Show simple item record

dc.contributor.authorRizvi, Saira
dc.date.accessioned2021-10-01 15:24:37 (GMT)
dc.date.available2021-10-01 15:24:37 (GMT)
dc.date.issued2021-10-01
dc.date.submitted2021-09-15
dc.identifier.urihttp://hdl.handle.net/10012/17609
dc.description.abstractThis work introduces the task of misinformation retrieval, identifying all documents containing misinformation for a given topic, and proposes a pipeline for misinformation retrieval on tweets. As part of the work, I curated 50 COVID-19 misinformation topics used in the TREC 2020 Health Misinformation track. In addition, I annotated a test set of tweets using the TREC COVID-19 misinformation on social media. Misinformation on social media has proven highly detrimental to communities by encouraging harmful and often life-threatening behavior. The chaos caused by COVID-19 misinformation has created an urgent need for misinformation detection methods to moderate social media platforms. Drawing upon previous work in misinformation detection and the TREC 2020 Health Misinformation Track, I focused on the task of misinformation retrieval on social media. I extended the COVID-Lies data set created to detect COVID-19 misinformation in tweets by rephrasing the misconceptions accompanying each tweet. I also created 50 COVID-19 related topics for the TREC 2020 Health Misinformation track used for evaluation purposes. I propose a natural language inference (NLI) based approach using CT-BERT to identify tweets that contradict a given fact, used to score documents utilizing the model’s classification probability. The model was trained using a combination of NLI data sets to find the best approach. Tweets were labeled for the TREC 2020 Health Misinformation Track topics to create a test set on which the best model achieves an AUC of 0.81. I conducted several experiments which show that domain adaptation significantly improved the ability to detect misinformation. A combination of a large NLI corpus, such as SNLI, and an in-domain, such as the COVID-Lies, data set achieves the best performance on our test set. The pipelines retrieved and ranked tweets based on misinformation for 7 TREC topics from the COVID-19 Twitter stream. The top 20 unique tweets were analyzed using Precision@20 to evaluate the pipeline.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectData Scienceen
dc.subjectInformation Retrievalen
dc.subjectNatural Language Processingen
dc.subjectMisinformationen
dc.subject.lcshInformation retrievalen
dc.subject.lcshNatural language processing (Computer science)en
dc.subject.lcshMisinformationen
dc.titleMisinformation Retrievalen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws-etd.embargo.terms0en
uws.contributor.advisorClarke, Charles L. A.,1964-
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


UWSpace

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages