is that even if people are only reading posts shared from their friends, at least these posts will be credible.

Similarly, Google, as of January 30, 2018, 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 2018. Questions that involve bias and/or controversial opinions will not be addressed until a later time, prompting a larger problem that exists still: whether the search engine acts either as an arbiter of truth or as a knowledgeable guide by which to make decisions by.

In April 2017 news surfaced that Facebook, Mozilla, and Craigslist Craig contributed to the majority of a $14M donation to CUNY’s “News Integrity Initiative,” poised at eliminating fake news and creating more honest news media.

Later, in August, Mozilla, whose services host the Firefox web engine, announced the formation of the Mozilla Information Trust Initiative (MITI). The MITI would serve as a collective effort to develop products, research, and community-based solutions to combat the effects of filter bubbles and the proliferation of fake news. 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.

[Insert Facebook/Cambridge Analytica]

Revelations in March 2018 of Cambridge Analytica's harvesting and use of user data for at least 87 million Facebook profiles during the 2016 presidential election highlight the ethical implications of filter bubbles. Co-Founder and whistleblower of Cambridge Analytica Christopher Wylie, detailed how the firm had the ability to develop "psychographic" profiles of those users and use the information to shape their voting behavior. Access to user data by third parties such as Cambridge Analytica can exasperate and amplify existing filter bubbles users have created, artificially increasing existing biases and further divide societies.