Customized Results and Filter Bubbles
Many search engines such as Google and Bing provide customized results based on the user's activity history. This leads to an effect that has been called a filter bubble. The term describes a phenomenon in which websites use algorithms to selectively guess what information a user would like to see, based on information about the user (such as location, past click behaviour and search history). As a result, websites tend to show only information which agrees with the user's past viewpoint, effectively isolating the user in a bubble that tends to exclude contrary information. Prime examples are Google's personalized search results and Facebook's personalized news stream. According to Eli Pariser, who coined the term, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Pariser 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 and that the two search results pages were "strikingly different." The bubble effect may have negative implications for civic discourse, according to Pariser.
Since this problem has been identified, competing search engines have emerged that seek to avoid this problem by not tracking or "bubbling" users. One example is DuckDuckGo.
Read more about this topic: Web Search Engine
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