End-to-End Joint Modeling for Fake News Detection

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dc.contributor.authorRahman, Munshi Mahbubur
dc.contributor.authorFoulds, James R.
dc.date.accessioned2020-05-18T13:24:27Z
dc.date.available2020-05-18T13:24:27Z
dc.descriptionMid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL)en_US
dc.description.abstractThe rapid spread of misinformation, including misleading and manipulative content, is a current and urgent threat to our society and to our democracy (Starbird, 2017). Several fact-checking websites (e.g., Snopes.com and PolitiFact.com) have been formed to manually verify/falsify claims, but this process is expensive and lacks scalability. An automated process to verify these claims is in high demand so that we can keep up with the speed that misinformation spreads.en_US
dc.description.urihttp://jfoulds.informationsystems.umbc.edu/papers/2020/Rahman%20(2020)%20-%20End-to-End%20Joint%20Modeling%20for%20Fake%20News%20Detection%20(MASC-SLL).pdfen_US
dc.format.extent2 pagesen_US
dc.genreconference papers and proceedignsen_US
dc.identifierdoi:10.13016/m2hq2l-9a3w
dc.identifier.citationMunshi Mahbubur Rahman and James R. Foulds,End-to-End Joint Modeling for Fake News Detection, http://jfoulds.informationsystems.umbc.edu/papers/2020/Rahman%20(2020)%20-%20End-to-End%20Joint%20Modeling%20for%20Fake%20News%20Detection%20(MASC-SLL).pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/18650
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleEnd-to-End Joint Modeling for Fake News Detectionen_US
dc.typeTexten_US

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