A Topic-Agnostic Approach for Identifying Fake News Pages

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.

<ccs2012> <concept> <concept\_id>10010147.10010257.10010258.10010259.10010263</concept\_id> <concept\_desc>Computing methodologies Supervised learning by classification</concept\_desc> <concept\_significance>500</concept\_significance> </concept> <concept> <concept\_id>10010147.10010257.10010321.10010336</concept\_id> <concept\_desc>Computing methodologies Feature selection</concept\_desc> <concept\_significance>100</concept\_significance> </concept> </ccs2012>

This work was partially supported by the DARPA MEMEX and D3M programs, NSF award CNS-1229185, and the Brazilian National Council for the Improvement of Higher Education (CAPES) under grant 88887.130294/2017-00. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.

{'source': 'arXiv_out/1905'}