TY - JOUR TI - Automatic deception detection: Methods for finding fake news: Automatic Deception Detection: Methods for Finding Fake News AU - Conroy, Niall J. AU - Rubin, Victoria L. AU - Chen, Yimin T2 - Proceedings of the Association for Information Science and Technology AB - This research surveys the current state‐of‐the‐art technologies that are instrumental in the adoption and development of fake news detection. “Fake news detection” is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres. The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories – linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network‐based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system. DA - 2015/// PY - 2015 DO - 10.1002/pra2.2015.145052010082 DP - DOI.org (Crossref) VL - 52 IS - 1 SP - 1 EP - 4 J2 - Proc. Assoc. Info. Sci. Tech. LA - en SN - 23739231 ST - Automatic deception detection UR - http://doi.wiley.com/10.1002/pra2.2015.145052010082 Y2 - 2020/04/01/07:03:53 KW - Data science KW - Fake news KW - Journalism KW - Machine learning KW - Natural language processing KW - Network analysis ER -