Analysis and Detection of "Pink Slime" Websites in Social Media Posts

Local news outlets play a vital role in providing trusted and relevant information to communities and addressing their specific needs and concerns. The emergence of news outlets posing as local sources and their spread on social media presents a significant challenge in the digital information landscape. This paper presents a comprehensive study investigating tweets featuring "Pink slime" news, which is a term that has been used to refer to these news outlets due to its deceptive nature. By analyzing a large dataset of tweets, we gain valuable insights into the patterns of these tweets and the origin of these tweets including automated accounts. We show in this work that extracting syntactical features proves valuable in developing a classification approach for detecting such tweets and show that the approach achieves 92.5% accuracy. We also show that our approach achieves near-perfect detection when grouping the tweets by URL.