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Material and methods: A bibliographic search was carried out until April 2022, in the following electronic databases: Pubmed/Medline, Scopus, Scielo, Google Scholar and Web of Science. We included studies that were case-control and cohort studies, dealing with the association between orthodontic treatment and TMD, in English and Spanish, and with no time limit. The Newcastle-Ottawa scale was used to assess risk in the included studies. In addition, RevMan 5.3 was considered for meta-analysis, using as a measure the ODDS ratio in a random-effects model with a 95% confidence interval.
Results: The preliminary search yielded a total of 686 articles, discarding those that did not meet the selection criteria, leaving only 6 articles. These studies reported that there is a significant association between orthodontic treatment and the occurrence of TMD, equivalent to an ODDS ratio of 1.84 with a confidence interval of 1.19-2.83.
A filter bubble or ideological frame is a state of intellectual isolation that can result from personalized searches, where a website algorithm selectively curates what information a user would like to see based on information about the user, such as location, past click-behavior, and search history. Consequently, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles, resulting in a limited and customized view of the world. The choices made by these algorithms are only sometimes transparent. Prime examples include Google Personalized Search results and Facebook's personalized news-stream.
The term filter bubble was coined by internet activist Eli Pariser circa 2010. In Pariser's influential book under the same name, The Filter Bubble (2011), it was predicted that individualized personalization by algorithmic filtering would lead to intellectual isolation and social fragmentation. The bubble effect may have negative implications for civic discourse, according to Pariser, but contrasting views regard the effect as minimal and addressable.According to Pariser, users get less exposure to conflicting viewpoints and are isolated intellectually in their informational bubble. He related an example in which one user searched Google for "BP" and got investment news about British Petroleum, while another searcher got information about the Deepwater Horizon oil spill, noting that the two search results pages were "strikingly different" despite use of the same key words. 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.
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." An internet user's past browsing and search history is built up over time when they indicate interest in topics by "clicking links, viewing friends, putting movies in [their] queue, reading news stories," and so forth. An internet firm then uses this information to target advertising to the user, or make certain types of information appear more prominently in search results pages.
As of 2011, 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.
Pariser's idea of the filter bubble was popularized after the TED talk in May 2011, in which he gave 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 2011, 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," and "creates the impression that our narrow self-interest is all that exists." In his view, filter bubbles are potentially harmful to both individuals and society. He criticized Google and Facebook for offering users "too much candy and not enough carrots." He warned that "invisible algorithmic editing of the web" may limit our exposure to new information and narrow our outlook. According to Pariser, the detrimental effects of filter bubbles include harm to the general society in the sense that they have the possibility of "undermining civic discourse" and making people more vulnerable to "propaganda and manipulation." He wrote:
A filter bubble has been described as exacerbating a phenomenon that called splinternet or cyberbalkanization,[Note 1] which happens when the internet becomes divided 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 1996. Other terms have been used to describe this phenomenon, including "ideological frames" and "the figurative sphere surrounding you as you search the internet."
The concept of a filter bubble has been extended into other areas, to describe societies that self-segregate according political views but also economic, social, and cultural situations. That bubbling results in a loss of the broader community and creates the sense that for example, children do not belong at social events unless those events were especially planned to be appealing for children and unappealing for adults without children.
On the other hand, filter bubbles are implicit mechanisms of pre-selected personalization, where a user's media consumption is created by personalized algorithms; the content a user sees is filtered through an AI-driven algorithm that reinforces their existing beliefs and preferences, potentially excluding contrary or diverse perspectives. In this case, users have a more passive role and are perceived as victims of a technology that automatically limits their exposure to information that would challenge their world view. Some researchers argue, however, that because users still play an active role in selectively curating their own newsfeeds and information sources through their interactions with search engines and social media networks, that they directly assist in the filtering process by AI-driven algorithms, thus effectively engaging in self-segregating filter bubbles.
Despite their differences, the usage of these terms go hand-in-hand in both academic and platform studies. It is often hard to distinguish between the two concepts in social network studies, due to limitations in accessibility of the filtering algorithms, that perhaps could enable researchers to compare and contrast the agencies of the two concepts. This type of research will continue to grow more difficult to conduct, as many social media networks have also begun to limit API access needed for academic research.
There are conflicting reports about the extent to which personalized filtering happens 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 of 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 personalize individual internet experiences further if they choose to do so. For instance, the technology exists for Google to keep track of users' 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,[failed verification] 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. 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.[failed verification] 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. 041b061a72