Rumors are everywhere during the COVID-19 pandemic and it is increasingly difficult to separate rumors from facts. Those first confronted by the enormous challenge of fighting this infodemic to save their lives were the people of Hubei Province in China. To understand how they defined and processed rumors, we conducted an interview study with Hubei residents when they were under lockdown.
Individuals typically defined rumors in terms of one or two of three features: non-factual information, information unsanctioned by the government, and information causing panic.
Definitions of rumors.
They reported low motivation in verifying the information and often either rejected any information they perceived as suspicious or waited for the government to debunk rumors. Even among those who tried to verify information, most relied exclusively on heuristic processing cues such as source credibility, linguistic and visual cues, and intuition. Systematic processing strategies such as fact-checking and discussing with family and friends were seldom used.
Strategies to identify rumors
Zou, W., & Tang, L. (2021). Rumors and processing strategies during the COVID-19 outbreak in China. Public Understanding of Science. 10.1177/0963662520979459. Free full text online
These days, public health agencies such as the CDC are increasingly using social media to communicate directly with the public. We conducted a study to examine how the CDC and state health departments were doing based on their contents in 2017. This study was guided by the Extended Parallel Processing Model. This study was led by Dr. Cui Meadows at East Carolina University.
The Facebook messages of both the CDC and SHDs cover a wide range of topics. This includes infectious disease outbreaks and general health issues and concerns, such as antibiotic use, cancer, cardiovascular disease, dental health, domestic violence, healthy eating, infant/child health, salmonella, smoking, travel safety, and immunization.
Overall, the CDC’s messages are better crafted than those of state health departments in terms of including components that have been shown to be conducive to health behaviors, such as information about severity, susceptibility, self-efficacy and response efficacy.
The federal and state health agencies rarely communicate self-efficacy related information on Facebook. This is problematic because efficacy information is critical to promoting healthy outcomes because high efficacy can help boost self-confidence and enhance the belief that the recommended action will work.
Meadows, C., Meadows, C. Z., Tang., L., (2020). The CDC and state health department Facebook messages: An examination of frames and the Extended Parallel Processing Model. Communication Studies 71(5): 740-752. doi: 10.1080/10510974.2020.1819839 [Full text]
Laypersons’ beliefs about mental illnesses influence their experience of mental illnesses and their likelihood of seeking help. In relation to PPD, such beliefs include culture-specific beliefs held by those without medical expertise about the nature of PPD, causes, and appropriate coping strategies. At the center of such a conceptual model are etiological beliefs, i.e. the attributions people make about PPD.
In the same study I described earlier, we explored how Chinese mothers make attributions of PPD by asking them to tell stories (stories of their own experience of PPD and stories of other women’s experiences with PPD). We found:
Chinese mothers often take a situational approach to PPD, attributing it to external factors that could be changed (e.g., lack of support from in-laws) or to internal factors that will disappear over time (e.g., hormonal fluctuation).
As a result, these mothers often choose to cope with their PPD symptoms through self-help strategies such as talking to someone, enlisting help, and positive thinking. Seeking professional help is not a preferred coping strategy.
Furthermore, participants often make different attributions about the PPD experiences of themselves and the experiences of others. In explaining other women’s PPD symptoms, they are more likely to evoke the myths about gender and motherhood in the Chinese culture and blame PPD on these mothers’ personality flaws, husbands’ affairs, and financial hardships.
Tang, L., Zhang, X., & Zhu, R. (2020). What causes postpartum depression and how to cope with it: A phenomenological study of mothers in China. Health Communication. Doi: 10.1080/10410236.2020.1771063. Published online first. [Full article]
Around 8–13% of new mothers suffer from postpartum depression (PPD) worldwide. In the Greater China Region, this figure rises to around 20%. How does the Chinese culture (especially in terms of gender relationship and family dynamics) influence Chinese women understand, experience, and deal with PPD?
We conducted interviews with 38 mothers with small children in China to find out. Some interesting findings are:
New mothers want instrumental supports from their own mothers and mothers-in-law; informational support from their friends, physicians, and the Internet; and emotional support from their husbands. Anger and stress occur when such expectations are not met. Such expectations are rooted in China’ traditional definition of families and gender roles.
Traditional Chinese families are extended families where three or even four generations live together. Consequently, having a child is not only the choice of a couple but also their duty to their parents, and raising a child, likewise, is the responsibility of the grandparents as much as that of the parents.
China’s traditional gender roles dictate that women are responsible for household chores while men are responsible for working outside the home. Hence, childcare is considered the job of women: mothers, grandmothers, and even aunts.
Finally, culture affects how family members in China communicate with each other regarding social support. Different from western cultures in which individuals are prioritized, Chinese culture prioritizes group goals over individual needs. Consequently, Chinese people are less likely to enlist the social support they need to avoid disrupting family harmony.
Tang, L., Zhu, R., & Zhang, X. (2016). Postpartum depression and social support in China: A cultural perspective. Journal of Health Communication, 21(9), 1055-1061. doi: 10.1080/10810730.2016.1204384. [Full article]
Do you remember the 2015 Measles outbreak originating in Disneyland in California? This outbreak was one of the biggest outbreaks of emerging infectious diseases in the United States before the COVID-19 pandemic.
In one study, we examined the semantic networks of Twitter contents about measles based on the corpus of 1 million tweets.
Semantic networks represent the semantic relationships among a set of words. In a semantic network, word-use frequencies and co-occurrence of the most frequently occurring words represent shared meanings and common perceptions. For instance, the cluster of purple words in the lower-left corner of the network represents the political frame, where people talk about the causes and solutions of the outbreak in political terms. For instance, whether measles was brought to the US by immigrant? What kind of role the government should play in preventing such outbreaks?
We identified four major frames: news update frame, public health frame, vaccine frame, and political frame.
We also mapped the longitudinal changes of the frames during different stages of the outbreak.
The news update frame appeared to be the most dominant frame during the initial and resolution stages.
The public health frame was 1 of the 2 most dominant frames in the pre- crisis stage; however, its use decreased during the initial stage and was lowest during the maintenance stage.
The use of the vaccine frame increased from pre-crisis stage to the initial stage and the vaccine frame became the most dominant frame during the maintenance.
The political frame was the least often used frame in all four stages of the outbreak and appeared most frequently during the maintenance stage.
Tang, L., Bie, B., Zhi, D. (2018). Tweeting about measles during an outbreak: A semantic network approach to the framing of emerging infectious diseases. American Journal of Infection Control, 46(12), 1375-1380. doi: 10.1016/j.ajic.2018.05.019
The SARS-COV-2 virus or novel coronavirus was first detected in December of 2019 in Wuhan, Hubei Province, China. Even though doctors suspected that it was a highly dangerous virus similar to SARS, the Chinese government denied the existence of human-to-human transmission until January 20th. On January 23rd, Wuhan was suddenly put under a complete lockdown. The rest of Hubei Province was also put under lockdown soon afterward.
How did residents in Hubei Province acquire and share information during its COVID-19 outbreak? As China was the first country to experience the disease, understanding of the virus and the outbreak was extremely limited during the initial stage of the outbreak. Methods of prevention and treatment were almost completely unknown. The information control measures carried out in China made it even more difficult for the public to understand the situation.
We conducted interviews with residents of Hubei Province while they were under lockdown between late January and the end of February 2020. Our study reached several interesting findings.
While we expected that people would actively search for COVID-19 related information, our study suggested that individuals primarily acquire information passively, relying on TV news, news subscription services, and verified social media accounts of governments, and state media.
People trust governmental sources, despite the initial problems of cover-up.
People prefer to share information with family members through private channels than with one’s extended social networks and the general public through public channels mostly due to concerns with censorship.
This study has been recently published in the COVID-19 special issue of Health Communication.
Tang, L. & Zou, W. (2020). Health information consumption under COVID-19 lockdown: An interview study of residents of Hubei Province, China. Health Communication, 36(1): 74-80. doi: 10.1080/10410236.2020.1847447.
Have you wondered how you stumbled upon that anti-vaccine video on YouTube? How come one innocent click of the video might or might not lead you to an avalanche of more videos that not only tell you that vaccine causes autism but also try to sell you vitamins and magical herbs?
Recently, I completed a study that examined how user behavior and YouTube’s ranking and recommendation algorithms influence the exposure to different kinds of vaccine information.
We explored the different patterns of exposure on YouTube when a person starts with a keyword-based search on the YouTube platform (goal-oriented browsing) and when a person starts with an anti-vaccine video on another website such as Facebook (direct navigation). We simulated the patterns of exposure by creating four networks of videos based on YouTube recommendations.
First, we created two search networks, one from a set of pro-vaccine keywords and the other from a set of anti-vaccine keywords. For each network, we collected the first six videos, and for each of these six videos, we further collected six recommended videos, and another six videos for this second layer of videos. Videos collected based on pro-vaccine keywords and anti-vaccine keywords are then put into separate networks. The same procedure was used to create two additional networks starting from two sets of anti-vaccine seed videos. (In the four networks below, green dots represent pro-vaccine videos, red dots represent anti-vaccine videos, and gray dots represent non-vaccine videos.)
What we found was that viewers are more likely to encounter anti-vaccine videos through direct navigation starting from an anti-vaccine seed video (the two networks on the bottom) than through goal-oriented browsing (the two networks on the top).
Why is it the case?
I think it is because YouTube is intentionally suppressing the rankings of anti-vaccine videos in its search research. So you are unlikely to find anti-vaccine video even if you start with some anti-vaccine keywords. However, when you start with an anti-vaccine seed video that you encounter from another platform, such as Facebook, reddit, or your crazy uncle’s email, the protection mechanism YouTube doesn’t work anymore. When you click one anti-vaccine video, YouTube’s recommendation algorithm will show more and more anti-vaccine videos. This is also called the “filter bubble,”
Tang, L., Fujimoto, K., Amith, M., Cunningham, R., Costantini, R.A., York, F., Xiang, G., Boom, J., & Tao, C. (2020). Going down the rabbit hole? An exploration of network exposure to vaccine misinformation on YouTube. Journal of Medical Internet Research. doi: 10.2196/23262
Dr. Lu Tang, Health Communication Researcher 