Publications
With the spread of misinformation rampant online, "fake news" has become a hot topic. Since people tend to seek additional information on social media for health-related decision-making, the spread of misinformation becomes a serious issue that may lead to harmful consequences. Organizations have recently begun initiatives to fight misinformation through fact-checking online information. Despite these attempted steps, there has been few studies that have studied the actual effects of fact-checking online misinformation, particularly involving a controversial subject such as vaccines. The goal of this project is to (1) examine the effects of fact-checking misinformation on vaccines and (2) develop a fact-checking algorithm to detect vaccine misinformation online.
Methodology
We employ an online experiment to examine the effects of several fact-checking components from different organizational sources toward people's attitudes and opinions on vaccines and the fact-checking sources themselves. The second component of the project involves the development of a fact-checking vaccine misinformation algorithm.
Related Resources
Vraga, E. K., & Bode, L. (2018). I do not believe you: how providing a source corrects health misperceptions across social media platforms. Information, Communication, & Society, 21(10), 1337-1353.
Vraga, E. K., & Bode, L. (2017). Using Expert Sources to Correct Health Misinformation in Social Media. Science Communication, 39(5), 621-645.
Funding
Department of Communication
University of California, Davis
- Zhang, J., Featherstone, J. D., Calabrese, C., & Wojcieszak, M. (2021). Effects of fact-checking social media vaccine misinformation on attitudes toward vaccines.
- Ding, J. & Zhang, J. (2019). Correcting Vaccine Misinformation on Social Media Using Fact-checking Labels. Abstract accepted at the 2019 American Public Health Association Annual Meeting, Philadelphia, PA.
- Ding, J., Sun, Q., & Zhang, J. (2019). Classifying Vaccine Misinformation on Twitter and Understanding Semantic Structures of Tweets Using Machine Learning Method. Abstract accepted at the 2019 American Public Health Association Annual Meeting, Philadelphia, PA.
- Ding, J., Sun, Q., & Zhang, J. (2019). Classifying and Understanding the Semantic Structures of Vaccine Misinformation on Twitter. Abstract accepted at the 5th International Conference on Computational Social Science, Amsterdam, The Netherlands.
- Ding, J., Sun, Q., & Zhang, J. (2019). Classifying and Understanding the Semantic Structures of Vaccine Misinformation on Twitter. Abstract accepted at the XXXIX Sunbelt Social Networks Conference of the International Network for Social Network Analysis, Montréal, Québec.
With the spread of misinformation rampant online, "fake news" has become a hot topic. Since people tend to seek additional information on social media for health-related decision-making, the spread of misinformation becomes a serious issue that may lead to harmful consequences. Organizations have recently begun initiatives to fight misinformation through fact-checking online information. Despite these attempted steps, there has been few studies that have studied the actual effects of fact-checking online misinformation, particularly involving a controversial subject such as vaccines. The goal of this project is to (1) examine the effects of fact-checking misinformation on vaccines and (2) develop a fact-checking algorithm to detect vaccine misinformation online.
Methodology
We employ an online experiment to examine the effects of several fact-checking components from different organizational sources toward people's attitudes and opinions on vaccines and the fact-checking sources themselves. The second component of the project involves the development of a fact-checking vaccine misinformation algorithm.
Related Resources
Vraga, E. K., & Bode, L. (2018). I do not believe you: how providing a source corrects health misperceptions across social media platforms. Information, Communication, & Society, 21(10), 1337-1353.
Vraga, E. K., & Bode, L. (2017). Using Expert Sources to Correct Health Misinformation in Social Media. Science Communication, 39(5), 621-645.
Funding
Department of Communication
University of California, Davis