Reddit Politics Polarization On Due To New Users

Reddit Politics Machine Learning Model U Of T

Reddit Politics Machine Learning Model U Of T

Reddit Politics Machine Learning Model U Of T
Reddit Politics Machine Learning Model U Of T

Reddit Politics – formerly known as the ‘front page of the internet – has come to be known in recent years as a politically polarizing platform. More and more people are relying on the platform to find news, making it an extremely popular place for political discussion.

Following Donald Trump’s 2015 presidential campaign, the r/TheDonald political subreddit took shape on the site. Other sites, such as r/democracy and r/conservative, also quickly gained popularity, creating endless reverberations and battles between right- and left-wing communities. These changes amplified an extremist, vocal minority, and many have argued that the existing user base on Reddit has become heavily polarized during the elections.

However, a U of T study – led by Isaac Waller, a Ph.D. student in computer science, and Ashton Anderson, an assistant professor in the Department of Computing and Mathematics at UTSC – found that Reddit’s increased polarization is not the product of its existing users, but rather the influx of new users who joined the platform during and after the 2016 US election.

What Can Reddit Politics Teach Us?

What Can Reddit Politics Teach Us?
What Can Reddit Politics Teach Us?

The change to belligerence

This phenomenon is not new. Since the popularity of the Internet, online culture is constantly changing as new users crowd out and change the culture on online forums and platforms. This trend peaked in September 1993, the so-called ‘Eternal September’, when AOL began providing access to more users. This phenomenon pervaded the small group of forums existing at the time, fundamentally altering the social status quo. Since then, the internet has seen a steady stream of new users across a number of platforms – which is exactly what happened to Reddit in 2016.

To study polarization across the platform, Waller and Anderson designed a machine learning model that considered more than 5.1 billion comments to generate community embeddings – scales used to represent and quantify similarity in community membership. These dips show how many individuals are active in particular communities, which are then used to show divisions across multiple lines on social media platforms, including mainstream polarization treat.

A stubborn Internet

A stubborn Internet
A stubborn Internet. Reddit Politics

This research is also important in creating a new analytical vehicle for online platforms. Sociologists are constantly looking for ways to quantify and better understand social connections and group identities, and harnessing machine learning for this task has proven particularly useful.

Reddit politics is an ideal candidate for it, as the platform has yet to implement user-based algorithms that can influence user activity by recommending new content and communities to them. This algorithmic-deficient content allows users to have more organic interactions within the communities they care about and gives them more control over what they consume.

However, content algorithms on other popular platforms impact the communities and circles with which users interact. As a result, they lose control over the content they use. Although there have been some previous studies in this area, they have always focused on platforms like Twitter or Facebook. However, these platforms and their algorithms that generate interactions would not exist if users had more control over what they see.

The study also offers some support for the echo chamber hypothesis, arguing that a group of online people with similar views can often develop some kind of tunnel vision. People end up only joining communities where they know their views will be accepted, which leads them to believe that their views are shared by the majority, even if it isn’t.

As the effects of Eternal September continue and as the ‘netiquette’ changes with the arrival of new, unfamiliar users, these analytical methods will only become more and more powerful in understanding community. online community – and will hopefully give us a better idea of ​​how social media might be affected by real-world events.

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