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

Published: 13 May 2024 Publication History

Abstract

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 present a significant challenge in the digital information landscape. This paper presents a comprehensive study investigating posts 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 posts, we gain valuable insights into the patterns of these posts and the origin of these posts. We show in this work that extracting syntactical features proves valuable in developing a classification approach for detecting such posts and that the approach achieves 92.5% accuracy. We also show that our approach achieves near-perfect detection when grouping the posts by URL.

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cover image ACM Conferences
WWW '24: Proceedings of the ACM Web Conference 2024
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 May 2024

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Author Tags

  1. classification
  2. information integrity
  3. misinformation
  4. news
  5. social media
  6. tweets

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WWW '24
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WWW '24: The ACM Web Conference 2024
May 13 - 17, 2024
Singapore, Singapore

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