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Toward Informed News Media Consumption: Avoiding Fake News Via Labelling of Online Content

dc.contributor.authorStrong, Jay
dc.contributor.advisorSpradling, Matthew J.
dc.date.accessioned2020-05-07T14:14:42Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2020-05-07T14:14:42Z
dc.date.issued2020-04-22
dc.date.submitted2020
dc.identifier.urihttps://hdl.handle.net/2027.42/155018
dc.description.abstractSocial media has become a primary source of online news content for the vast majority of consumers in recent years. News content on social networking platforms is inexpensive, easily accessible, readily available, and rapidly disseminated, thus making it a natural preference for news content providers and consumers alike. However, these same social media platforms have also provided an opportunity for malicious users to undermine credible news sources and widely propagate misinformation to the public. This contemporary fake news phenomenon has been associated with a number of adverse social implications, including public confusion and manipulation, and election interference, among others. Fake news has been used as a means to incite hysteria and diversion, which continues to be a growing problem in the United States and across the globe. Much of the efforts to abate the spread of fake news on social media have been focused on detecting and removing the sources directly. However, much less has been done to empower individuals so that they may avoid the threat of potential harm caused by widespread misinformation on social media.en_US
dc.language.isoen_USen_US
dc.subjectsocial mediaen_US
dc.subjectcontent labelingen_US
dc.subjectfake newsen_US
dc.subjectmisinformationen_US
dc.subjectnews mediaen_US
dc.subjectsocial networksen_US
dc.subject.othercomputer scienceen_US
dc.subject.otherinformation scienceen_US
dc.subject.othermedia studiesen_US
dc.titleToward Informed News Media Consumption: Avoiding Fake News Via Labelling of Online Contenten_US
dc.typeThesisen_US
dc.description.thesisdegreenameMaster of Science (MS)en_US
dc.description.thesisdegreedisciplineComputer Scienceen_US
dc.description.thesisdegreegrantorUniversity of Michigan-Flinten_US
dc.contributor.committeememberAllison, Mark
dc.contributor.committeememberAlhosban, Amal
dc.identifier.uniqname35201446en_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/155018/1/Strong2020.pdf
dc.identifier.orcidhttps://orcid.org/0000-0002-2520-876Xen_US
dc.description.filedescriptionDescription of Strong2020.pdf : Thesis
dc.identifier.name-orcidStrong, Jay; 0000-0002-2520-876Xen_US
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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