The filter bubble, a term created by Eli Pariser (Jackson, 2017), refers to the situation when websites use algorithms to assume and deduce what information a user would be interested in and providing them with information based on this deduction and assumption (Pariser, 2011). Thus, a filter bubble can lead to users becoming insulated from information and data that contradicts their points of view, successfully isolating them in their personal ideological, intellectual and cultural “bubbles” (Pariser, 2011). The filter bubble can be used to various domains of a person’s online life, such as the feeds of their social media platforms, the results of their Google search (Bohannon, 2015), showing users personalized and customized information depending on their online activity (Holone, 2016). Most of the users are not aware of the presence of the filter bubble (Brinkmann, 2018) and since the number of online users keeps on increasing (Chaffey, 2018), the preponderance of the filter bubble grows as well (Jackson, 2017).
There have been arguments that the filter bubble takes away people’s control (Rowland, 2011). According to Kaushik (2011), deciding and choosing the relevant and useful information through algorithms, social media platforms can show customized and captivating user content. Regardless of that, research indicates that this process generates the echo chamber effect, referring to the idea that online users are “more likely to engage with people and media sources that share their political beliefs” (Chesire, 2017; Sunstein, 2001). As Flaxman et al (2016) state, as a consequence the filter bubble and the echo chamber effect can lead to the expansion of online segregation. This seems to take place in the expansion of political announcements among individuals on Facebook that are given significant information from millennials and sources that have the same political opinions (Mitchell et al., 2015). Hence, indicating that the filter bubble can lead to particular political exposure, which could cause political attitude polarization (Dylko et al., 2017).
Since many users are not informed about the existence of the filter bubble and its consequences, they tend to assume that they receive online information and data from a wide range of different sources, rather than becoming and being isolated to their personal interests (Lumb, 2015). It turns out that the filter bubble influences and affects the better connected users more than the average users (Gottron and Schwagereit, 2016). Moreover, the filter bubble is useful and needed to create better and easier online content for users (Pothier, 2017). However, Holly Green (2011) argues that people should be able to control the filters and the chosen and selected information that they receive online.
Since the effects and consequences of the filter bubble have been noticed and observed and have become important in the media and the academic literature, it is expected and believed that online users will take action and get rid of the filter bubble to escape from becoming a victim and from getting affected by their personal biases (Adee, 2016; El-Bermawy, 2016). To achieve this goal, before challenging the content shown, the individuals need to acknowledge the existence of the filter bubble and the fact that they are being influenced by it. However, the reality indicates that online users will keep on using platforms that have algorithms with their own information to experience and obtain content that agrees with their personal viewpoints (Hempel, 2017).
Bibliography
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