Creating Task-Generic Features for Fake News Detection

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dc.contributor.authorOlivieri, Alex
dc.contributor.authorShabani, Shaban
dc.contributor.authorSokhn, Maria
dc.contributor.authorCudré-Mauroux, Philippe
dc.date.accessioned2019-01-03T00:35:18Z
dc.date.available2019-01-03T00:35:18Z
dc.date.issued2019-01-08
dc.description.abstractInformation spreads at a pace never seen before on online platforms, even when this information is fake. Fake news can have substantial impact, for instance when it concern politics and influences the results of legislations or elections. Finding a methodology to verify if some piece of news is true or false is hence essential. In this work, we propose a methodology to create task-generic features that are paired with textual features in order to detect fake news. Task-generic features are created by elaborating on metadata attached to answers from Google’s search engine, and by using crowdsourcing for missing values. We experimentally validate our method on a dataset for fake news detection based on the PolitiFact website. Our results show an improvement in F1-Score of 3% over the state of the art, which is significant for a 6-class task.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.624
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59956
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTruth and Lies: Deception and Cognition on the Internet
dc.subjectInternet and the Digital Economy
dc.subjectCrowdsourcing
dc.subjectFake News
dc.subjectGoogle Custom Search
dc.subjectMachine Learning
dc.subjectPolitics
dc.titleCreating Task-Generic Features for Fake News Detection
dc.typeConference Paper
dc.type.dcmiText

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Truth and Lies: Deception and Cognition on the Internet