During the recent Federal election, I was watching the election night coverage, and following the discussion taking place on my Twitter feed. It quickly became apparent that the people in my Twitter feed who were following the elections universally unhappy at the outcome. I saw one dissenting voice, and that was to suggest that the result was perhaps not quite the enormous disaster that people suggested. Of course, the result didn’t tally with what I was seeing. What had happened?
I’d created a filter bubble. It’s where search result algorithms (based on someone’s previous search history) tend to filter out results that the person doesn’t like or agree with. Eli Pariser in The Filter Bubble argued that it’s a potential problem for democracy because people would tend to get less rounded political information over time.
Interestingly, during the election campaign, Twitter agreed with my feed, if not so one-sidedly (which contrasted with one person’s experience of the UK elections in 2011). This could be based on the younger demographic in social media. But Chen and Vroem in an Australian Electoral Commission study found that the age range of political Twitter users was quite varied.
Is this filter bubble a problem for me? Only if I use my current Twitter feed for political analysis. I mostly follow librarians and writers on Twitter, and generally use it for work and study. Ironically, I’d created a bubble with a tool useful for breaking them.
The context in which you use a tool is important, and there are times when you absolutely need the web to filter for you. Could it be that librarians (who largely work in the public sector) and writers (ever-hopeful of Arts Council grants) tend to be more left-of-centre? Possibly. What I do know is that the bubble wasn’t a problem this time. I wasn’t at all surprised by the result.