The Junk News Aggregator: Examining junk news posted on Facebook, starting with the 2018 US Midterm Elections
Abstract
In recent years, the phenomenon of online misinformation and junk news circulating on social media has come to constitute an important and widespread problem affecting public life online across the globe, particularly around important political events such as elections. At the same time, there have been calls for more transparency around misinformation on social media platforms, as many of the most popular social media platforms function as "walled gardens," where it is impossible for researchers and the public to readily examine the scale and nature of misinformation activity as it unfolds on the platforms. In order to help address this, we present the Junk News Aggregator, a publicly available interactive web tool, which allows anyone to examine, in near real-time, all of the public content posted to Facebook by important junk news sources in the US. It allows the public to gain access to and examine the latest articles posted on Facebook (the most popular social media platform in the US and one where content is not readily accessible at scale from the open Web), as well as organise them by time, news publisher, and keywords of interest, and sort them based on all eight engagement metrics available on Facebook. Therefore, the Aggregator allows the public to gain insights on the volume, content, key themes, and types and volumes of engagement received by content posted by junk news publishers, in near real-time, hence opening up and offering transparency in these activities as they unfold, at scale across the top most popular junk news publishers. In this way, the Aggregator can help increase transparency around the nature, volume, and engagement with junk news on social media, and serve as a media literacy tool for the public.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2019
- DOI:
- 10.48550/arXiv.1901.07920
- arXiv:
- arXiv:1901.07920
- Bibcode:
- 2019arXiv190107920L
- Keywords:
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- Computer Science - Social and Information Networks