Filter bubble

Share This
« Back to Glossary Index

Eli Pariser first introduced the term ‘filter bubble’ in 2010 to describe the individualized information ecosystem formed by algorithms, which is influenced by a user’s online activity. This concept significantly impacts the type of content and advertisements a user comes across. For example, Google[1], a significant advocate of this idea, uses 57 unique data points to tailor search results for each user. This can occur on an individual or group scale and often results in political, economic, social, and cultural division. The filter bubble goes beyond mere personalization as it can lead to intellectual isolation by restricting exposure to diverse perspectives, thereby potentially jeopardizing democracy and societal health. It’s also comparable to echo chambers, which refer to exposure to a limited spectrum of views. Nonetheless, it’s important to remember that there are methods to counteract filter bubbles, like fostering critical thinking and advocating for algorithm[2] transparency.

Terms definitions
1. Google ( Google ) Primarily acknowledged for its search engine, Google is a universally esteemed technology corporation. The company, established in 1998 by Sergey Brin and Larry Page, has expanded significantly, branching out into numerous tech-related fields. Google offers a wide array of services and products, encompassing Android, YouTube, Cloud, Maps, and Gmail. It also manufactures hardware like Chromebooks and Pixel smartphones. Since 2015, Google has been a subsidiary of Alphabet Inc. and is celebrated for its inventive spirit and workplace environment that promotes employees' personal projects. Despite confronting several ethical and legal challenges, Google continues to influence the tech sector with its groundbreaking innovations and technological progress, including the creation of Android OS and the purchase of companies specializing in AI.
2. algorithm. A set of instructions or rules that are clearly defined and offer a solution to a specific problem or task is known as an algorithm. With roots tracing back to ancient civilizations, algorithms have undergone centuries of evolution and today play a pivotal role in contemporary computing. Techniques such as divide-and-conquer are utilized in their creation and their efficiency is assessed via metrics such as big O notation. Algorithms can be depicted in multiple ways, including pseudocode, flowcharts, or programming languages. To execute them, they are translated into a language comprehensible to computers, with the execution speed being influenced by the utilized instruction set. Depending on their design or implementation paradigm, algorithms can be categorized differently, and their level of efficiency can greatly affect processing time. In fields like computer science and artificial intelligence, the comprehension and effective application of algorithms is vital.
Filter bubble (Wikipedia)

A filter bubble or ideological frame is a state of intellectual isolation that can result from personalized searches, recommendation systems, and algorithmic curation. The search results are based on information about the user, such as their 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.

Social media inadvertently isolates users into their own ideological filter bubbles, according to Pariser.

However there are conflicting reports about the extent to which personalized filtering happens and whether such activity is beneficial or harmful, with various studies producing inconclusive results.

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.

« Back to Glossary Index
Keep up with updates