End-to-End Joint Modeling for Fake News Detection

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Rahman (2020) - End-to-End Joint Modeling for Fake News Detection (MASC-SLL).pdf (157.49 KB)

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http://jfoulds.informationsystems.umbc.edu/papers/2020/Rahman%20(2020)%20-%20End-to-End%20Joint%20Modeling%20for%20Fake%20News%20Detection%20(MASC-SLL).pdf

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http://hdl.handle.net/11603/18650

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conference papers and proceedigns
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Munshi 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).pdf

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Abstract

The 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.