Covid-19 Fake News Detection

DiPerna, Vincent; Choi, Sungjeon; Blair, Michael; Kamran, Safa

Covid-19 Fake News Detection

dc.contributor.author	DiPerna, Vincent	en
dc.contributor.author	Choi, Sungjeon	en
dc.contributor.author	Blair, Michael	en
dc.contributor.author	Kamran, Safa	en
dc.date.accessioned	2023-05-12T14:18:50Z	en
dc.date.available	2023-05-12T14:18:50Z	en
dc.date.issued	2023-04-27	en
dc.description.abstract	The Covid-19 virus is a respiratory illness that causes the isolation and retreat of people globally. People wanted updates and information in real time related to the virus such as what regions/areas are affected, to what degree are the regions affected (heavily infected, none infected, etc), how to prevent catching the virus, and cures for the virus. Social media became a popular platform for people to share information, news, and opinions about the virus. As much positive information that may be spread among social media, just as much, if not more, misinformation can be spread on social media platforms. Misinformation is harmful because it can directly affect the health of individuals who fall victim to the misinformation. For example, say a twitter user tweets medical advice about Covid-19, and people who see the tweet choose to follow the advice. Now consider the scenario where they were intentionally spreading false information, which is indeed the opposite of what you should do. The individuals who followed the twitter trolls medical advice may have their own health at risk, and anyone in their sphere of influence. Our aim is to understand the types of misinformation spread in social media, and help people identify misinformation spread on Twitter related to the subject of Covid-19. We’re going to do this by extracting relevant information such as the content of a tweet (the tweet itself) or the author of the tweet. Then, we will identify whether the tweets include true information or fabricated information. Once we do this, we are going to test and train an AI model to identify whether a tweet is spreading misinformation, or real information. After we train an AI model to identify the type of information, we will categorize the tweet into the category it was trying to spread information about. Our end goal is to integrate the preprocessing script and the AI model with a website that shows the analysis of the tweets. We want users to be able to insert a tweet into our website related to Covid-19, and the user should be returned with the relevant classification of the tweet. Also, users will be able to download a Web Archive file (WARC) of the archived tweet. Overall, we think the combination of these tasks will help aid users in identifying misinformation related to Covid-19.	en
dc.description.notes	COVID19FakeNewsDetectionReport.docx, COVID19FakeNewsDetectionReport.pdf, COVID19FakeNewsDetectionPresentation.pptx , COVID19FakeNewsDetectionPresentation.pdf	en
dc.identifier.uri	http://hdl.handle.net/10919/115025	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.subject	COVID 19	en
dc.subject	Fake News	en
dc.subject	Fake News Detection	en
dc.subject	Python	en
dc.subject	Machine Learning	en
dc.subject	TWARC	en
dc.subject	MySQL	en
dc.subject	Data Processing	en
dc.subject	Text Classifier	en
dc.subject	Tweets	en
dc.subject	Twitter	en
dc.title	Covid-19 Fake News Detection	en
dc.type	Presentation	en
dc.type	Report	en