Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19

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dc.contributor.authorBoukouvalas, Zois
dc.contributor.authorMallinson, Christine
dc.contributor.authorCrothers, Evan
dc.contributor.authorJapkowicz, Nathalie
dc.contributor.authorPiplai, Aritran
dc.contributor.authorMittal, Sudip
dc.contributor.authorJoshi, Anupam
dc.contributor.authorAdali, Tulay
dc.date.accessioned2021-08-09T17:26:15Z
dc.date.available2021-08-09T17:26:15Z
dc.date.issued2020-06-01
dc.description.abstractSocial media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria.en_US
dc.description.sponsorshipWe thank Dr. Kenton White, Chief Scientist at Advanced Symbolics Inc, for providing the initial Twitter dataset.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/924/Independent-Component-Analysis-for-Trustworthy-Cyberspace-during-High-Impact-Events-An-Application-to-Covid-19en_US
dc.format.extent9 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2sqmq-xrbk
dc.identifier.urihttp://hdl.handle.net/11603/22346
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Center for Social Science Scholarship
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty 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.en_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleIndependent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19en_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-9151-8347en_US

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