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).
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