Event Title

Monitoring and Correcting HPV Vaccine Misinformation on Social Media

Advisor(s)

Dr. Vijayakrishna (V.K.) Gadi; University of Illinois at Chicago

Dr. Ming (Bryan) Wang; University of Nebraska-Lincoln

Discipline

Computer Science

Start Date

21-4-2021 10:45 AM

End Date

21-4-2021 11:05 AM

Abstract

Vaccine misinformation is widely disseminated on social media, and it is often difficult to correct. Social media posts that contain vaccine misinformation have been found to generate high engagement among social media users, and the spread of misinformation can be harmful to society. This project focuses on monitoring and analyzing sources and types of HPV vaccine misinformation on different social media platforms. In order to collect data on misinformation on different social media platforms, and to analyze the gathered data, R was used. During the data analysis process, non-vaccine related videos were left out, and vaccine sentiment, either pro-, neutral, or anti- vaccine, was determined. Image elements in the misinformation such as whether the vaccine, health professionals, or parents were shown, were analyzed, as well as the misinformation domain of the data, focusing on concealment, distortion, ambivalence, and falsification. The topics that were focused on were conspiracy theories, vaccine inefficiency, civil liberties, and alternative medicine. The evidence provided for the misinformation was also looked into, and data was collected on the health beliefs related to the HPV vaccine.

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Apr 21st, 10:45 AM Apr 21st, 11:05 AM

Monitoring and Correcting HPV Vaccine Misinformation on Social Media

Vaccine misinformation is widely disseminated on social media, and it is often difficult to correct. Social media posts that contain vaccine misinformation have been found to generate high engagement among social media users, and the spread of misinformation can be harmful to society. This project focuses on monitoring and analyzing sources and types of HPV vaccine misinformation on different social media platforms. In order to collect data on misinformation on different social media platforms, and to analyze the gathered data, R was used. During the data analysis process, non-vaccine related videos were left out, and vaccine sentiment, either pro-, neutral, or anti- vaccine, was determined. Image elements in the misinformation such as whether the vaccine, health professionals, or parents were shown, were analyzed, as well as the misinformation domain of the data, focusing on concealment, distortion, ambivalence, and falsification. The topics that were focused on were conspiracy theories, vaccine inefficiency, civil liberties, and alternative medicine. The evidence provided for the misinformation was also looked into, and data was collected on the health beliefs related to the HPV vaccine.