COVID-19 misinformation on Twitter: the role of deceptive support
| dc.contributor.author | Hashemi Chaleshtori, Fateme, author | |
| dc.contributor.author | Ray, Indrakshi, advisor | |
| dc.contributor.author | Anderson, Charles W., committee member | |
| dc.contributor.author | Malaiya, Yashwant K., committee member | |
| dc.contributor.author | Adams, Henry, committee member | |
| dc.date.accessioned | 2022-08-29T10:16:03Z | |
| dc.date.available | 2022-08-29T10:16:03Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Social media platforms like Twitter are a major dissemination point for information and the COVID-19 pandemic is no exception. But not all of the information comes from reliable sources, which raises doubts about their validity. In social media posts, writers reference news articles to gain credibility by leveraging the trust readers have in reputable news outlets. However, there is not always a positive correlation between the cited article and the social media posting. Targeting the Twitter platform, this study presents a novel pipeline to determine whether a Tweet is indeed supported by the news article it refers to. The approach follows two general objectives: to develop a model capable of detecting Tweets containing claims that are worthy of fact-checking and then, to assess whether the claims made in a given Tweet are supported by the news article it cites. In the event that a Tweet is found to be trustworthy, we extract its claim via a sequence labeling approach. In doing so, we seek to reduce the noise and highlight the informative parts of a Tweet. Instead of detecting erroneous and invalid information by analyzing the propagation patterns or ensuing examination of Tweets against already proven statements, this study aims to identify reliable support (or lack thereof) before misinformation spreads. Our research reveals that 14.5% of the Tweets are not factual and therefore not worth checking. An effective filter like this is especially useful when looking at a platform such as Twitter, where hundreds of thousands of posts are created every day. Further, our analysis indicates that among the Tweets which refer to a news article as evidence of a factual claim, at least 1% of those Tweets are not substantiated by the article, and therefore mislead the reader. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | HashemiChaleshtori_colostate_0053N_17318.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235604 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | natural language processing | |
| dc.subject | fake news | |
| dc.subject | social computing | |
| dc.title | COVID-19 misinformation on Twitter: the role of deceptive support | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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