Jieyu Featherstone on the Spread of Misinformation About the Influenza Vaccine
The spread of misinformation online, especially through social networks, is detrimental to the public health of the United States. During the 2017-2018 influenza season, influenza vaccination rates were low, and online vaccine misinformation may have played a role.
To better understand this role, Jieyu Ding Featherstone, a PhD student at the University of California Davis, and her colleagues, professor Jingwen Zhang and doctoral student Qiusi Sun, conducted a study1 involving an automatic learning algorithm that classified vaccine misinformation on Twitter and analyzed semantic structures of classified Tweets.
Among the findings of this study were the most central words of misinformation Tweets (flu, vaccine, not, get, and death) and non-misinformation Tweets (flu, vaccine, not, get, and health). Overall, conspiracies about government, big pharmaceutical money, and influenza vaccines causing influenza were the focus of the misinformation.
To gain more insight on these findings, Infectious Diseases Consultant reached out to Featherstone, who presented these findings at the American Public Health Association’s 2019 Annual Meeting.
ID CON: Your findings identified the central words for misinformation and non-misinformation Tweets. How can health care providers overcome these misinformation tweets and reduce vaccine hesitancy among patients?
JDF: I think health care professionals should identify people spreading misinformation and people who are affected by misinformation; from there, health care professionals would know the online information diffusion networks and target people who are affected and educate them. In the meantime, for misinformation senders, we need to inform social media companies when misinformation is being posted, because those senders may harm public health; maybe the companies can take down the posts from the website or remove contents that contain misinformation.
ID CON: What are the next steps in identifying and understanding misinformation discourses? Who has to be involved to reduce the amount of misinformation on social media?
JDF: Good question, we need bigger data sets to improve detection, and this cannot be done without efforts from computer scientists, data scientists, and researchers.
ID CON: You mentioned previously that health care professionals can potentially work with social media companies to take down harmful posts. What role do social media companies play in the spread of misinformation? In your opinion, do social media companies need to amend their posting policies to better protect the general public?
JDF: Social media companies are providing platforms to people who want to express opinions and share information. Technically, they do not actively assist the spread of misinformation, but some people may take advantage of the platform and spread misinformation or disinformation for their own political or economical purposes. It is a tough question for social media companies to decide what they want to block and remove.
I think it is not only a discussion of ethics but a question of law. Certain online behaviors, such as maliciously spreading misinformation to influence election and public health, should be categorized as cybercrimes. In that case, social media companies and government have guidelines to regulate the online environment. Relying on company policies to take down contents and posts that are harmful might not be consistent in curbing certain harmful misinformation, as each company has the right to make its own rules.
ID CON: How will the results from your study potentially impact clinical practice?
JDF: Nurses and doctors should be aware of current discourses and claims on vaccine misinformation so that they can educate and warn their patients—even warn them about accounts that are misinformation senders. That way, health care professionals can direct patients to credible sources for health information, provide patients with correct information, and answer patients’ concerns about vaccines.
Reference:
- Featherstone JD, Zhang J, Sun Q. Classifying vaccine misinformation on Twitter and understanding semantic structures of tweets using machine learning method. Paper presented at: American Public Health Association 2019 Annual Meeting; November 2-6, 2019; Philadelphia, PA. https://apha.confex.com/apha/2019/meetingapp.cgi/Paper/447885. Accessed November 18, 2019.