![]() However, it is not only your own behavior that influences the results. This in turn strengthens the filter, making it more likely that you will receive similar results in the future. Then, when you click among the first search results (as most people do), you are confirming back to the search engine that the results were indeed relevant and/or interesting. ![]() When you perform a Google search, the information about you is used in addition to your search term to find and prioritize the search results most likely to be of your interest. Parisier’s main argument is that this narrowing creates a filter bubble, which is invisible to the user, but still has immense impact on the information available to the individual. Combining this with information about your social network, viewing habits and geography leads to an increasingly narrow view on the information available online. Your search and browse history is a key piece of the information used to tailor the results you get when you perform online searches. Search algorithms are using large quantities of information about the user to find and present relevant information to the individual user. In this book, Parisier explains how the internet search engines and their algorithms are creating a situation where users increasingly are getting information that confirms their prior beliefs. In 2011, Eli Parisier released The Filter Bubble: What the Internet Is Hiding From You ( 1). ![]() The aim of this paper is to shed light on the effects of the filter bubble on online personal health information. Second, the way the algorithms work can lead to the creation of a filter bubble ( 1), to use Eli Parisier’s term. First, most people do not know about this filtering, and even if they do, it is still inherently difficult to understand and grasp how it influences the search results. This is a problem for at least two reasons. The main reason why this may happen is that the technology we are using is hiding the complexity of the search algorithms, and is not revealing the additional information on which the filtering is based. In our example, it could mean the difference between choosing to vaccinate a child, or leaving it vulnerable to common, easily preventable diseases. However, as I will argue in this paper, there are serious problems with this, which in certain situations can mean the difference between life and death. In most cases, this personalized search is beneficial to us, since it produces results that seem relevant to the user. In fact, over 200 so called “signals” go into that simple search, making your results almost certainly different from mine. What’s interesting is that the results are sorted not only by objective relevance, but rather is heavily influenced by your search history, your social network, when you are searching, and where you are searching from. Most people will click one of the links on that first page. The results are sorted and presented to you, with the ten top hits on the first page. You will get an overwhelming number of results (at the time of writing, I got about 35 million hits). You go to Google Search, and search for “vaccines and children”. Imagine sitting in front of your computer trying to decide whether or not your children are to be vaccinated against common childhood diseases.
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