skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Award ID contains: 1742702

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; (, 20th IEEE Int'l Conf. on Data Mining (ICDM))
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. ; (, 29th ACM Int'l Conf. on Information and Knowledge Management (CIKM))
    null (Ed.)
    Full Text Available 
  3. ; (, Joint European Conf. on Machine Learning and Principles & Practice of Knowledge Discovery in Databases (ECML-PKDD))
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; (, 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining)
    Full Text Available 
  5. ; ; ; ; ; (, 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining)
    Full Text Available 
  6. ; (, arXiv)
    null (Ed.)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Conf. on Empirical Methods in Natural Language Processing (EMNLP))
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, American Behavioral Scientist)
    As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content. 
    more » « less