The filter bubble is most notably linked to Eli Pariser (2011), who defined the bubble as a new generation of internet filters that create a unique universe of information for each of us, fundamentally altering the way we encounter ideas and information. He identified three dynamics of the filter bubble:
- Everyone is alone in their bubble, being pulled away from the increasing sharing economy.
- The filter bubble is invisible, and it is almost impossible to see and control the filters applied.
- There is no choice to entering the filter bubble.
Although using filters helps to make sense of the overwhelming torrent of information available (Pariser, 2011), consumer wants for personalised advertising means that the ‘data market’ held on individuals is subject to issues of security. The information supplied to companies through ‘click signals’ may be information that an individual wouldn’t trust with their closest friend (Pariser, 2011).
Whilst the concept is not new, a study by Eslami et al (2015) suggested that more than 60% of Facebook users are entirely unaware of any curation and believe that every story from friends and followed pages appears on their news feed. The lack of transparency regarding pre-selected personalisation is affecting the way people respond to personalised messages, thus inhibiting media diversity and information access on social platforms (Zuiderveen et al., 2016).
Perhaps one of the biggest features of the filter bubble debate surrounds the 2016 Presidential election and the Brexit Vote, appearing as one of the two biggest conversations about social media after November 2016 (Leetaru, 2017). It is argued that social media was scapegoated in both votes for the shock results that varied heavily from the polls (DiFranzo and Garcia, 2017). Automated accounts were also used: a third of pro-Trump tweets came from automated bots, similarly present for the pro-leave campaign (DiFranzo and Garcia, 2017). This allows parties to engage with online communities, gain credibility and influence (Kelly and Francois, 2018), and ensure that their message curates onto as many timelines as possible (Hern, 2017). When coincided with the isolating filter bubble, an ostracised space of like-minded opinions is created, and diminishes the democracy of seeing both points of view (Farnam Street, 2017).
Facebook was heavily criticised for fuelling the bubble that users experienced when consuming news articles (DiFranzo and Garcia, 2017). Although Facebook retaliated that it was not solely the algorithm, but the choices and clicks of the consumer (Price, 2016), Facebook is increasingly opaque, inhibiting the assessment of the effect the platform had (Hern, 2017).
The rise of the filter bubble actively extends the time spent online: social media has become a vast marketing platform of ready-to-purchase products and a gatekeeper that aggressively encourages people to buy more than is needed (Borkowska, 2018). Whilst filters and personalisation endorse social media platforms as new marketing techniques, a collaborative effort is needed to ensure the security of personal data and allow people to manage their own filters rather than be controlled by them (Eslami et al., 2015).
References
- DiFranzo, D. and Gloria-Garcia, K. (2017) Filter bubbles and fake news. Crossroads. Vol.23(3), pp.32-35.
- Eslami, M., Rickman, A., Vaccaro, K., Amirhossein, A., Vuong, A., Karahalios, K., Hamilton, K. and Sandvig, C. (2015) “I always assumed that I wasn’t really that close to [her]”: Reasoning about Invisible Algorithms in News Feeds. Proceeding.
- Pariser, E (2011) Filter Bubbles: What the Internet isn’t telling you. Penguin Group, London.
- Zuiderveen Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., de Vreese, C. H., & Helberger, N. (2016). Should We Worry about Filter Bubbles? Internet Policy Review, 5(1).
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