Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
dc.contributor.author | Boukouvalas, Zois | |
dc.contributor.author | Mallinson, Christine | |
dc.contributor.author | Crothers, Evan | |
dc.contributor.author | Japkowicz, Nathalie | |
dc.contributor.author | Piplai, Aritran | |
dc.contributor.author | Mittal, Sudip | |
dc.contributor.author | Joshi, Anupam | |
dc.contributor.author | Adalı, Tülay | |
dc.date.accessioned | 2020-10-22T17:15:32Z | |
dc.date.available | 2020-10-22T17:15:32Z | |
dc.date.issued | 2020-06-30 | |
dc.description.abstract | Social 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.uri | https://arxiv.org/abs/2006.01284 | en_US |
dc.format.extent | 9 pages | en_US |
dc.genre | journal articles preprints | en_US |
dc.identifier | doi:10.13016/m2n0kx-zgui | |
dc.identifier.citation | Boukouvalas, Zois; Mallinson, Christine; Crothers, Evan; Japkowicz, Nathalie; Piplai, Aritran; Mittal, Sudip; Joshi, Anupam; Adalı, Tülay; Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19; Machine Learning (2020); https://arxiv.org/abs/2006.01284 | en_US |
dc.identifier.uri | http://hdl.handle.net/11603/19952 | |
dc.language.iso | en_US | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Center for Social Science Research | |
dc.rights | This 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.subject | detection of misinformation | en_US |
dc.subject | knowledge discovery | en_US |
dc.subject | independent component analysis | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | en_US | |
dc.title | Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 | en_US |
dc.type | Text | en_US |
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